diff --git a/.conda/bld.bat b/.conda/bld.bat index 542b82616..22b63e50a 100644 --- a/.conda/bld.bat +++ b/.conda/bld.bat @@ -7,7 +7,7 @@ set PIP_IGNORE_INSTALLED=False @REM Install the pip dependencies. Note: Using urls to wheels might be better: @REM https://docs.conda.io/projects/conda-build/en/stable/user-guide/wheel-files.html) -pip install -r .\requirements.txt +pip install --no-cache-dir -r .\requirements.txt @REM Install sleap itself. This does not install the requirements, but will list which @REM requirements are missing (see "install_requires") when user attempts to install. diff --git a/.conda/build.sh b/.conda/build.sh index 85bbe442f..620cd127a 100644 --- a/.conda/build.sh +++ b/.conda/build.sh @@ -7,7 +7,7 @@ export PIP_IGNORE_INSTALLED=False # Install the pip dependencies. Note: Using urls to wheels might be better: # https://docs.conda.io/projects/conda-build/en/stable/user-guide/wheel-files.html) -pip install -r ./requirements.txt +pip install --no-cache-dir -r ./requirements.txt # Install sleap itself. This does not install the requirements, but will list which diff --git a/.conda/meta.yaml b/.conda/meta.yaml index c80d3b56f..caffe9fcb 100644 --- a/.conda/meta.yaml +++ b/.conda/meta.yaml @@ -16,7 +16,7 @@ source: path: ../ build: - number: 9 + number: 1 requirements: host: @@ -83,7 +83,7 @@ requirements: - conda-forge::scikit-video - conda-forge::seaborn - sleap::tensorflow >=2.6.3,<2.11 # No windows GPU support for >2.10, sleap channel has 2.6.3 - - conda-forge::tensorflow-hub + - conda-forge::tensorflow-hub <0.14.0 # Causes pynwb conflicts on linux GH-1446 test: imports: diff --git a/.conda_mac/build.sh b/.conda_mac/build.sh index f1299991b..2036035f6 100644 --- a/.conda_mac/build.sh +++ b/.conda_mac/build.sh @@ -7,6 +7,6 @@ export PIP_NO_INDEX=False export PIP_NO_DEPENDENCIES=False export PIP_IGNORE_INSTALLED=False -pip install -r requirements.txt +pip install --no-cache-dir -r requirements.txt python setup.py install --single-version-externally-managed --record=record.txt \ No newline at end of file diff --git a/.conda_mac/meta.yaml b/.conda_mac/meta.yaml index db2f23215..7496f2057 100644 --- a/.conda_mac/meta.yaml +++ b/.conda_mac/meta.yaml @@ -16,7 +16,7 @@ about: summary: {{ data.get('description') }} build: - number: 5 + number: 1 source: path: ../ diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 91680b64c..24c20c513 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -28,7 +28,7 @@ Please include information about how you installed. - OS: - Version(s): - + - SLEAP installation method (listed [here](https://sleap.ai/installation.html#)): - [ ] [Conda from package](https://sleap.ai/installation.html#conda-package) - [ ] [Conda from source](https://sleap.ai/installation.html#conda-from-source) diff --git a/.github/workflows/build_ci.yml b/.github/workflows/build_ci.yml new file mode 100644 index 000000000..baf046295 --- /dev/null +++ b/.github/workflows/build_ci.yml @@ -0,0 +1,155 @@ +# Run tests using built conda packages and wheels. +name: Build CI (no upload) + +# Run when changes to pip wheel +on: + push: + paths: + - 'setup.py' + - 'requirements.txt' + - 'dev_requirements.txt' + - 'jupyter_requirements.txt' + - 'pypi_requirements.txt' + - 'environment_build.yml' + - '.github/workflows/build_ci.yml' + +jobs: + build: + name: Build wheel (${{ matrix.os }}) + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: ["ubuntu-22.04"] + # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstrategymatrixinclude + include: + # Use this condarc as default + - condarc: .conda/condarc.yaml + - wheel_name: sleap-wheel-linux + steps: + # Setup + - uses: actions/checkout@v2 + + - name: Cache conda + uses: actions/cache@v1 + env: + # Increase this value to reset cache if environment_build.yml has not changed + CACHE_NUMBER: 0 + with: + path: ~/conda_pkgs_dir + key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('environment_build.yml', 'pyproject.toml') }} + + - name: Setup Miniconda for Build + # https://github.com/conda-incubator/setup-miniconda + uses: conda-incubator/setup-miniconda@v2.0.1 + with: + python-version: 3.7 + use-only-tar-bz2: true # IMPORTANT: This needs to be set for caching to work properly! + environment-file: environment_build.yml + condarc-file: ${{ matrix.condarc }} + activate-environment: sleap_ci + + - name: Print build environment info + shell: bash -l {0} + run: | + which python + conda list + pip freeze + + # Build pip wheel + - name: Build pip wheel + shell: bash -l {0} + run: | + python setup.py bdist_wheel + + # Upload artifact "tests" can use it + - name: Upload wheel artifact + uses: actions/upload-artifact@v3 + with: + name: ${{ matrix.wheel_name }} + path: dist/*.whl + retention-days: 1 + + tests: + name: Run tests using wheel (${{ matrix.os }}) + runs-on: ${{ matrix.os }} + needs: build # Ensure the build job has completed before starting this job. + strategy: + fail-fast: false + matrix: + os: ["ubuntu-22.04", "windows-2022", "macos-latest"] + # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstrategymatrixinclude + include: + # Default values + - wheel_name: sleap-wheel-linux + - venv_cmd: source venv/bin/activate + - pip_cmd: | + wheel_path=$(find dist -name "*.whl") + echo $wheel_path + pip install '$wheel_path'[dev] + - test_args: pytest --durations=-1 tests/ + - condarc: .conda/condarc.yaml + # Use special condarc if macos + - os: "macos-latest" + condarc: .conda_mac/condarc.yaml + # Ubuntu specific values + - os: ubuntu-22.04 + # Otherwise core dumped in github actions + test_args: | + sudo apt install xvfb libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 + sudo Xvfb :1 -screen 0 1024x768x24 - diff --git a/.github/workflows/website.yml b/.github/workflows/website.yml index 97d221c47..7db6b4d74 100644 --- a/.github/workflows/website.yml +++ b/.github/workflows/website.yml @@ -8,7 +8,7 @@ on: # 'main' triggers updates to 'sleap.ai', all others to 'sleap.ai/develop' - main - develop - - liezl/update_installation_docs + - liezl/add-pip-extras paths: - "docs/**" - "README.rst" diff --git a/README.rst b/README.rst index 446d01ed2..dbc5a7cac 100644 --- a/README.rst +++ b/README.rst @@ -75,7 +75,7 @@ Quick install .. code-block:: bash - pip install sleap + pip install sleap[pypi] See the docs for `full installation instructions `_. diff --git a/dev_requirements.txt b/dev_requirements.txt index e96944730..f7bb23643 100644 --- a/dev_requirements.txt +++ b/dev_requirements.txt @@ -18,7 +18,5 @@ black==21.6b0 pre-commit twine==3.3.0 PyGithub -jupyterlab jedi==0.17.2 -ipykernel click==8.0.4 \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index bc73ae0d7..b1e79fcc3 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -15,10 +15,10 @@ import os import sys import shutil -import docs.utils from datetime import date sys.path.insert(0, os.path.abspath("..")) +import docs.utils # -- Project information ----------------------------------------------------- @@ -28,7 +28,7 @@ copyright = f"2019–{date.today().year}, Talmo Lab" # The short X.Y version -version = "1.3.1" +version = "1.3.2" # Get the sleap version # with open("../sleap/version.py") as f: @@ -36,7 +36,7 @@ # version = re.search("\d.+(?=['\"])", version_file).group(0) # Release should be the full branch name -release = "v1.3.1" +release = "v1.3.2" html_title = f"SLEAP ({release})" html_short_title = "SLEAP" diff --git a/docs/guides/cli.md b/docs/guides/cli.md index 0c08e9b17..35ea52171 100644 --- a/docs/guides/cli.md +++ b/docs/guides/cli.md @@ -118,158 +118,166 @@ optional arguments: If you specify how many identities there should be in a frame (i.e., the number of animals) with the {code}`--tracking.clean_instance_count` argument, then we will use a heuristic method to connect "breaks" in the track identities where we lose one identity and spawn another. This can be used as part of the inference pipeline (if models are specified), as part of the tracking-only pipeline (if the predictions file is specified and no models are specified), or by itself on predictions with pre-tracked identities (if you specify {code}`--tracking.tracker none`). See {ref}`proofreading` for more details on tracking. ```none -usage: sleap-track [-h] [-m MODELS] [--frames FRAMES] [--only-labeled-frames] - [--only-suggested-frames] [-o OUTPUT] [--no-empty-frames] - [--verbosity {none,rich,json}] - [--video.dataset VIDEO.DATASET] - [--video.input_format VIDEO.INPUT_FORMAT] - [--video.index VIDEO.INDEX] - [--cpu | --first-gpu | --last-gpu | --gpu GPU] - [--peak_threshold PEAK_THRESHOLD] [--batch_size BATCH_SIZE] - [--open-in-gui] [--tracking.tracker TRACKING.TRACKER] - [--tracking.target_instance_count TRACKING.TARGET_INSTANCE_COUNT] - [--tracking.pre_cull_to_target TRACKING.PRE_CULL_TO_TARGET] - [--tracking.pre_cull_iou_threshold TRACKING.PRE_CULL_IOU_THRESHOLD] +usage: sleap-track [-h] [-m MODELS] [--frames FRAMES] [--only-labeled-frames] [--only-suggested-frames] [-o OUTPUT] [--no-empty-frames] + [--verbosity {none,rich,json}] [--video.dataset VIDEO.DATASET] [--video.input_format VIDEO.INPUT_FORMAT] + [--video.index VIDEO.INDEX] [--cpu | --first-gpu | --last-gpu | --gpu GPU] [--max_edge_length_ratio MAX_EDGE_LENGTH_RATIO] + [--dist_penalty_weight DIST_PENALTY_WEIGHT] [--batch_size BATCH_SIZE] [--open-in-gui] [--peak_threshold PEAK_THRESHOLD] + [-n MAX_INSTANCES] [--tracking.tracker TRACKING.TRACKER] [--tracking.max_tracking TRACKING.MAX_TRACKING] + [--tracking.max_tracks TRACKING.MAX_TRACKS] [--tracking.target_instance_count TRACKING.TARGET_INSTANCE_COUNT] + [--tracking.pre_cull_to_target TRACKING.PRE_CULL_TO_TARGET] [--tracking.pre_cull_iou_threshold TRACKING.PRE_CULL_IOU_THRESHOLD] [--tracking.post_connect_single_breaks TRACKING.POST_CONNECT_SINGLE_BREAKS] - [--tracking.clean_instance_count TRACKING.CLEAN_INSTANCE_COUNT] - [--tracking.clean_iou_threshold TRACKING.CLEAN_IOU_THRESHOLD] - [--tracking.similarity TRACKING.SIMILARITY] - [--tracking.match TRACKING.MATCH] - [--tracking.track_window TRACKING.TRACK_WINDOW] - [--tracking.save_shifted_instances TRACKING.SAVE_SHIFTED_INSTANCES] - [--tracking.min_new_track_points TRACKING.MIN_NEW_TRACK_POINTS] - [--tracking.min_match_points TRACKING.MIN_MATCH_POINTS] - [--tracking.img_scale TRACKING.IMG_SCALE] - [--tracking.of_window_size TRACKING.OF_WINDOW_SIZE] - [--tracking.of_max_levels TRACKING.OF_MAX_LEVELS] - [--tracking.kf_node_indices TRACKING.KF_NODE_INDICES] + [--tracking.clean_instance_count TRACKING.CLEAN_INSTANCE_COUNT] [--tracking.clean_iou_threshold TRACKING.CLEAN_IOU_THRESHOLD] + [--tracking.similarity TRACKING.SIMILARITY] [--tracking.match TRACKING.MATCH] [--tracking.robust TRACKING.ROBUST] + [--tracking.track_window TRACKING.TRACK_WINDOW] [--tracking.min_new_track_points TRACKING.MIN_NEW_TRACK_POINTS] + [--tracking.min_match_points TRACKING.MIN_MATCH_POINTS] [--tracking.img_scale TRACKING.IMG_SCALE] + [--tracking.of_window_size TRACKING.OF_WINDOW_SIZE] [--tracking.of_max_levels TRACKING.OF_MAX_LEVELS] + [--tracking.save_shifted_instances TRACKING.SAVE_SHIFTED_INSTANCES] [--tracking.kf_node_indices TRACKING.KF_NODE_INDICES] [--tracking.kf_init_frame_count TRACKING.KF_INIT_FRAME_COUNT] [data_path] positional arguments: - data_path Path to data to predict on. This can be a labels - (.slp) file or any supported video format. + data_path Path to data to predict on. This can be a labels (.slp) file or any supported video format. optional arguments: -h, --help show this help message and exit -m MODELS, --model MODELS - Path to trained model directory (with - training_config.json). Multiple models can be - specified, each preceded by --model. - --frames FRAMES List of frames to predict when running on a video. Can - be specified as a comma separated list (e.g. 1,2,3) or - a range separated by hyphen (e.g., 1-3, for 1,2,3). If - not provided, defaults to predicting on the entire - video. + Path to trained model directory (with training_config.json). Multiple models can be specified, each preceded by --model. + --frames FRAMES List of frames to predict when running on a video. Can be specified as a comma separated list (e.g. 1,2,3) or a range + separated by hyphen (e.g., 1-3, for 1,2,3). If not provided, defaults to predicting on the entire video. --only-labeled-frames - Only run inference on user labeled frames when running - on labels dataset. This is useful for generating - predictions to compare against ground truth. + Only run inference on user labeled frames when running on labels dataset. This is useful for generating predictions to compare + against ground truth. --only-suggested-frames - Only run inference on unlabeled suggested frames when - running on labels dataset. This is useful for - generating predictions for initialization during - labeling. + Only run inference on unlabeled suggested frames when running on labels dataset. This is useful for generating predictions for + initialization during labeling. -o OUTPUT, --output OUTPUT - The output filename to use for the predicted data. If - not provided, defaults to - '[data_path].predictions.slp' if generating predictions or - '[data_path].[tracker].[similarity method].[matching method].slp' - if retracking predictions. - --no-empty-frames Clear any empty frames that did not have any detected - instances before saving to output. - -n, --max_instances MAX_INSTANCES - Limit maximum number of instances in multi-instance models. - Not available for ID models. Defaults to None. + The output filename to use for the predicted data. If not provided, defaults to '[data_path].predictions.slp'. + --no-empty-frames Clear any empty frames that did not have any detected instances before saving to output. --verbosity {none,rich,json} - Verbosity of inference progress reporting. 'none' does - not output anything during inference, 'rich' displays - an updating progress bar, and 'json' outputs the - progress as a JSON encoded response to the console. + Verbosity of inference progress reporting. 'none' does not output anything during inference, 'rich' displays an updating + progress bar, and 'json' outputs the progress as a JSON encoded response to the console. --video.dataset VIDEO.DATASET The dataset for HDF5 videos. --video.input_format VIDEO.INPUT_FORMAT The input_format for HDF5 videos. --video.index VIDEO.INDEX - The index of the video to run inference on. Only used if - data_path points to a labels file. - --cpu Run inference only on CPU. If not specified, will use - available GPU. + Integer index of video in .slp file to predict on. To be used with an .slp path as an alternative to specifying the video + path. + --cpu Run inference only on CPU. If not specified, will use available GPU. --first-gpu Run inference on the first GPU, if available. --last-gpu Run inference on the last GPU, if available. - --gpu GPU Run training on the i-th GPU on the system. If 'auto', run on - the GPU with the highest percentage of available memory. + --gpu GPU Run training on the i-th GPU on the system. If 'auto', run on the GPU with the highest percentage of available memory. --max_edge_length_ratio MAX_EDGE_LENGTH_RATIO - The maximum expected length of a connected pair of points as a - fraction of the image size. Candidate connections longer than - this length will be penalized during matching. Only applies to - bottom-up (PAF) models. + The maximum expected length of a connected pair of points as a fraction of the image size. Candidate connections longer than + this length will be penalized during matching. Only applies to bottom-up (PAF) models. --dist_penalty_weight DIST_PENALTY_WEIGHT - A coefficient to scale weight of the distance penalty. Set to - values greater than 1.0 to enforce the distance penalty more + A coefficient to scale weight of the distance penalty. Set to values greater than 1.0 to enforce the distance penalty more strictly. Only applies to bottom-up (PAF) models. - --peak_threshold PEAK_THRESHOLD - Minimum confidence map value to consider a peak as - valid. --batch_size BATCH_SIZE - Number of frames to predict at a time. Larger values - result in faster inference speeds, but require more - memory. - --open-in-gui Open the resulting predictions in the GUI when - finished. + Number of frames to predict at a time. Larger values result in faster inference speeds, but require more memory. + --open-in-gui Open the resulting predictions in the GUI when finished. + --peak_threshold PEAK_THRESHOLD + Minimum confidence map value to consider a peak as valid. + -n MAX_INSTANCES, --max_instances MAX_INSTANCES + Limit maximum number of instances in multi-instance models. Not available for ID models. Defaults to None. --tracking.tracker TRACKING.TRACKER - Options: simple, flow, None (default: None) + Options: simple, flow, simplemaxtracks, flowmaxtracks, None (default: None) + --tracking.max_tracking TRACKING.MAX_TRACKING + If true then the tracker will cap the max number of tracks. (default: False) + --tracking.max_tracks TRACKING.MAX_TRACKS + Maximum number of tracks to be tracked by the tracker. (default: None) --tracking.target_instance_count TRACKING.TARGET_INSTANCE_COUNT - Target number of instances to track per frame. - (default: 0) + Target number of instances to track per frame. (default: 0) --tracking.pre_cull_to_target TRACKING.PRE_CULL_TO_TARGET - If non-zero and target_instance_count is also non- - zero, then cull instances over target count per frame - *before* tracking. (default: 0) + If non-zero and target_instance_count is also non-zero, then cull instances over target count per frame *before* tracking. + (default: 0) --tracking.pre_cull_iou_threshold TRACKING.PRE_CULL_IOU_THRESHOLD - If non-zero and pre_cull_to_target also set, then use - IOU threshold to remove overlapping instances over - count *before* tracking. (default: 0) + If non-zero and pre_cull_to_target also set, then use IOU threshold to remove overlapping instances over count *before* + tracking. (default: 0) --tracking.post_connect_single_breaks TRACKING.POST_CONNECT_SINGLE_BREAKS - If non-zero and target_instance_count is also non- - zero, then connect track breaks when exactly one track - is lost and exactly one track is spawned in frame. - (default: 0) + If non-zero and target_instance_count is also non-zero, then connect track breaks when exactly one track is lost and exactly + one track is spawned in frame. (default: 0) --tracking.clean_instance_count TRACKING.CLEAN_INSTANCE_COUNT - Target number of instances to clean *after* tracking. - (default: 0) + Target number of instances to clean *after* tracking. (default: 0) --tracking.clean_iou_threshold TRACKING.CLEAN_IOU_THRESHOLD - IOU to use when culling instances *after* tracking. - (default: 0) + IOU to use when culling instances *after* tracking. (default: 0) --tracking.similarity TRACKING.SIMILARITY Options: instance, centroid, iou (default: instance) --tracking.match TRACKING.MATCH Options: hungarian, greedy (default: greedy) + --tracking.robust TRACKING.ROBUST + Robust quantile of similarity score for instance matching. If equal to 1, keep the max similarity score (non-robust). + (default: 1) --tracking.track_window TRACKING.TRACK_WINDOW How many frames back to look for matches (default: 5) - --tracking.save_shifted_instances TRACKING.SAVE_SHIFTED_INSTANCES - For optical-flow: Save the shifted instances between - elapsed frames for optimal comparison (default: 0) --tracking.min_new_track_points TRACKING.MIN_NEW_TRACK_POINTS - Minimum number of instance points for spawning new - track (default: 0) + Minimum number of instance points for spawning new track (default: 0) --tracking.min_match_points TRACKING.MIN_MATCH_POINTS Minimum points for match candidates (default: 0) --tracking.img_scale TRACKING.IMG_SCALE For optical-flow: Image scale (default: 1.0) --tracking.of_window_size TRACKING.OF_WINDOW_SIZE - For optical-flow: Optical flow window size to consider - at each pyramid (default: 21) + For optical-flow: Optical flow window size to consider at each pyramid (default: 21) --tracking.of_max_levels TRACKING.OF_MAX_LEVELS - For optical-flow: Number of pyramid scale levels to - consider (default: 3) + For optical-flow: Number of pyramid scale levels to consider (default: 3) + --tracking.save_shifted_instances TRACKING.SAVE_SHIFTED_INSTANCES + If non-zero and tracking.tracker is set to flow, save the shifted instances between elapsed frames (default: 0) --tracking.kf_node_indices TRACKING.KF_NODE_INDICES - For Kalman filter: Indices of nodes to track. - (default: ) + For Kalman filter: Indices of nodes to track. (default: ) --tracking.kf_init_frame_count TRACKING.KF_INIT_FRAME_COUNT - For Kalman filter: Number of frames to track with - other tracker. 0 means no Kalman filters will be used. - (default: 0) + For Kalman filter: Number of frames to track with other tracker. 0 means no Kalman filters will be used. (default: 0) +``` + +#### Examples: + +**1. Simple inference without tracking:** + +```none +sleap-track -m "models/my_model" -o "output_predictions.slp" "input_video.mp4" +``` + +**2. Inference with multi-model pipelines (e.g., top-down):** + +```none +sleap-track -m "models/centroid" -m "models/centered_instance" -o "output_predictions.slp" "input_video.mp4" +``` + +**3. Inference on suggested frames of a labeling project:** + +```none +sleap-track -m "models/my_model" --only-suggested-frames -o "labels_with_predictions.slp" "labels.v005.slp" +``` + +The resulting `labels_with_predictions.slp` can then merged into the base labels project from the SLEAP GUI via **File** --> **Merge into project...**. + +**4. Inference with simple tracking:** + +```none +sleap-track -m "models/my_model" --tracking.tracker simple -o "output_predictions.slp" "input_video.mp4" +``` + +**5. Inference with max tracks limit:** + +```none +sleap-track -m "models/my_model" --tracking.tracker simplemaxtracks --tracking.max_tracking 1 --tracking.max_tracks 4 -o "output_predictions.slp" "input_video.mp4" +``` + +**6. Re-tracking without pose inference:** + +```none +sleap-track --tracking.tracker simplemaxtracks --tracking.max_tracking 1 --tracking.max_tracks 4 -o "retracked.slp" "input_predictions.slp" +``` + +**7. Select GPU for pose inference:** + +```none +sleap-track --gpu 1 ... +``` + +**8. Select subset of frames to predict on:** + +```none +sleap-track -m "models/my_model" --frames 1000-2000 "input_video.mp4" ``` ## Dataset files diff --git a/docs/installation.md b/docs/installation.md index 6918597e8..7c2a7d710 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -28,7 +28,7 @@ On Windows, our personal preference is to use alternative terminal apps like [Cm (apple-silicon)= -### Macs (Pre-Installation) +### Macs Pre-M1 (Pre-Installation) SLEAP can be installed on Macs by following these instructions: @@ -106,7 +106,7 @@ wget -nc https://github.com/conda-forge/miniforge/releases/latest/download/Mamba **On Macs (Apple Silicon)**, use this terminal command: ```bash -wget -nc https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-MacOSX-arm64.sh && bash Mambaforge-MacOSX-arm64.sh -b && ~/mambaforge/bin/conda init zsh +curl -fsSL --compressed https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-MacOSX-arm64.sh -o Mambaforge3-MacOSX-arm64.sh && chmod +x Mambaforge3-MacOSX-arm64.sh && ./Mambaforge3-MacOSX-arm64.sh -b -p ~/mambaforge3 && rm Mambaforge3-MacOSX-arm64.sh && ~/mambaforge3/bin/conda init "$(basename "${SHELL}")" && source "$HOME/.$(basename "${SHELL}")rc" ``` ## Installation methods @@ -120,13 +120,13 @@ SLEAP can be installed three different ways: via {ref}`conda package` to create a new environment where we can isolate the `pip install`. If you are working on **Google Colab**, skip to step 3 to perform the `pip install` without using a conda environment. +Although you do not need Mambaforge installed to perform a `pip install`, we recommend {ref}`installing Mambaforge` to create a new environment where we can isolate the `pip install`. Alternatively, you can use a venv if you have an existing python installation. If you are working on **Google Colab**, skip to step 3 to perform the `pip install` without using a conda environment. 1. Otherwise, create a new conda environment where we will `pip install sleap`: @@ -215,11 +215,20 @@ Although you do not need Mambaforge installed to perform a `pip install`, we rec 3. Finally, we can perform the `pip install`: ```bash - pip install sleap==1.3.1 + pip install sleap[pypi]==1.3.1 ``` This works on **any OS except Apple silicon** and on **Google Colab**. + ```{note} + The pypi distributed package of SLEAP ships with the following extras: + - **pypi**: For installation without an mamba environment file. All dependencies come from PyPI. + - **jupyter**: This installs all *pypi* and jupyter lab dependencies. + - **dev**: This installs all *jupyter* dependencies and developement tools for testing and building docs. + - **conda_jupyter**: For installation using a mamba environment file included in the source code. Most dependencies are listed as conda packages in the environment file and only a few come from PyPI to allow jupyter lab support. + - **conda_dev**: For installation using [a mamba environment](https://github.com/search?q=repo%3Atalmolab%2Fsleap+path%3Aenvironment*.yml&type=code) with a few PyPI dependencies for development tools. + ``` + ```{note} - Requires Python 3.7 - To enable GPU support, make sure that you have **CUDA Toolkit v11.3** and **cuDNN v8.2** installed. diff --git a/docs/make_api_doctree.py b/docs/make_api_doctree.py index a507070d7..68de7ba95 100644 --- a/docs/make_api_doctree.py +++ b/docs/make_api_doctree.py @@ -10,6 +10,7 @@ "sleap.version", ] + def make_api_doctree(): doctree = "" @@ -42,4 +43,4 @@ def make_api_doctree(): if __name__ == "__main__": - make_api_doctree() \ No newline at end of file + make_api_doctree() diff --git a/docs/notebooks/Data_structures.ipynb b/docs/notebooks/Data_structures.ipynb index 7eb9a552c..1ad1e6abb 100644 --- a/docs/notebooks/Data_structures.ipynb +++ b/docs/notebooks/Data_structures.ipynb @@ -1,21 +1,4 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "SLEAP - Data structures.ipynb", - "provenance": [], - "collapsed_sections": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, "cells": [ { "cell_type": "markdown", @@ -29,6 +12,9 @@ }, { "cell_type": "markdown", + "metadata": { + "id": "NqgGonrTRLg9" + }, "source": [ "# Data structures\n", "\n", @@ -41,10 +27,7 @@ "- `Skeleton` → Defines the nodes and edges that define the set of unique landmark types that each point represents, e.g., \"head\", \"tail\", etc. This *does not contain positions* -- those are stored in individual `Point`s.\n", "- `LabeledFrame` → Contains a set of `Instance`/`PredictedInstance`s for a single frame.\n", "- `Labels` → Contains a set of `LabeledFrame`s and the associated metadata for the videos and other information related to the project or predictions." - ], - "metadata": { - "id": "NqgGonrTRLg9" - } + ] }, { "cell_type": "markdown", @@ -61,6 +44,7 @@ }, { "cell_type": "code", + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -68,179 +52,19 @@ "id": "3GTiapGASisF", "outputId": "c7ce8c05-a473-4995-8cab-0f20d04a52b1" }, + "outputs": [], "source": [ "# This should take care of all the dependencies on colab:\n", - "!pip uninstall -y opencv-python opencv-contrib-python && pip install sleap\n", + "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n", + "!pip install -qqq sleap[pypi]\n", "\n", "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n", "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap" - ], - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Found existing installation: opencv-python 4.1.2.30\n", - "Uninstalling opencv-python-4.1.2.30:\n", - " Successfully uninstalled opencv-python-4.1.2.30\n", - "Found existing installation: opencv-contrib-python 4.1.2.30\n", - 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"Building wheels for collected packages: pykalman, jsmin\n", - " Building wheel for pykalman (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for pykalman: filename=pykalman-0.9.5-py3-none-any.whl size=48462 sha256=a06494160ef192a795ebcc248474d9c759e93594f237a46d572d71045302de71\n", - " Stored in directory: /root/.cache/pip/wheels/6a/04/02/2dda6ea59c66d9e685affc8af3a31ad3a5d87b7311689efce6\n", - " Building wheel for jsmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for jsmin: filename=jsmin-3.0.1-py3-none-any.whl size=13782 sha256=11175f12c4cdb3583f65125aa1f875e232ab437f5d9bdf1a6a73fbdb3d9ba69a\n", - " Stored in directory: /root/.cache/pip/wheels/a4/0b/64/fb4f87526ecbdf7921769a39d91dcfe4860e621cf15b8250d6\n", - "Successfully built pykalman jsmin\n", - "Installing collected packages: keras-applications, tf-estimator-nightly, shiboken2, opencv-python, image-classifiers, efficientnet, commonmark, colorama, attrs, segmentation-models, scikit-video, rich, qimage2ndarray, python-rapidjson, PySide2, pykalman, opencv-python-headless, jsonpickle, jsmin, imgstore, imgaug, cattrs, sleap\n", - " Attempting uninstall: attrs\n", - " Found existing installation: attrs 21.4.0\n", - " Uninstalling attrs-21.4.0:\n", - " Successfully uninstalled attrs-21.4.0\n", - " Attempting uninstall: imgaug\n", - " Found existing installation: imgaug 0.2.9\n", - " Uninstalling imgaug-0.2.9:\n", - " Successfully uninstalled imgaug-0.2.9\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", - "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n", - "Successfully installed PySide2-5.14.1 attrs-21.2.0 cattrs-1.1.1 colorama-0.4.4 commonmark-0.9.1 efficientnet-1.0.0 image-classifiers-1.0.0 imgaug-0.4.0 imgstore-0.2.9 jsmin-3.0.1 jsonpickle-1.2 keras-applications-1.0.8 opencv-python-4.5.5.64 opencv-python-headless-4.5.5.62 pykalman-0.9.5 python-rapidjson-1.6 qimage2ndarray-1.8.3 rich-10.16.1 scikit-video-1.1.11 segmentation-models-1.0.1 shiboken2-5.14.1 sleap-1.2.2 tf-estimator-nightly-2.8.0.dev2021122109\n" - ] - } ] }, { "cell_type": "code", + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -248,76 +72,76 @@ "id": "0n8oqLWBU0v7", "outputId": "f9cdcfe1-d152-4a0a-b769-6f9f7d8c0cf0" }, - "source": [ - "# Test video:\n", - "!wget https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4\n", - "\n", - "# Test video labels (from predictions/not necessary for inference benchmarking):\n", - "!wget https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.slp\n", - "\n", - "# Bottom-up model:\n", - "# !wget https://storage.googleapis.com/sleap-data/reference/flies13/bu.210506_230852.multi_instance.n%3D1800.zip\n", - "\n", - "# Top-down model (two-stage):\n", - "!wget https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip\n", - "!wget https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip" - ], - "execution_count": 2, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "--2022-04-04 00:19:01-- https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4\n", - "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.97.128, 142.251.107.128, 173.194.214.128, ...\n", - "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.97.128|:443... connected.\n", + "--2023-08-31 12:03:50-- https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.176.16, 142.250.72.144, 172.217.12.144, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.176.16|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 85343812 (81M) [video/mp4]\n", - "Saving to: ‘190719_090330_wt_18159206_rig1.2@15000-17560.mp4’\n", + "Saving to: ‘190719_090330_wt_18159206_rig1.2@15000-17560.mp4.1’\n", "\n", - "190719_090330_wt_18 100%[===================>] 81.39M 142MB/s in 0.6s \n", + "190719_090330_wt_18 100%[===================>] 81.39M 27.7MB/s in 2.9s \n", "\n", - "2022-04-04 00:19:02 (142 MB/s) - ‘190719_090330_wt_18159206_rig1.2@15000-17560.mp4’ saved [85343812/85343812]\n", + "2023-08-31 12:03:53 (27.7 MB/s) - ‘190719_090330_wt_18159206_rig1.2@15000-17560.mp4.1’ saved [85343812/85343812]\n", "\n", - "--2022-04-04 00:19:02-- https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.slp\n", - "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.214.128, 173.194.215.128, 173.194.216.128, ...\n", - "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.214.128|:443... connected.\n", + "--2023-08-31 12:03:53-- https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.slp\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.188.240, 142.250.217.144, 142.250.68.16, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.188.240|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1581400 (1.5M) [application/octet-stream]\n", - "Saving to: ‘190719_090330_wt_18159206_rig1.2@15000-17560.slp’\n", + "Saving to: ‘190719_090330_wt_18159206_rig1.2@15000-17560.slp.1’\n", "\n", - "190719_090330_wt_18 100%[===================>] 1.51M --.-KB/s in 0.01s \n", + "190719_090330_wt_18 100%[===================>] 1.51M 3.99MB/s in 0.4s \n", "\n", - "2022-04-04 00:19:02 (151 MB/s) - ‘190719_090330_wt_18159206_rig1.2@15000-17560.slp’ saved [1581400/1581400]\n", + "2023-08-31 12:03:54 (3.99 MB/s) - ‘190719_090330_wt_18159206_rig1.2@15000-17560.slp.1’ saved [1581400/1581400]\n", "\n", - "--2022-04-04 00:19:02-- https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip\n", - "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.214.128, 173.194.215.128, 173.194.216.128, ...\n", - "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.214.128|:443... connected.\n", + "--2023-08-31 12:03:54-- https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.72.240, 142.250.188.240, 142.250.189.16, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.72.240|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 6372537 (6.1M) [application/zip]\n", - "Saving to: ‘centroid.fast.210504_182918.centroid.n=1800.zip’\n", + "Saving to: ‘centroid.fast.210504_182918.centroid.n=1800.zip.1’\n", "\n", - "centroid.fast.21050 100%[===================>] 6.08M --.-KB/s in 0.05s \n", + "centroid.fast.21050 100%[===================>] 6.08M --.-KB/s in 0.1s \n", "\n", - "2022-04-04 00:19:02 (134 MB/s) - ‘centroid.fast.210504_182918.centroid.n=1800.zip’ saved [6372537/6372537]\n", + "2023-08-31 12:03:54 (56.6 MB/s) - ‘centroid.fast.210504_182918.centroid.n=1800.zip.1’ saved [6372537/6372537]\n", "\n", - "--2022-04-04 00:19:02-- https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip\n", - "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.216.128, 173.194.217.128, 173.194.218.128, ...\n", - "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.216.128|:443... connected.\n", + "--2023-08-31 12:03:54-- https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.14.112, 142.250.176.16, 142.250.72.176, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.14.112|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 30775963 (29M) [application/zip]\n", - "Saving to: ‘td_fast.210505_012601.centered_instance.n=1800.zip’\n", + "Saving to: ‘td_fast.210505_012601.centered_instance.n=1800.zip.1’\n", "\n", - "td_fast.210505_0126 100%[===================>] 29.35M 190MB/s in 0.2s \n", + "td_fast.210505_0126 100%[===================>] 29.35M 21.3MB/s in 1.4s \n", "\n", - "2022-04-04 00:19:03 (190 MB/s) - ‘td_fast.210505_012601.centered_instance.n=1800.zip’ saved [30775963/30775963]\n", + "2023-08-31 12:03:56 (21.3 MB/s) - ‘td_fast.210505_012601.centered_instance.n=1800.zip.1’ saved [30775963/30775963]\n", "\n" ] } + ], + "source": [ + "# Test video:\n", + "!wget https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4\n", + "\n", + "# Test video labels (from predictions/not necessary for inference benchmarking):\n", + "!wget https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.slp\n", + "\n", + "# Bottom-up model:\n", + "# !wget https://storage.googleapis.com/sleap-data/reference/flies13/bu.210506_230852.multi_instance.n%3D1800.zip\n", + "\n", + "# Top-down model (two-stage):\n", + "!wget https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip\n", + "!wget https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip" ] }, { "cell_type": "code", + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -325,30 +149,42 @@ "id": "F-zzLnAoWrC5", "outputId": "b0ae7571-3ac0-42c7-d50f-982e4d9a459f" }, - "source": [ - "!ls -lah" - ], - "execution_count": 3, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "total 119M\n", - "drwxr-xr-x 1 root root 4.0K Apr 4 00:19 .\n", - "drwxr-xr-x 1 root root 4.0K Apr 4 00:15 ..\n", - "-rw-r--r-- 1 root root 82M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.mp4\n", - "-rw-r--r-- 1 root root 1.6M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.slp\n", - "-rw-r--r-- 1 root root 6.1M May 20 2021 'centroid.fast.210504_182918.centroid.n=1800.zip'\n", - "drwxr-xr-x 4 root root 4.0K Mar 23 14:21 .config\n", - "drwxr-xr-x 1 root root 4.0K Mar 23 14:22 sample_data\n", - "-rw-r--r-- 1 root root 30M May 20 2021 'td_fast.210505_012601.centered_instance.n=1800.zip'\n" + "total 239M\n", + "drwxrwxr-x 3 talmolab talmolab 4.0K Aug 31 12:03 .\n", + "drwxrwxr-x 7 talmolab talmolab 4.0K Aug 31 11:39 ..\n", + "-rw-rw-r-- 1 talmolab talmolab 82M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.mp4\n", + "-rw-rw-r-- 1 talmolab talmolab 82M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.mp4.1\n", + "-rw-rw-r-- 1 talmolab talmolab 1.6M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.slp\n", + "-rw-rw-r-- 1 talmolab talmolab 1.6M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.slp.1\n", + "drwxrwxr-x 2 talmolab talmolab 4.0K Jun 20 10:00 analysis_example\n", + "-rw-rw-r-- 1 talmolab talmolab 713K Jun 20 10:00 Analysis_examples.ipynb\n", + "-rw-rw-r-- 1 talmolab talmolab 6.1M May 20 2021 'centroid.fast.210504_182918.centroid.n=1800.zip'\n", + "-rw-rw-r-- 1 talmolab talmolab 6.1M May 20 2021 'centroid.fast.210504_182918.centroid.n=1800.zip.1'\n", + "-rw-rw-r-- 1 talmolab talmolab 486K Aug 31 11:39 Data_structures.ipynb\n", + "-rw-rw-r-- 1 talmolab talmolab 4.1K Jun 20 10:00 index.rst\n", + "-rw-rw-r-- 1 talmolab talmolab 197K Aug 31 11:39 Interactive_and_realtime_inference.ipynb\n", + "-rw-rw-r-- 1 talmolab talmolab 398K Aug 31 11:39 Interactive_and_resumable_training.ipynb\n", + "-rw-rw-r-- 1 talmolab talmolab 149K Aug 31 11:39 Model_evaluation.ipynb\n", + "-rw-rw-r-- 1 talmolab talmolab 150K Aug 31 11:39 Post_inference_tracking.ipynb\n", + "-rw-rw-r-- 1 talmolab talmolab 30M May 20 2021 'td_fast.210505_012601.centered_instance.n=1800.zip'\n", + "-rw-rw-r-- 1 talmolab talmolab 30M May 20 2021 'td_fast.210505_012601.centered_instance.n=1800.zip.1'\n", + "-rw-rw-r-- 1 talmolab talmolab 9.5K Aug 31 11:39 Training_and_inference_on_an_example_dataset.ipynb\n", + "-rw-rw-r-- 1 talmolab talmolab 12K Aug 31 11:39 Training_and_inference_using_Google_Drive.ipynb\n" ] } + ], + "source": [ + "!ls -lah" ] }, { "cell_type": "code", + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -356,6 +192,51 @@ "id": "w6xCj73QXM0t", "outputId": "47d181ba-9272-4b9d-ab2a-0fcae34f38d1" }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-31 12:03:56.989133: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-08-31 12:03:57.058048: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2023-08-31 12:03:57.060007: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n", + "2023-08-31 12:03:57.060013: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", + "2023-08-31 12:03:57.445179: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n", + "2023-08-31 12:03:57.445232: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n", + "2023-08-31 12:03:57.445236: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SLEAP: 1.3.2\n", + "TensorFlow: 2.11.0\n", + "Numpy: 1.21.6\n", + "Python: 3.7.12\n", + "OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n", + "GPUs: None detected.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-31 12:03:58.223182: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-08-31 12:03:58.223923: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n", + "2023-08-31 12:03:58.223968: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n", + "2023-08-31 12:03:58.223999: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n", + "2023-08-31 12:03:58.224028: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n", + "2023-08-31 12:03:58.224057: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n", + "2023-08-31 12:03:58.224084: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n", + "2023-08-31 12:03:58.224111: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n", + "2023-08-31 12:03:58.224140: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n", + "2023-08-31 12:03:58.224144: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1934] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", + "Skipping registering GPU devices...\n" + ] + } + ], "source": [ "import sleap\n", "\n", @@ -369,26 +250,6 @@ "# Print some info:\n", "sleap.versions()\n", "sleap.system_summary()" - ], - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n", - "SLEAP: 1.2.2\n", - "TensorFlow: 2.8.0\n", - "Numpy: 1.21.5\n", - "Python: 3.7.13\n", - "OS: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\n", - "GPUs: 1/1 available\n", - " Device: /physical_device:GPU:0\n", - " Available: True\n", - " Initalized: False\n", - " Memory growth: True\n" - ] - } ] }, { @@ -402,17 +263,18 @@ }, { "cell_type": "markdown", + "metadata": { + "id": "0Fyey-smRjXx" + }, "source": [ "SLEAP can read videos in a variety of different formats through the `sleap.load_video` high level API. Once loaded, the `sleap.Video` object allows you to access individual frames as if the it were a standard numpy array.\n", "\n", "**Note:** The actual frames are not loaded until you access them so we don't blow up our memory when using long videos." - ], - "metadata": { - "id": "0Fyey-smRjXx" - } + ] }, { "cell_type": "code", + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -420,6 +282,16 @@ "id": "cH_qfme2We7k", "outputId": "cb6aaf9c-ab38-4b3b-ffac-8acd78bf13c1" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2560, 1024, 1024, 1)\n", + "(4, 1024, 1024, 1) uint8\n" + ] + } + ], "source": [ "# Videos can be represented agnostic to the backend format\n", "video = sleap.load_video(\"190719_090330_wt_18159206_rig1.2@15000-17560.mp4\")\n", @@ -430,17 +302,6 @@ "# And we can load images in the video using array indexing:\n", "imgs = video[:4]\n", "print(imgs.shape, imgs.dtype)" - ], - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "(2560, 1024, 1024, 1)\n", - "(4, 1024, 1024, 1) uint8\n" - ] - } ] }, { @@ -463,9 +324,20 @@ }, { "cell_type": "code", + "execution_count": 8, "metadata": { "id": "wnIgeeivXiln" }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-31 12:03:58.498908: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], "source": [ "# Top-down\n", "predictor = sleap.load_model([\n", @@ -475,9 +347,7 @@ "\n", "# Bottom-up\n", "# predictor = sleap.load_model(\"bu.210506_230852.multi_instance.n=1800.zip\")" - ], - "execution_count": 6, - "outputs": [] + ] }, { "cell_type": "markdown", @@ -490,6 +360,7 @@ }, { "cell_type": "code", + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -502,61 +373,67 @@ "id": "4RWl4PwTZkuN", "outputId": "82141aed-1fa1-4d44-8bad-d8d78a642cd7" }, - "source": [ - "labels = predictor.predict(video)\n", - "labels" - ], - "execution_count": 7, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - "Output()" - ], "application/vnd.jupyter.widget-view+json": { + "model_id": "cf38d776e9fc48ada47705ce018c64af", "version_major": 2, - "version_minor": 0, - "model_id": "581b3a9402bc4837bde932e98fa475a7" - } + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-31 12:04:01.923466: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: \"CropAndResize\" attr { key: \"T\" value { type: DT_FLOAT } } attr { key: \"extrapolation_value\" value { f: 0 } } attr { key: \"method\" value { s: \"bilinear\" } } inputs { dtype: DT_FLOAT shape { dim { size: -45 } dim { size: -46 } dim { size: -47 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -15 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: \"CPU\" vendor: \"GenuineIntel\" model: \"103\" frequency: 3600 num_cores: 16 environment { key: \"cpu_instruction_set\" value: \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\" } environment { key: \"eigen\" value: \"3.4.90\" } l1_cache_size: 49152 l2_cache_size: 524288 l3_cache_size: 16777216 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: -48 } dim { size: -49 } dim { size: 1 } } }\n", + "2023-08-31 12:04:01.923717: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: \"CropAndResize\" attr { key: \"T\" value { type: DT_UINT8 } } attr { key: \"extrapolation_value\" value { f: 0 } } attr { key: \"method\" value { s: \"bilinear\" } } inputs { dtype: DT_UINT8 shape { dim { size: 4 } dim { size: 1024 } dim { size: 1024 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -15 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: \"CPU\" vendor: \"GenuineIntel\" model: \"103\" frequency: 3600 num_cores: 16 environment { key: \"cpu_instruction_set\" value: \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\" } environment { key: \"eigen\" value: \"3.4.90\" } l1_cache_size: 49152 l2_cache_size: 524288 l3_cache_size: 16777216 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: -56 } dim { size: -57 } dim { size: 1 } } }\n", + "2023-08-31 12:04:01.926044: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: \"CropAndResize\" attr { key: \"T\" value { type: DT_FLOAT } } attr { key: \"extrapolation_value\" value { f: 0 } } attr { key: \"method\" value { s: \"bilinear\" } } inputs { dtype: DT_FLOAT shape { dim { size: -90 } dim { size: -91 } dim { size: -92 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -20 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -20 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: \"CPU\" vendor: \"GenuineIntel\" model: \"103\" frequency: 3600 num_cores: 16 environment { key: \"cpu_instruction_set\" value: \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\" } environment { key: \"eigen\" value: \"3.4.90\" } l1_cache_size: 49152 l2_cache_size: 524288 l3_cache_size: 16777216 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -20 } dim { size: -94 } dim { size: -95 } dim { size: 1 } } }\n" + ] }, { - "output_type": "display_data", "data": { - "text/plain": [ - "" - ], "text/html": [ "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "\n"
-            ],
             "text/html": [
               "
\n",
               "
\n"
+            ],
+            "text/plain": [
+              "\n"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "Labels(labeled_frames=2560, videos=1, skeletons=1, tracks=0)"
             ]
           },
+          "execution_count": 9,
           "metadata": {},
-          "execution_count": 7
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "labels = predictor.predict(video)\n",
+        "labels"
       ]
     },
     {
@@ -570,6 +447,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -577,25 +455,25 @@
         "id": "EgL-bqRj-l6R",
         "outputId": "3fd8f355-92b1-4bbb-b7e9-d564b007d97b"
       },
-      "source": [
-        "labels.videos"
-      ],
-      "execution_count": 8,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "[Video(backend=MediaVideo(filename='190719_090330_wt_18159206_rig1.2@15000-17560.mp4', grayscale=True, bgr=True, dataset='', input_format='channels_last'))]"
             ]
           },
+          "execution_count": 10,
           "metadata": {},
-          "execution_count": 8
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "labels.videos"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -603,21 +481,20 @@
         "id": "EOu9c9ly-nkN",
         "outputId": "3e66210c-12f6-48e4-c829-41aa3768b140"
       },
-      "source": [
-        "labels.skeletons"
-      ],
-      "execution_count": 9,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "[Skeleton(name='Skeleton-0', nodes=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], edges=[('thorax', 'head'), ('thorax', 'abdomen'), ('thorax', 'wingL'), ('thorax', 'wingR'), ('thorax', 'forelegL4'), ('thorax', 'forelegR4'), ('thorax', 'midlegL4'), ('thorax', 'midlegR4'), ('thorax', 'hindlegL4'), ('thorax', 'hindlegR4'), ('head', 'eyeL'), ('head', 'eyeR')], symmetries=[('wingL', 'wingR'), ('forelegL4', 'forelegR4'), ('hindlegL4', 'hindlegR4'), ('eyeL', 'eyeR'), ('midlegL4', 'midlegR4')])]"
+              "[Skeleton(name='Skeleton-0', description='None', nodes=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], edges=[('thorax', 'head'), ('thorax', 'abdomen'), ('thorax', 'wingL'), ('thorax', 'wingR'), ('thorax', 'forelegL4'), ('thorax', 'forelegR4'), ('thorax', 'midlegL4'), ('thorax', 'midlegR4'), ('thorax', 'hindlegL4'), ('thorax', 'hindlegR4'), ('head', 'eyeL'), ('head', 'eyeR')], symmetries=[('forelegL4', 'forelegR4'), ('wingL', 'wingR'), ('eyeL', 'eyeR'), ('midlegL4', 'midlegR4'), ('hindlegL4', 'hindlegR4')])]"
             ]
           },
+          "execution_count": 11,
           "metadata": {},
-          "execution_count": 9
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "labels.skeletons"
       ]
     },
     {
@@ -631,6 +508,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -638,22 +516,21 @@
         "id": "pGcyrjKf8hp4",
         "outputId": "1ff0ab5a-5a67-4d35-c09f-21adbcec655e"
       },
-      "source": [
-        "labeled_frame = labels[0]  # shortcut for labels.labeled_frames[0]\n",
-        "labeled_frame"
-      ],
-      "execution_count": 10,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "LabeledFrame(video=MediaVideo('190719_090330_wt_18159206_rig1.2@15000-17560.mp4'), frame_idx=0, instances=2)"
             ]
           },
+          "execution_count": 12,
           "metadata": {},
-          "execution_count": 10
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "labeled_frame = labels[0]  # shortcut for labels.labeled_frames[0]\n",
+        "labeled_frame"
       ]
     },
     {
@@ -667,6 +544,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 13,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -675,21 +553,20 @@
         "id": "s2YiRWSa7f6D",
         "outputId": "3f76ae98-dd72-4c2e-ac06-9bfe3b2c2637"
       },
-      "source": [
-        "labels[0].plot(scale=0.5)"
-      ],
-      "execution_count": 11,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "labels[0].plot(scale=0.5)"
       ]
     },
     {
@@ -703,6 +580,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 14,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -710,26 +588,26 @@
         "id": "ZP9Z0etc9e0c",
         "outputId": "00986c80-23d0-43fa-f4f9-c60482e5293e"
       },
-      "source": [
-        "labeled_frame.instances"
-      ],
-      "execution_count": 12,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "[PredictedInstance(video=Video(filename=190719_090330_wt_18159206_rig1.2@15000-17560.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=0, points=[head: (234.2, 430.5, 0.98), thorax: (271.6, 436.1, 0.94), abdomen: (308.0, 438.6, 0.59), wingL: (321.8, 440.1, 0.39), wingR: (322.0, 436.8, 0.49), forelegL4: (246.1, 450.6, 0.92), forelegR4: (242.3, 413.9, 0.78), midlegL4: (285.8, 459.9, 0.47), midlegR4: (272.3, 406.7, 0.77), hindlegR4: (317.6, 430.6, 0.30), eyeL: (242.1, 441.9, 0.89), eyeR: (245.3, 420.9, 0.92)], score=0.95, track=None, tracking_score=0.00),\n",
               " PredictedInstance(video=Video(filename=190719_090330_wt_18159206_rig1.2@15000-17560.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=0, points=[head: (319.4, 435.9, 0.83), thorax: (354.4, 435.2, 0.80), abdomen: (368.3, 433.8, 0.71), wingL: (393.9, 480.3, 0.83), wingR: (398.4, 430.0, 0.81), forelegL4: (307.8, 445.7, 0.26), forelegR4: (305.6, 421.4, 0.69), midlegL4: (325.7, 475.0, 0.94), midlegR4: (331.8, 385.1, 0.88), hindlegL4: (363.7, 474.1, 0.88), hindlegR4: (376.0, 398.4, 0.52), eyeL: (329.3, 445.6, 0.90), eyeR: (327.9, 425.1, 0.84)], score=0.84, track=None, tracking_score=0.00)]"
             ]
           },
+          "execution_count": 14,
           "metadata": {},
-          "execution_count": 12
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "labeled_frame.instances"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 15,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -737,22 +615,21 @@
         "id": "Y-stVhiw9uIr",
         "outputId": "4cd7dbdf-bd91-4037-b971-3a17c85193bd"
       },
-      "source": [
-        "instance = labeled_frame[0]  # shortcut for labeled_frame.instances[0]\n",
-        "instance"
-      ],
-      "execution_count": 13,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "PredictedInstance(video=Video(filename=190719_090330_wt_18159206_rig1.2@15000-17560.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=0, points=[head: (234.2, 430.5, 0.98), thorax: (271.6, 436.1, 0.94), abdomen: (308.0, 438.6, 0.59), wingL: (321.8, 440.1, 0.39), wingR: (322.0, 436.8, 0.49), forelegL4: (246.1, 450.6, 0.92), forelegR4: (242.3, 413.9, 0.78), midlegL4: (285.8, 459.9, 0.47), midlegR4: (272.3, 406.7, 0.77), hindlegR4: (317.6, 430.6, 0.30), eyeL: (242.1, 441.9, 0.89), eyeR: (245.3, 420.9, 0.92)], score=0.95, track=None, tracking_score=0.00)"
             ]
           },
+          "execution_count": 15,
           "metadata": {},
-          "execution_count": 13
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "instance = labeled_frame[0]  # shortcut for labeled_frame.instances[0]\n",
+        "instance"
       ]
     },
     {
@@ -766,6 +643,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 16,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -773,32 +651,31 @@
         "id": "7xK-uGJZ905J",
         "outputId": "102accd0-ba45-44b0-b839-eff15a06245a"
       },
-      "source": [
-        "instance.points"
-      ],
-      "execution_count": 14,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "(PredictedPoint(x=234.244384765625, y=430.52001953125, visible=True, complete=False, score=0.9790461659431458),\n",
-              " PredictedPoint(x=271.5894470214844, y=436.1461181640625, visible=True, complete=False, score=0.9357967376708984),\n",
-              " PredictedPoint(x=308.02899169921875, y=438.5711975097656, visible=True, complete=False, score=0.5859644412994385),\n",
-              " PredictedPoint(x=321.8167419433594, y=440.0872802734375, visible=True, complete=False, score=0.3912011682987213),\n",
-              " PredictedPoint(x=322.0196533203125, y=436.77008056640625, visible=True, complete=False, score=0.48613619804382324),\n",
-              " PredictedPoint(x=246.1430206298828, y=450.56182861328125, visible=True, complete=False, score=0.9176540970802307),\n",
-              " PredictedPoint(x=242.2632293701172, y=413.94976806640625, visible=True, complete=False, score=0.7807964086532593),\n",
-              " PredictedPoint(x=285.78167724609375, y=459.9156494140625, visible=True, complete=False, score=0.4739593267440796),\n",
-              " PredictedPoint(x=272.27996826171875, y=406.71759033203125, visible=True, complete=False, score=0.7721188068389893),\n",
-              " PredictedPoint(x=317.5997619628906, y=430.6052551269531, visible=True, complete=False, score=0.2960105538368225),\n",
-              " PredictedPoint(x=242.1038055419922, y=441.94561767578125, visible=True, complete=False, score=0.8855815529823303),\n",
-              " PredictedPoint(x=245.3200225830078, y=420.93609619140625, visible=True, complete=False, score=0.9199579954147339))"
+              "(PredictedPoint(x=234.24440002441406, y=430.52008056640625, visible=True, complete=False, score=0.9790770411491394),\n",
+              " PredictedPoint(x=271.58941650390625, y=436.1461486816406, visible=True, complete=False, score=0.9358043670654297),\n",
+              " PredictedPoint(x=308.02960205078125, y=438.57135009765625, visible=True, complete=False, score=0.5861632227897644),\n",
+              " PredictedPoint(x=321.81768798828125, y=440.08721923828125, visible=True, complete=False, score=0.39127233624458313),\n",
+              " PredictedPoint(x=322.0193176269531, y=436.7702941894531, visible=True, complete=False, score=0.48629727959632874),\n",
+              " PredictedPoint(x=246.14295959472656, y=450.5621643066406, visible=True, complete=False, score=0.9176925420761108),\n",
+              " PredictedPoint(x=242.2632598876953, y=413.9497375488281, visible=True, complete=False, score=0.780803382396698),\n",
+              " PredictedPoint(x=285.78155517578125, y=459.91552734375, visible=True, complete=False, score=0.47393468022346497),\n",
+              " PredictedPoint(x=272.280029296875, y=406.71759033203125, visible=True, complete=False, score=0.7721256017684937),\n",
+              " PredictedPoint(x=317.598876953125, y=430.6053466796875, visible=True, complete=False, score=0.296230286359787),\n",
+              " PredictedPoint(x=242.10415649414062, y=441.9450378417969, visible=True, complete=False, score=0.8855596780776978),\n",
+              " PredictedPoint(x=245.32009887695312, y=420.9360656738281, visible=True, complete=False, score=0.9200019240379333))"
             ]
           },
+          "execution_count": 16,
           "metadata": {},
-          "execution_count": 14
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "instance.points"
       ]
     },
     {
@@ -812,6 +689,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 17,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -819,31 +697,30 @@
         "id": "jEWddPpg93GM",
         "outputId": "ddd09bae-83e1-48f7-b870-3155a68e6ecb"
       },
-      "source": [
-        "pts = instance.numpy()\n",
-        "print(pts)"
-      ],
-      "execution_count": 15,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "[[234.24438477 430.52001953]\n",
-            " [271.58944702 436.14611816]\n",
-            " [308.0289917  438.57119751]\n",
-            " [321.81674194 440.08728027]\n",
-            " [322.01965332 436.77008057]\n",
-            " [246.14302063 450.56182861]\n",
-            " [242.26322937 413.94976807]\n",
-            " [285.78167725 459.91564941]\n",
-            " [272.27996826 406.71759033]\n",
+            "[[234.24440002 430.52008057]\n",
+            " [271.5894165  436.14614868]\n",
+            " [308.02960205 438.5713501 ]\n",
+            " [321.81768799 440.08721924]\n",
+            " [322.01931763 436.77029419]\n",
+            " [246.14295959 450.56216431]\n",
+            " [242.26325989 413.94973755]\n",
+            " [285.78155518 459.91552734]\n",
+            " [272.2800293  406.71759033]\n",
             " [         nan          nan]\n",
-            " [317.59976196 430.60525513]\n",
-            " [242.10380554 441.94561768]\n",
-            " [245.32002258 420.93609619]]\n"
+            " [317.59887695 430.60534668]\n",
+            " [242.10415649 441.94503784]\n",
+            " [245.32009888 420.93606567]]\n"
           ]
         }
+      ],
+      "source": [
+        "pts = instance.numpy()\n",
+        "print(pts)"
       ]
     },
     {
@@ -857,15 +734,15 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 18,
       "metadata": {
         "id": "Thx9INKJ_JHk"
       },
+      "outputs": [],
       "source": [
         "labels = sleap.Labels(labels.labeled_frames[:4])  # crop to the first few labels for this example\n",
         "labels.save(\"labels_with_images.pkg.slp\", with_images=True, embed_all_labeled=True)"
-      ],
-      "execution_count": 16,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -878,14 +755,14 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 19,
       "metadata": {
         "id": "fJvcyJDw_Wbz"
       },
+      "outputs": [],
       "source": [
         "!rm \"190719_090330_wt_18159206_rig1.2@15000-17560.mp4\""
-      ],
-      "execution_count": 17,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -898,6 +775,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 20,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -905,26 +783,26 @@
         "id": "enTHiSIY_qg0",
         "outputId": "96589190-e771-4fd8-bc41-7cd7bf7262d9"
       },
-      "source": [
-        "labels = sleap.load_file(\"labels_with_images.pkg.slp\")\n",
-        "labels"
-      ],
-      "execution_count": 18,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "Labels(labeled_frames=4, videos=1, skeletons=1, tracks=0)"
             ]
           },
+          "execution_count": 20,
           "metadata": {},
-          "execution_count": 18
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "labels = sleap.load_file(\"labels_with_images.pkg.slp\")\n",
+        "labels"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 21,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -933,22 +811,47 @@
         "id": "X8zy1PyP_2cW",
         "outputId": "757240fe-eb6f-465f-b079-170ef889144d"
       },
-      "source": [
-        "labels[0].plot(scale=0.5)"
-      ],
-      "execution_count": 19,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "labels[0].plot(scale=0.5)"
       ]
     }
-  ]
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "name": "SLEAP - Data structures.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
diff --git a/docs/notebooks/Interactive_and_realtime_inference.ipynb b/docs/notebooks/Interactive_and_realtime_inference.ipynb
index 2460ccd51..8d0107fa7 100644
--- a/docs/notebooks/Interactive_and_realtime_inference.ipynb
+++ b/docs/notebooks/Interactive_and_realtime_inference.ipynb
@@ -1,18 +1,4 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "SLEAP - Interactive and realtime inference.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "markdown",
@@ -26,16 +12,16 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "DpvQa3M3n7jC"
+      },
       "source": [
         "# Interactive and realtime inference\n",
         "\n",
         "For most workflows, using the [`sleap-track` CLI](https://sleap.ai/guides/cli.html#sleap-track) is probably the most convenient option, but if you're developing a custom application you can take advantage of SLEAP's inference API to use your trained models in your own custom scripts.\n",
         "\n",
         "In this notebook we will explore how to predict poses from raw images in pure Python, and do some basic benchmarking on a simulated realtime predictor that could be used to enable closed-loop experiments."
-      ],
-      "metadata": {
-        "id": "DpvQa3M3n7jC"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -52,197 +38,47 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 1,
       "metadata": {
-        "id": "BYxJ2rJOMW8B",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "BYxJ2rJOMW8B",
         "outputId": "6ef53f4c-5074-4f41-8523-3d989a0f2844"
       },
-      "source": [
-        "# This should take care of all the dependencies on colab:\n",
-        "!pip uninstall -y opencv-python opencv-contrib-python && pip install sleap\n",
-        "\n",
-        "\n",
-        "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n",
-        "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap"
-      ],
-      "execution_count": 1,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "Found existing installation: opencv-python 4.1.2.30\n",
-            "Uninstalling opencv-python-4.1.2.30:\n",
-            "  Successfully uninstalled opencv-python-4.1.2.30\n",
-            "Found existing installation: opencv-contrib-python 4.1.2.30\n",
-            "Uninstalling opencv-contrib-python-4.1.2.30:\n",
-            "  Successfully uninstalled opencv-contrib-python-4.1.2.30\n",
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-            "  Building wheel for jsmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for jsmin: filename=jsmin-3.0.1-py3-none-any.whl size=13782 sha256=fd47efc594f3416388e6e074d4602a5b5559ce66e69e621778a182409f5a004c\n",
-            "  Stored in directory: /root/.cache/pip/wheels/a4/0b/64/fb4f87526ecbdf7921769a39d91dcfe4860e621cf15b8250d6\n",
-            "Successfully built pykalman jsmin\n",
-            "Installing collected packages: keras-applications, tf-estimator-nightly, shiboken2, opencv-python, image-classifiers, efficientnet, commonmark, colorama, attrs, segmentation-models, scikit-video, rich, qimage2ndarray, python-rapidjson, PySide2, pykalman, opencv-python-headless, jsonpickle, jsmin, imgstore, imgaug, cattrs, sleap\n",
-            "  Attempting uninstall: attrs\n",
-            "    Found existing installation: attrs 21.4.0\n",
-            "    Uninstalling attrs-21.4.0:\n",
-            "      Successfully uninstalled attrs-21.4.0\n",
-            "  Attempting uninstall: imgaug\n",
-            "    Found existing installation: imgaug 0.2.9\n",
-            "    Uninstalling imgaug-0.2.9:\n",
-            "      Successfully uninstalled imgaug-0.2.9\n",
-            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
-            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n",
-            "Successfully installed PySide2-5.14.1 attrs-21.2.0 cattrs-1.1.1 colorama-0.4.4 commonmark-0.9.1 efficientnet-1.0.0 image-classifiers-1.0.0 imgaug-0.4.0 imgstore-0.2.9 jsmin-3.0.1 jsonpickle-1.2 keras-applications-1.0.8 opencv-python-4.5.5.64 opencv-python-headless-4.5.5.62 pykalman-0.9.5 python-rapidjson-1.6 qimage2ndarray-1.8.3 rich-10.16.1 scikit-video-1.1.11 segmentation-models-1.0.1 shiboken2-5.14.1 sleap-1.2.2 tf-estimator-nightly-2.8.0.dev2021122109\n"
+            "\u001b[31mERROR: Cannot uninstall opencv-python 4.6.0, RECORD file not found. Hint: The package was installed by conda.\u001b[0m\u001b[31m\n",
+            "\u001b[0m\u001b[31mERROR: Cannot uninstall shiboken2 5.15.6, RECORD file not found. You might be able to recover from this via: 'pip install --force-reinstall --no-deps shiboken2==5.15.6'.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
           ]
         }
+      ],
+      "source": [
+        "# This should take care of all the dependencies on colab:\n",
+        "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n",
+        "!pip install -qqq sleap[pypi]\n",
+        "\n",
+        "\n",
+        "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n",
+        "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Import SLEAP to make sure it installed correctly and print out some information about the system:"
-      ],
       "metadata": {
         "id": "qjfoeOZvpV8o"
-      }
+      },
+      "source": [
+        "Import SLEAP to make sure it installed correctly and print out some information about the system:"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -250,31 +86,38 @@
         "id": "jftAOyvvuQeh",
         "outputId": "5c415dbc-7ecf-46db-8271-c17cc89552a4"
       },
-      "source": [
-        "import sleap\n",
-        "sleap.disable_preallocation()  # This initializes the GPU and prevents TensorFlow from filling the entire GPU memory\n",
-        "sleap.versions()\n",
-        "sleap.system_summary()"
-      ],
-      "execution_count": 2,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
-            "SLEAP: 1.2.2\n",
-            "TensorFlow: 2.8.0\n",
+            "SLEAP: 1.3.2\n",
+            "TensorFlow: 2.7.0\n",
             "Numpy: 1.21.5\n",
-            "Python: 3.7.13\n",
-            "OS: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\n",
+            "Python: 3.7.12\n",
+            "OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n",
             "GPUs: 1/1 available\n",
             "  Device: /physical_device:GPU:0\n",
             "         Available: True\n",
             "        Initalized: False\n",
             "     Memory growth: True\n"
           ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2023-09-01 13:56:37.731425: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 13:56:37.735933: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 13:56:37.736867: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n"
+          ]
         }
+      ],
+      "source": [
+        "import sleap\n",
+        "sleap.disable_preallocation()  # This initializes the GPU and prevents TensorFlow from filling the entire GPU memory\n",
+        "sleap.versions()\n",
+        "sleap.system_summary()"
       ]
     },
     {
@@ -290,54 +133,79 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 3,
       "metadata": {
-        "id": "sDIF3RKdM86u",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "sDIF3RKdM86u",
         "outputId": "5d435b70-d296-4e19-b1b1-0cd9d509e9f3"
       },
-      "source": [
-        "!curl -L --output video.mp4 https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4\n",
-        "!curl -L --output centroid_model.zip https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip\n",
-        "!curl -L --output centered_instance_id_model.zip https://storage.googleapis.com/sleap-data/reference/flies13/td_id.fast.v2.210519_111253.multi_class_topdown.n%3D1800.zip\n",
-        "!ls -lah"
-      ],
-      "execution_count": 3,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
             "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-            "100 81.3M  100 81.3M    0     0   119M      0 --:--:-- --:--:-- --:--:--  119M\n",
+            "100 81.3M  100 81.3M    0     0  23.7M      0  0:00:03  0:00:03 --:--:-- 23.7M\n",
             "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
             "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-            "100 6223k  100 6223k    0     0  23.2M      0 --:--:-- --:--:-- --:--:-- 23.2M\n",
+            "100 6223k  100 6223k    0     0  30.2M      0 --:--:-- --:--:-- --:--:-- 30.3M\n",
             "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
             "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-            "100 32.2M  100 32.2M    0     0  62.4M      0 --:--:-- --:--:-- --:--:-- 62.4M\n",
-            "total 120M\n",
-            "drwxr-xr-x 1 root root 4.0K Apr  3 23:33 .\n",
-            "drwxr-xr-x 1 root root 4.0K Apr  3 23:31 ..\n",
-            "-rw-r--r-- 1 root root  33M Apr  3 23:33 centered_instance_id_model.zip\n",
-            "-rw-r--r-- 1 root root 6.1M Apr  3 23:33 centroid_model.zip\n",
-            "drwxr-xr-x 4 root root 4.0K Mar 23 14:21 .config\n",
-            "drwxr-xr-x 1 root root 4.0K Mar 23 14:22 sample_data\n",
-            "-rw-r--r-- 1 root root  82M Apr  3 23:33 video.mp4\n"
+            "100 32.2M  100 32.2M    0     0  14.5M      0  0:00:02  0:00:02 --:--:-- 14.5M\n",
+            "total 1.1G\n",
+            "drwxrwxr-x  5 talmolab talmolab 4.0K Sep  1 13:56  .\n",
+            "drwxrwxr-x 10 talmolab talmolab 4.0K Aug 31 15:43  ..\n",
+            "-rw-rw-r--  1 talmolab talmolab  82M May 20  2021  190719_090330_wt_18159206_rig1.2@15000-17560.mp4.1\n",
+            "-rw-rw-r--  1 talmolab talmolab 1.6M May 20  2021  190719_090330_wt_18159206_rig1.2@15000-17560.slp\n",
+            "-rw-rw-r--  1 talmolab talmolab 1.6M May 20  2021  190719_090330_wt_18159206_rig1.2@15000-17560.slp.1\n",
+            "drwxrwxr-x  2 talmolab talmolab 4.0K Jun 20 10:00  analysis_example\n",
+            "-rw-rw-r--  1 talmolab talmolab 713K Jun 20 10:00  Analysis_examples.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab  33M Sep  1 13:56  centered_instance_id_model.zip\n",
+            "-rw-rw-r--  1 talmolab talmolab 6.1M May 20  2021 'centroid.fast.210504_182918.centroid.n=1800.zip'\n",
+            "-rw-rw-r--  1 talmolab talmolab 6.1M May 20  2021 'centroid.fast.210504_182918.centroid.n=1800.zip.1'\n",
+            "-rw-rw-r--  1 talmolab talmolab 6.1M Sep  1 13:56  centroid_model.zip\n",
+            "drwxrwxr-x  4 talmolab talmolab 4.0K Sep  1 13:30  dataset\n",
+            "-rw-rw-r--  1 talmolab talmolab 481K Sep  1 13:49  Data_structures.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab 661K Aug 31 12:52  fly_clip.mp4\n",
+            "-rw-rw-r--  1 talmolab talmolab 4.1K Jun 20 10:00  index.rst\n",
+            "-rw-rw-r--  1 talmolab talmolab 197K Sep  1 13:53  Interactive_and_realtime_inference.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab 120K Aug 31 12:25  Interactive_and_resumable_training.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab 620M Aug 31 12:14  labels.pkg.slp\n",
+            "-rw-rw-r--  1 talmolab talmolab 1.6M Aug 31 12:05  labels_with_images.pkg.slp\n",
+            "-rw-rw-r--  1 talmolab talmolab 158K Aug 31 12:35  Model_evaluation.ipynb\n",
+            "drwxrwxr-x  4 talmolab talmolab 4.0K Sep  1 13:39  models\n",
+            "-rw-rw-r--  1 talmolab talmolab 157K Aug 31 12:52  Post_inference_tracking.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab 412K Aug 31 12:52  predictions.slp\n",
+            "-rw-rw-r--  1 talmolab talmolab 422K Aug 31 12:52  retracked.slp\n",
+            "-rw-rw-r--  1 talmolab talmolab  30M May 20  2021 'td_fast.210505_012601.centered_instance.n=1800.zip'\n",
+            "-rw-rw-r--  1 talmolab talmolab  30M May 20  2021 'td_fast.210505_012601.centered_instance.n=1800.zip.1'\n",
+            "-rw-rw-r--  1 talmolab talmolab  30M May 20  2021 'td_fast.210505_012601.centered_instance.n=1800.zip.2'\n",
+            "-rw-rw-r--  1 talmolab talmolab  78M May  6  2021  test.pkg.slp\n",
+            "-rw-rw-r--  1 talmolab talmolab  89M Sep  1 13:42  trained_models.zip\n",
+            "-rw-rw-r--  1 talmolab talmolab  94K Sep  1 13:44  Training_and_inference_on_an_example_dataset.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab  12K Aug 31 11:39  Training_and_inference_using_Google_Drive.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab  82M Sep  1 13:56  video.mp4\n"
           ]
         }
+      ],
+      "source": [
+        "!curl -L --output video.mp4 https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4\n",
+        "!curl -L --output centroid_model.zip https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip\n",
+        "!curl -L --output centered_instance_id_model.zip https://storage.googleapis.com/sleap-data/reference/flies13/td_id.fast.v2.210519_111253.multi_class_topdown.n%3D1800.zip\n",
+        "!ls -lah"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "**Note:** These zip files just have the contents of standard SLEAP model folders that are generated during training."
-      ],
       "metadata": {
         "id": "0edP4yp7PMJy"
-      }
+      },
+      "source": [
+        "**Note:** These zip files just have the contents of standard SLEAP model folders that are generated during training."
+      ]
     },
     {
       "cell_type": "markdown",
@@ -354,32 +222,45 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "predictor = sleap.load_model([\"centroid_model.zip\", \"centered_instance_id_model.zip\"], batch_size=16)"
-      ],
+      "execution_count": 4,
       "metadata": {
         "id": "cC7IKtPDOktW"
       },
-      "execution_count": 4,
-      "outputs": []
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2023-09-01 13:57:04.806004: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
+            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+            "2023-09-01 13:57:04.807011: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 13:57:04.807970: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 13:57:04.808962: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 13:57:05.103658: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 13:57:05.104377: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 13:57:05.105059: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 13:57:05.106019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21129 MB memory:  -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
+          ]
+        }
+      ],
+      "source": [
+        "predictor = sleap.load_model([\"centroid_model.zip\", \"centered_instance_id_model.zip\"], batch_size=16)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "w7xGANT7PfmL"
+      },
       "source": [
         "This function handles all the logic of loading trained models, reading the configurations used to train them, and constructs inference models that also include non-trainable operations like peak finding and instance grouping.\n",
         "\n",
         "Next, we'll load a video that we want to use for inference. SLEAP `Video` objects don't actually load the whole video into memory, they just provide a common numpy-like interface for reading from different file formats:"
-      ],
-      "metadata": {
-        "id": "w7xGANT7PfmL"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "video = sleap.load_video(\"video.mp4\")\n",
-        "video.shape, video.dtype"
-      ],
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -387,199 +268,128 @@
         "id": "CJ9-vuddPelx",
         "outputId": "9f09d46d-6808-471e-9aed-92a408b97b06"
       },
-      "execution_count": 5,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "((2560, 1024, 1024, 1), dtype('uint8'))"
             ]
           },
+          "execution_count": 5,
           "metadata": {},
-          "execution_count": 5
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "video = sleap.load_video(\"video.mp4\")\n",
+        "video.shape, video.dtype"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Our predictor is pretty flexible. It can handle a variety of different input formats, all of which will return a `Labels` object that contains all of our predictions:"
-      ],
       "metadata": {
         "id": "O3xA6cuTQ6sG"
-      }
+      },
+      "source": [
+        "Our predictor is pretty flexible. It can handle a variety of different input formats, all of which will return a `Labels` object that contains all of our predictions:"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "# Load frames to a numpy array.\n",
-        "imgs = video[:100]\n",
-        "print(f\"imgs.shape: {imgs.shape}\")\n",
-        "\n",
-        "# Predict on numpy array.\n",
-        "predictions = predictor.predict(imgs)\n",
-        "predictions"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 68,
-          "referenced_widgets": [
-            "d6ca46c1a214448098ad47270939d0c2",
-            "64f2d6a13449451190f6a01f3312235b"
-          ]
-        },
-        "id": "IdhwFe1dRG2K",
-        "outputId": "f5b7d30c-4fad-48b6-9652-c83933c9adf8"
-      },
       "execution_count": 6,
+      "metadata": {},
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0cc2e3a471764285a58d023906ba1f7a",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "d6ca46c1a214448098ad47270939d0c2"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "imgs.shape: (100, 1024, 1024, 1)\n"
           ]
         },
         {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              ""
-            ],
-            "text/html": [
-              "
\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\n"
-            ],
-            "text/html": [
-              "
\n",
-              "
\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Labels(labeled_frames=100, videos=1, skeletons=1, tracks=2)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 6
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Predict on the entire video with parallelizable loading/preprocessing:\n",
-        "predictions = predictor.predict(video)\n",
-        "predictions"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51,
-          "referenced_widgets": [
-            "0e9d4c257a4d4c45b02337a0e038e45e",
-            "fb2df858b0a444edb4b0f429743abd9f"
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2023-09-01 13:57:13.455046: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n"
           ]
         },
-        "id": "McsFHqx0Q6F0",
-        "outputId": "a648dac3-6e78-4fbd-e4b1-91389ead143d"
-      },
-      "execution_count": 7,
-      "outputs": [
         {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Output()"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "0e9d4c257a4d4c45b02337a0e038e45e"
-            }
-          },
-          "metadata": {}
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2023-09-01 13:57:15.358483: I tensorflow/stream_executor/cuda/cuda_blas.cc:1774] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
+          ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              ""
-            ],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "\n"
-            ],
             "text/html": [
               "
\n",
               "
\n"
+            ],
+            "text/plain": [
+              "\n"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "Labels(labeled_frames=2560, videos=1, skeletons=1, tracks=2)"
+              "Labels(labeled_frames=100, videos=1, skeletons=1, tracks=2)"
             ]
           },
+          "execution_count": 6,
           "metadata": {},
-          "execution_count": 7
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "# Load frames to a numpy array.\n",
+        "imgs = video[:100]\n",
+        "print(f\"imgs.shape: {imgs.shape}\")\n",
+        "\n",
+        "# Predict on numpy array.\n",
+        "predictions = predictor.predict(imgs)\n",
+        "predictions"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "We can then inspect the results of our predictor:"
-      ],
       "metadata": {
         "id": "E8Qm3Y8ERrFb"
-      }
+      },
+      "source": [
+        "We can then inspect the results of our predictor:"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "# Visualize a frame.\n",
-        "predictions[100].plot(scale=0.25)"
-      ],
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -588,27 +398,26 @@
         "id": "MhPh8uwaRFfT",
         "outputId": "29e5ae1f-bf9d-44ea-a2fe-573b51faaf67"
       },
-      "execution_count": 8,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "# Visualize a frame.\n",
+        "predictions[100].plot(scale=0.25)"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "# Inspect the contents of a single frame.\n",
-        "labeled_frame = predictions[100]\n",
-        "labeled_frame.instances"
-      ],
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -616,27 +425,28 @@
         "id": "Xyz5qfrFR3Cd",
         "outputId": "203d483f-6e1b-4e1e-ff89-0dc62488edad"
       },
-      "execution_count": 9,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "[PredictedInstance(video=Video(filename=video.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=100, points=[head: (212.5, 427.0, 0.94), thorax: (252.0, 433.1, 0.95), abdomen: (288.6, 439.3, 0.68), wingL: (304.5, 443.3, 0.88), wingR: (306.2, 435.8, 0.68), forelegL4: (216.2, 445.5, 0.88), forelegR4: (216.1, 410.0, 0.90), midlegL4: (244.4, 471.3, 0.90), midlegR4: (256.6, 408.9, 0.86), hindlegL4: (275.0, 459.2, 0.89), hindlegR4: (292.3, 412.0, 0.81), eyeL: (220.0, 438.0, 0.84), eyeR: (223.8, 417.5, 0.91)], score=0.99, track=Track(spawned_on=0, name='female'), tracking_score=0.00),\n",
               " PredictedInstance(video=Video(filename=video.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=100, points=[head: (313.7, 432.6, 0.87), thorax: (348.9, 427.9, 1.00), abdomen: (378.9, 425.8, 0.83), wingL: (397.0, 428.7, 0.89), wingR: (394.9, 420.7, 0.74), forelegL4: (307.4, 446.4, 0.88), forelegR4: (306.5, 422.5, 0.89), midlegL4: (341.6, 474.2, 0.97), midlegR4: (332.6, 386.3, 0.97), hindlegL4: (378.9, 458.8, 0.92), hindlegR4: (387.7, 394.8, 0.88), eyeL: (323.7, 442.1, 0.96), eyeR: (320.7, 420.8, 0.88)], score=0.99, track=Track(spawned_on=0, name='male'), tracking_score=0.00)]"
             ]
           },
+          "execution_count": 9,
           "metadata": {},
-          "execution_count": 9
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "# Inspect the contents of a single frame.\n",
+        "labeled_frame = predictions[100]\n",
+        "labeled_frame.instances"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "# Convert an instance to a numpy array:\n",
-        "labeled_frame[0].numpy()"
-      ],
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -644,10 +454,8 @@
         "id": "FDMcaIwtR7he",
         "outputId": "df3ead74-4505-4680-de86-2dbd531145e1"
       },
-      "execution_count": 10,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "rec.array([[212.51400757, 426.97024536],\n",
@@ -655,7 +463,7 @@
               "           [288.64355469, 439.3086853 ],\n",
               "           [304.53396606, 443.33477783],\n",
               "           [306.20336914, 435.77227783],\n",
-              "           [216.24688721, 445.4755249 ],\n",
+              "           [216.24688721, 445.47549438],\n",
               "           [216.14550781, 409.98342896],\n",
               "           [244.39497375, 471.31561279],\n",
               "           [256.61740112, 408.89056396],\n",
@@ -666,30 +474,30 @@
               "          dtype=float64)"
             ]
           },
+          "execution_count": 10,
           "metadata": {},
-          "execution_count": 10
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "# Convert an instance to a numpy array:\n",
+        "labeled_frame[0].numpy()"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "c6kRMZDYSKIp"
+      },
       "source": [
         "What if we don't want or need the inference results wrapped in the SLEAP structures?\n",
         "\n",
         "By using the low-level inference model, we can actually go directly from image to numpy arrays of our results:"
-      ],
-      "metadata": {
-        "id": "c6kRMZDYSKIp"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "imgs = video[:16]  # batch of 16 images\n",
-        "\n",
-        "predictions = predictor.inference_model.predict(imgs, numpy=True)\n",
-        "predictions"
-      ],
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -697,199 +505,30 @@
         "id": "pWo_bG1HSJaJ",
         "outputId": "d22e30e9-13ae-466b-d94c-ce787c96a818"
       },
-      "execution_count": 11,
       "outputs": [
         {
-          "output_type": "execute_result",
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "4/4 [==============================] - 2s 176ms/step\n"
+          ]
+        },
+        {
           "data": {
             "text/plain": [
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-              "        [0.98339975, 0.9842536 ]], dtype=float32),\n",
-              " 'centroids': array([[[271.8735 , 436.4811 ],\n",
-              "         [355.93707, 435.63477]],\n",
-              " \n",
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-              " \n",
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-              " \n",
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-              "          0.855937  , 0.83885545, 0.9163793 ]],\n",
-              " \n",
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-              "          0.145257  , 0.81688404, 0.88855964],\n",
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-              "          0.9375358 , 0.92730594, 0.8518941 ]],\n",
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-              "          0.21640284, 0.41097188, 0.9234465 , 0.8912649 , 0.8676514 ,\n",
-              "          0.91081864, 0.9236754 , 0.9313458 ]],\n",
-              " \n",
-              "        [[0.96605366, 0.9777925 , 0.67958933, 0.5347009 , 0.49430045,\n",
-              "          0.89868015, 0.88998073, 0.82294536, 0.49898368, 0.1423007 ,\n",
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-              "          0.89034295, 0.98887867, 0.8843883 ]],\n",
-              " \n",
-              "        [[0.89794546, 0.97743154, 0.5481075 , 0.52363163, 0.570176  ,\n",
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-              "          0.8713986 , 0.862072  , 0.91699666]],\n",
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-              "          0.87627506, 0.8575353 , 0.8765893 ]],\n",
-              " \n",
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-              "          0.7050669 , 0.94214785, 0.39777142, 0.7715537 , 0.617287  ,\n",
-              "          0.06328648, 1.0118883 , 0.8866795 ],\n",
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-              "          0.8836143 , 0.86114466, 0.88294595]],\n",
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-              "          0.94770956, 0.85123426, 0.9446316 ]],\n",
-              " \n",
-              "        [[0.94501513, 0.95821375, 0.7855571 , 0.7544449 , 0.58367   ,\n",
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-              "          0.24900919, 0.981288  , 0.92618316],\n",
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-              "          0.95540494, 0.9207019 , 0.8778761 ]],\n",
-              " \n",
-              "        [[0.93817955, 0.9492211 , 0.7767393 , 0.8758958 , 0.38491583,\n",
-              "          0.88775396, 0.9298349 , 0.8082794 , 0.69305503, 0.1668036 ,\n",
-              "          0.26728866, 0.9830228 , 0.9346242 ],\n",
-              "         [0.909315  , 0.9609095 , 0.840956  , 0.83797425, 0.8743328 ,\n",
-              "          0.82546026, 0.32881746, 0.54940474, 0.96532434, 0.98827827,\n",
-              "          0.85375595, 0.95603913, 0.93167067]],\n",
-              " \n",
-              "        [[0.9048101 , 0.9246041 , 0.7558464 , 0.80823594, 0.47512585,\n",
-              "          0.86846614, 0.9260269 , 0.8822637 , 0.7126984 , 0.15086724,\n",
-              "          0.22018576, 0.9016736 , 0.90536344],\n",
-              "         [0.91812086, 0.9669677 , 0.78534484, 0.88368094, 0.7989964 ,\n",
-              "          0.6972392 , 0.51700455, 0.8321577 , 0.9426196 , 0.9527976 ,\n",
-              "          0.9190021 , 0.9706677 , 0.9077022 ]],\n",
-              " \n",
-              "        [[0.9391487 , 0.93520033, 0.85189587, 0.72796357, 0.6884538 ,\n",
-              "          0.8768974 , 0.9508925 , 0.6879569 , 0.7112255 , 0.70129263,\n",
-              "          0.6031595 , 0.8761619 , 0.9142955 ],\n",
-              "         [0.8932256 , 0.9750102 , 0.7894063 , 0.8651795 , 0.7224442 ,\n",
-              "          0.8268989 , 0.45971498, 0.93260354, 0.9202294 , 0.94214976,\n",
-              "          0.88344055, 0.9803063 , 0.8976606 ]]], dtype=float32),\n",
-              " 'instance_peaks': array([[[[234.2223 , 430.62558],\n",
-              "          [271.50427, 436.13205],\n",
-              "          [309.87225, 436.65012],\n",
-              "          [324.12576, 438.39148],\n",
-              "          [320.34717, 435.95013],\n",
-              "          [246.42339, 450.67798],\n",
-              "          [242.37634, 413.81458],\n",
-              "          [285.56247, 460.2276 ],\n",
-              "          [273.45126, 406.51892],\n",
+              "{'instance_peaks': array([[[[234.2224 , 430.62598],\n",
+              "          [271.5043 , 436.13202],\n",
+              "          [309.87125, 436.64966],\n",
+              "          [324.12512, 438.3908 ],\n",
+              "          [320.3458 , 435.9504 ],\n",
+              "          [246.42352, 450.67786],\n",
+              "          [242.37636, 413.81458],\n",
+              "          [285.5624 , 460.22766],\n",
+              "          [273.45117, 406.51895],\n",
               "          [      nan,       nan],\n",
               "          [      nan,       nan],\n",
-              "          [241.9709 , 442.32263],\n",
-              "          [245.46785, 421.90225]],\n",
+              "          [241.9716 , 442.32303],\n",
+              "          [245.46788, 421.90228]],\n",
               " \n",
               "         [[319.80017, 435.48407],\n",
               "          [351.93695, 434.0301 ],\n",
@@ -906,19 +545,19 @@
               "          [328.1667 , 423.94733]]],\n",
               " \n",
               " \n",
-              "        [[[234.36911, 430.38037],\n",
+              "        [[[234.36913, 430.38037],\n",
               "          [271.65576, 436.0479 ],\n",
-              "          [311.67505, 437.0108 ],\n",
-              "          [324.4831 , 438.1426 ],\n",
-              "          [322.2054 , 435.06854],\n",
-              "          [246.43256, 450.61487],\n",
-              "          [242.39862, 413.8269 ],\n",
-              "          [285.56503, 460.0099 ],\n",
-              "          [273.78204, 406.4644 ],\n",
+              "          [311.6751 , 437.00995],\n",
+              "          [324.48315, 438.1421 ],\n",
+              "          [322.20544, 435.06784],\n",
+              "          [246.43257, 450.61487],\n",
+              "          [242.3986 , 413.8269 ],\n",
+              "          [285.565  , 460.00977],\n",
+              "          [273.78204, 406.46442],\n",
               "          [      nan,       nan],\n",
               "          [      nan,       nan],\n",
-              "          [242.11815, 442.0634 ],\n",
-              "          [245.55441, 421.72803]],\n",
+              "          [242.11816, 442.0634 ],\n",
+              "          [245.55441, 421.7281 ]],\n",
               " \n",
               "         [[320.03793, 435.2389 ],\n",
               "          [353.87274, 434.77695],\n",
@@ -949,33 +588,33 @@
               "          [242.26588, 441.80545],\n",
               "          [245.77664, 420.7662 ]],\n",
               " \n",
-              "         [[320.46982, 435.25452],\n",
-              "          [354.89542, 434.93198],\n",
-              "          [372.2558 , 433.46106],\n",
-              "          [394.40723, 479.57962],\n",
-              "          [400.3011 , 431.9626 ],\n",
-              "          [306.98218, 449.3156 ],\n",
+              "         [[320.46994, 435.2546 ],\n",
+              "          [354.89484, 434.93176],\n",
+              "          [372.25574, 433.46127],\n",
+              "          [394.40717, 479.5797 ],\n",
+              "          [400.30173, 431.96054],\n",
+              "          [306.9821 , 449.3157 ],\n",
               "          [308.8817 , 421.52148],\n",
-              "          [325.98843, 474.91672],\n",
+              "          [325.98843, 474.9167 ],\n",
               "          [332.17917, 385.04684],\n",
-              "          [363.03186, 473.50638],\n",
+              "          [363.0318 , 473.50616],\n",
               "          [391.05493, 396.85666],\n",
-              "          [329.1689 , 445.0495 ],\n",
-              "          [328.89993, 423.52527]]],\n",
-              " \n",
-              " \n",
-              "        [[[234.65546, 429.69464],\n",
-              "          [272.38306, 435.6884 ],\n",
-              "          [311.04346, 437.86926],\n",
-              "          [324.80878, 437.3788 ],\n",
-              "          [322.84747, 433.93933],\n",
-              "          [246.71854, 451.2873 ],\n",
-              "          [242.57391, 413.58414],\n",
-              "          [286.16397, 461.83658],\n",
-              "          [272.8733 , 406.21573],\n",
+              "          [329.16904, 445.04953],\n",
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               "          [245.25829, 420.48416]],\n",
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@@ -1054,7 +693,7 @@
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@@ -1072,11 +711,11 @@
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               "          [378.56894, 469.3632 ],\n",
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               "          [329.04425, 418.99023]]],\n",
               " \n",
               " \n",
@@ -1094,8 +733,8 @@
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               " \n",
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-              "          [358.8828 , 430.98035],\n",
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@@ -1130,7 +769,7 @@
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@@ -1138,32 +777,32 @@
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               " \n",
               " \n",
@@ -1254,22 +893,22 @@
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               " \n",
               " \n",
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-              "          [273.84183, 406.37183],\n",
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               "          [      nan,       nan],\n",
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-              "          [244.4097 , 419.95215]],\n",
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               " \n",
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               "          [389.47607, 423.60114],\n",
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@@ -1289,7 +928,7 @@
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               "          [324.71207, 434.39133],\n",
               "          [224.85786, 451.4593 ],\n",
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               "          [285.67142, 461.77646],\n",
               "          [273.7307 , 406.5118 ],\n",
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@@ -1298,7 +937,7 @@
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               " \n",
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               "          [390.49423, 423.05255],\n",
               "          [413.68115, 433.6671 ],\n",
               "          [410.5454 , 419.09042],\n",
@@ -1339,36 +978,214 @@
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+              "        [0.94746464, 0.98374254],\n",
+              "        [0.97818244, 0.98671097],\n",
+              "        [0.9833999 , 0.98425347]], dtype=float32),\n",
+              " 'n_valid': array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])}"
             ]
           },
+          "execution_count": 11,
           "metadata": {},
-          "execution_count": 11
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "imgs = video[:16]  # batch of 16 images\n",
+        "\n",
+        "predictions = predictor.inference_model.predict(imgs, numpy=True)\n",
+        "predictions"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "for key, value in predictions.items():\n",
-        "    print(f\"'{key}': {value.shape} ({value.dtype})\")"
-      ],
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1376,11 +1193,10 @@
         "id": "k4ms3mUAX_ww",
         "outputId": "4ea4fc9f-bdbc-4c2d-da9e-68cfc734f22c"
       },
-      "execution_count": 12,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "'instance_peaks': (16, 2, 13, 2) (float32)\n",
             "'instance_peak_vals': (16, 2, 13) (float32)\n",
@@ -1390,23 +1206,32 @@
             "'n_valid': (16,) (int32)\n"
           ]
         }
+      ],
+      "source": [
+        "for key, value in predictions.items():\n",
+        "    print(f\"'{key}': {value.shape} ({value.dtype})\")"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "sDKsqAEVOogD"
+      },
       "source": [
         "## 4. Realtime performance\n",
         "\n",
         "Now that we know how to do inference with different types of outputs, let's try to use that to build a simulated \"realtime\" application with timing.\n",
         "\n",
         "First, we'll create a class that simulates a camera grabber API that provides a sequence of pre-loaded frames."
-      ],
-      "metadata": {
-        "id": "sDKsqAEVOogD"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "_vKMoT_oYcgZ"
+      },
+      "outputs": [],
       "source": [
         "from time import perf_counter\n",
         "import numpy as np\n",
@@ -1431,24 +1256,37 @@
         "        idx = self.frame_counter % len(self.frames)\n",
         "        self.frame_counter += 1\n",
         "        return self.frames[idx]\n"
-      ],
-      "metadata": {
-        "id": "_vKMoT_oYcgZ"
-      },
-      "execution_count": 13,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Then, we'll define a simply acquisition loop, in which we repeatedly grab a frame and perform inference to time how long it takes."
-      ],
       "metadata": {
         "id": "3-ctjg4wkxit"
-      }
+      },
+      "source": [
+        "Then, we'll define a simply acquisition loop, in which we repeatedly grab a frame and perform inference to time how long it takes."
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ExhVDw_AaOJq",
+        "outputId": "3531b16e-4c0b-4e9f-a09c-9004105b469b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "First inference time: 886.2 ms\n",
+            "Inference times: 63.1 +- 1.2 ms\n"
+          ]
+        }
+      ],
       "source": [
         "recording_duration = 100  # session length in frames\n",
         "\n",
@@ -1476,46 +1314,20 @@
         "first_inference_time, inference_times = inference_times[0], inference_times[1:]\n",
         "print(f\"First inference time: {first_inference_time:.1f} ms\")\n",
         "print(f\"Inference times: {inference_times.mean():.1f} +- {inference_times.std():.1f} ms\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ExhVDw_AaOJq",
-        "outputId": "3531b16e-4c0b-4e9f-a09c-9004105b469b"
-      },
-      "execution_count": 14,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "First inference time: 2181.9 ms\n",
-            "Inference times: 28.8 +- 2.6 ms\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "After the first batch, our inference latencies go way down and we can see how they vary over time:"
-      ],
       "metadata": {
         "id": "WtbC0_3ek8I-"
-      }
+      },
+      "source": [
+        "After the first batch, our inference latencies go way down and we can see how they vary over time:"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "plt.figure(figsize=(10, 4), dpi=120, facecolor=\"w\")\n",
-        "plt.plot(inference_times, \".\")\n",
-        "plt.xlabel(\"Time (frames)\")\n",
-        "plt.ylabel(\"Inference latency (ms)\")\n",
-        "plt.grid(True);"
-      ],
+      "execution_count": 15,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1524,28 +1336,31 @@
         "id": "R1uQIpjma5nJ",
         "outputId": "92a06b58-9250-482a-e645-86bb4cc5647a"
       },
-      "execution_count": 15,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.figure(figsize=(10, 4), dpi=120, facecolor=\"w\")\n",
+        "plt.plot(inference_times, \".\")\n",
+        "plt.xlabel(\"Time (frames)\")\n",
+        "plt.ylabel(\"Inference latency (ms)\")\n",
+        "plt.grid(True);"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "plt.figure(figsize=(6, 4), dpi=120, facecolor=\"w\")\n",
-        "plt.hist(inference_times, bins=30)\n",
-        "plt.xlabel(\"Inference latency (ms)\")\n",
-        "plt.ylabel(\"PDF\");"
-      ],
+      "execution_count": 16,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1554,19 +1369,50 @@
         "id": "ubgokqC4ct5m",
         "outputId": "03fea67b-5c92-413f-f841-5c9464be08a6"
       },
-      "execution_count": 16,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plt.figure(figsize=(6, 4), dpi=120, facecolor=\"w\")\n",
+        "plt.hist(inference_times, bins=30)\n",
+        "plt.xlabel(\"Inference latency (ms)\")\n",
+        "plt.ylabel(\"PDF\");"
       ]
     }
-  ]
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "name": "SLEAP - Interactive and realtime inference.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
diff --git a/docs/notebooks/Interactive_and_resumable_training.ipynb b/docs/notebooks/Interactive_and_resumable_training.ipynb
index 92435724a..708d10845 100644
--- a/docs/notebooks/Interactive_and_resumable_training.ipynb
+++ b/docs/notebooks/Interactive_and_resumable_training.ipynb
@@ -1,19 +1,4 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "SLEAP - Interactive and resumable training.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "machine_shape": "hm"
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "markdown",
@@ -27,6 +12,9 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "DpvQa3M3n7jC"
+      },
       "source": [
         "# Interactive and resumable training\n",
         "\n",
@@ -35,10 +23,7 @@
         "If you'd like to customize the training process, however, you can use SLEAP's low-level training functionality interactively. This allows you to define scripts that train models according to your own workflow, for example, to **resume training** on an already trained model. Another possible application would be to train a model using **transfer learning**, where a pretrained model can be used to initialize the weights of the new model.\n",
         "\n",
         "In this notebook we will explore how to set up a training job and train a model for multiple rounds without the GUI or CLI."
-      ],
-      "metadata": {
-        "id": "DpvQa3M3n7jC"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -55,196 +40,47 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 4,
       "metadata": {
-        "id": "BYxJ2rJOMW8B",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "BYxJ2rJOMW8B",
         "outputId": "d2230650-4e45-46f3-ff8f-dbe271bb9eb9"
       },
-      "source": [
-        "# This should take care of all the dependencies on colab:\n",
-        "!pip uninstall -y opencv-python opencv-contrib-python && pip install sleap\n",
-        "\n",
-        "\n",
-        "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n",
-        "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap"
-      ],
-      "execution_count": 1,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "Found existing installation: opencv-python 4.1.2.30\n",
-            "Uninstalling opencv-python-4.1.2.30:\n",
-            "  Successfully uninstalled opencv-python-4.1.2.30\n",
-            "Found existing installation: opencv-contrib-python 4.1.2.30\n",
-            "Uninstalling opencv-contrib-python-4.1.2.30:\n",
-            "  Successfully uninstalled opencv-contrib-python-4.1.2.30\n",
-            "Collecting sleap\n",
-            "  Downloading sleap-1.2.2-py3-none-any.whl (62.0 MB)\n",
-            "\u001b[K     |████████████████████████████████| 62.0 MB 1.3 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: certifi<=2021.10.8,>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from sleap) (2021.10.8)\n",
-            "Requirement already satisfied: tensorflow<2.9.0,>=2.6.3 in /usr/local/lib/python3.7/dist-packages (from sleap) (2.8.0)\n",
-            "Requirement already satisfied: pyzmq in /usr/local/lib/python3.7/dist-packages (from sleap) (22.3.0)\n",
-            "Collecting jsonpickle==1.2\n",
-            "  Downloading jsonpickle-1.2-py2.py3-none-any.whl (32 kB)\n",
-            "Requirement already satisfied: scikit-learn==1.0.* in /usr/local/lib/python3.7/dist-packages (from sleap) (1.0.2)\n",
-            "Collecting opencv-python-headless<=4.5.5.62,>=4.2.0.34\n",
-            "  Downloading opencv_python_headless-4.5.5.62-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (47.7 MB)\n",
-            "\u001b[K     |████████████████████████████████| 47.7 MB 85 kB/s \n",
-            "\u001b[?25hCollecting rich==10.16.1\n",
-            "  Downloading rich-10.16.1-py3-none-any.whl (214 kB)\n",
-            "\u001b[K     |████████████████████████████████| 214 kB 49.1 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: numpy<=1.21.5,>=1.19.5 in /usr/local/lib/python3.7/dist-packages (from sleap) (1.21.5)\n",
-            "Requirement already satisfied: imageio<=2.15.0 in /usr/local/lib/python3.7/dist-packages (from sleap) (2.4.1)\n",
-            "Collecting scikit-video\n",
-            "  Downloading scikit_video-1.1.11-py2.py3-none-any.whl (2.3 MB)\n",
-            "\u001b[K     |████████████████████████████████| 2.3 MB 38.3 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: h5py<=3.6.0,>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from sleap) (3.1.0)\n",
-            "Collecting cattrs==1.1.1\n",
-            "  Downloading cattrs-1.1.1-py3-none-any.whl (16 kB)\n",
-            "Collecting python-rapidjson\n",
-            "  Downloading python_rapidjson-1.6-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)\n",
-            "\u001b[K     |████████████████████████████████| 1.6 MB 39.3 MB/s \n",
-            "\u001b[?25hCollecting imgaug==0.4.0\n",
-            "  Downloading imgaug-0.4.0-py2.py3-none-any.whl (948 kB)\n",
-            "\u001b[K     |████████████████████████████████| 948 kB 40.4 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from sleap) (2.6.3)\n",
-            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from sleap) (0.11.2)\n",
-            "Collecting pykalman==0.9.5\n",
-            "  Downloading pykalman-0.9.5.tar.gz (228 kB)\n",
-            "\u001b[K     |████████████████████████████████| 228 kB 52.8 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from sleap) (3.13)\n",
-            "Collecting PySide2<=5.14.1,>=5.13.2\n",
-            "  Downloading PySide2-5.14.1-5.14.1-cp35.cp36.cp37.cp38-abi3-manylinux1_x86_64.whl (165.5 MB)\n",
-            "\u001b[K     |████████████████████████████████| 165.5 MB 76 kB/s \n",
-            "\u001b[?25hCollecting attrs==21.2.0\n",
-            "  Downloading attrs-21.2.0-py2.py3-none-any.whl (53 kB)\n",
-            "\u001b[K     |████████████████████████████████| 53 kB 2.3 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from sleap) (5.4.8)\n",
-            "Collecting imgstore==0.2.9\n",
-            "  Downloading imgstore-0.2.9-py2.py3-none-any.whl (904 kB)\n",
-            "\u001b[K     |████████████████████████████████| 904 kB 39.4 MB/s \n",
-            "\u001b[?25hCollecting jsmin\n",
-            "  Downloading jsmin-3.0.1.tar.gz (13 kB)\n",
-            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from sleap) (1.3.5)\n",
-            "Collecting segmentation-models==1.0.1\n",
-            "  Downloading segmentation_models-1.0.1-py3-none-any.whl (33 kB)\n",
-            "Requirement already satisfied: scipy<=1.7.3,>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from sleap) (1.4.1)\n",
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-            "Collecting qimage2ndarray<=1.8.3,>=1.8.2\n",
-            "  Downloading qimage2ndarray-1.8.3-py3-none-any.whl (11 kB)\n",
-            "Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug==0.4.0->sleap) (1.8.1.post1)\n",
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-            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug==0.4.0->sleap) (3.2.2)\n",
-            "Collecting opencv-python\n",
-            "  Downloading opencv_python-4.5.5.64-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (60.5 MB)\n",
-            "\u001b[K     |████████████████████████████████| 60.5 MB 1.4 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug==0.4.0->sleap) (1.15.0)\n",
-            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from imgstore==0.2.9->sleap) (2018.9)\n",
-            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from imgstore==0.2.9->sleap) (2.8.2)\n",
-            "Requirement already satisfied: tzlocal in /usr/local/lib/python3.7/dist-packages (from imgstore==0.2.9->sleap) (1.5.1)\n",
-            "Collecting colorama<0.5.0,>=0.4.0\n",
-            "  Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n",
-            "Requirement already satisfied: typing-extensions<5.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from rich==10.16.1->sleap) (3.10.0.2)\n",
-            "Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich==10.16.1->sleap) (2.6.1)\n",
-            "Collecting commonmark<0.10.0,>=0.9.0\n",
-            "  Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)\n",
-            "\u001b[K     |████████████████████████████████| 51 kB 6.5 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==1.0.*->sleap) (1.1.0)\n",
-            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==1.0.*->sleap) (3.1.0)\n",
-            "Collecting keras-applications<=1.0.8,>=1.0.7\n",
-            "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
-            "\u001b[K     |████████████████████████████████| 50 kB 6.0 MB/s \n",
-            "\u001b[?25hCollecting image-classifiers==1.0.0\n",
-            "  Downloading image_classifiers-1.0.0-py3-none-any.whl (19 kB)\n",
-            "Collecting efficientnet==1.0.0\n",
-            "  Downloading efficientnet-1.0.0-py3-none-any.whl (17 kB)\n",
-            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py<=3.6.0,>=3.1.0->sleap) (1.5.2)\n",
-            "Collecting shiboken2==5.14.1\n",
-            "  Downloading shiboken2-5.14.1-5.14.1-cp35.cp36.cp37.cp38-abi3-manylinux1_x86_64.whl (847 kB)\n",
-            "\u001b[K     |████████████████████████████████| 847 kB 39.4 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image->sleap) (2021.11.2)\n",
-            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image->sleap) (1.3.0)\n",
-            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug==0.4.0->sleap) (0.11.0)\n",
-            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug==0.4.0->sleap) (1.4.0)\n",
-            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug==0.4.0->sleap) (3.0.7)\n",
-            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (0.24.0)\n",
-            "Requirement already satisfied: flatbuffers>=1.12 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (2.0)\n",
-            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (1.6.3)\n",
-            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (3.3.0)\n",
-            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (3.17.3)\n",
-            "Collecting tf-estimator-nightly==2.8.0.dev2021122109\n",
-            "  Downloading tf_estimator_nightly-2.8.0.dev2021122109-py2.py3-none-any.whl (462 kB)\n",
-            "\u001b[K     |████████████████████████████████| 462 kB 48.3 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (0.2.0)\n",
-            "Requirement already satisfied: tensorboard<2.9,>=2.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (2.8.0)\n",
-            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (1.14.0)\n",
-            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (57.4.0)\n",
-            "Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (1.1.2)\n",
-            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (1.1.0)\n",
-            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (1.44.0)\n",
-            "Requirement already satisfied: gast>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (0.5.3)\n",
-            "Requirement already satisfied: libclang>=9.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (13.0.0)\n",
-            "Requirement already satisfied: absl-py>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (1.0.0)\n",
-            "Requirement already satisfied: keras<2.9,>=2.8.0rc0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (2.8.0)\n",
-            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from astunparse>=1.6.0->tensorflow<2.9.0,>=2.6.3->sleap) (0.37.1)\n",
-            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.3.6)\n",
-            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.8.1)\n",
-            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.0.1)\n",
-            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (2.23.0)\n",
-            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.35.0)\n",
-            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.6.1)\n",
-            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.4.6)\n",
-            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.2.8)\n",
-            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (4.2.4)\n",
-            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (4.8)\n",
-            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.3.1)\n",
-            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (4.11.3)\n",
-            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.7.0)\n",
-            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.4.8)\n",
-            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.0.4)\n",
-            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.24.3)\n",
-            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (2.10)\n",
-            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.2.0)\n",
-            "Building wheels for collected packages: pykalman, jsmin\n",
-            "  Building wheel for pykalman (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for pykalman: filename=pykalman-0.9.5-py3-none-any.whl size=48462 sha256=dde739150408cee5e4cb98680575a79e9cf2574d606fea22d81dac69689e1b5f\n",
-            "  Stored in directory: /root/.cache/pip/wheels/6a/04/02/2dda6ea59c66d9e685affc8af3a31ad3a5d87b7311689efce6\n",
-            "  Building wheel for jsmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for jsmin: filename=jsmin-3.0.1-py3-none-any.whl size=13782 sha256=28e30a78deeb41cb8a5a2a452ecd4209438e26a6f74af8de2e29a7da35b6fe93\n",
-            "  Stored in directory: /root/.cache/pip/wheels/a4/0b/64/fb4f87526ecbdf7921769a39d91dcfe4860e621cf15b8250d6\n",
-            "Successfully built pykalman jsmin\n",
-            "Installing collected packages: keras-applications, tf-estimator-nightly, shiboken2, opencv-python, image-classifiers, efficientnet, commonmark, colorama, attrs, segmentation-models, scikit-video, rich, qimage2ndarray, python-rapidjson, PySide2, pykalman, opencv-python-headless, jsonpickle, jsmin, imgstore, imgaug, cattrs, sleap\n",
-            "  Attempting uninstall: attrs\n",
-            "    Found existing installation: attrs 21.4.0\n",
-            "    Uninstalling attrs-21.4.0:\n",
-            "      Successfully uninstalled attrs-21.4.0\n",
-            "  Attempting uninstall: imgaug\n",
-            "    Found existing installation: imgaug 0.2.9\n",
-            "    Uninstalling imgaug-0.2.9:\n",
-            "      Successfully uninstalled imgaug-0.2.9\n",
-            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
-            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n",
-            "Successfully installed PySide2-5.14.1 attrs-21.2.0 cattrs-1.1.1 colorama-0.4.4 commonmark-0.9.1 efficientnet-1.0.0 image-classifiers-1.0.0 imgaug-0.4.0 imgstore-0.2.9 jsmin-3.0.1 jsonpickle-1.2 keras-applications-1.0.8 opencv-python-4.5.5.64 opencv-python-headless-4.5.5.62 pykalman-0.9.5 python-rapidjson-1.6 qimage2ndarray-1.8.3 rich-10.16.1 scikit-video-1.1.11 segmentation-models-1.0.1 shiboken2-5.14.1 sleap-1.2.2 tf-estimator-nightly-2.8.0.dev2021122109\n"
+            "\u001b[31mERROR: Cannot uninstall opencv-python 4.6.0, RECORD file not found. Hint: The package was installed by conda.\u001b[0m\u001b[31m\n",
+            "\u001b[0m\u001b[31mERROR: Cannot uninstall shiboken2 5.15.6, RECORD file not found. You might be able to recover from this via: 'pip install --force-reinstall --no-deps shiboken2==5.15.6'.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
           ]
         }
+      ],
+      "source": [
+        "# This should take care of all the dependencies on colab:\n",
+        "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n",
+        "!pip install -qqq sleap[pypi]\n",
+        "\n",
+        "\n",
+        "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n",
+        "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Import SLEAP to make sure it installed correctly and print out some information about the system:"
-      ],
       "metadata": {
         "id": "qjfoeOZvpV8o"
-      }
+      },
+      "source": [
+        "Import SLEAP to make sure it installed correctly and print out some information about the system:"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -252,23 +88,16 @@
         "id": "jftAOyvvuQeh",
         "outputId": "f62974d2-51e7-47d8-defb-ab6f970c995f"
       },
-      "source": [
-        "import sleap\n",
-        "sleap.versions()\n",
-        "sleap.system_summary()"
-      ],
-      "execution_count": 2,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
-            "SLEAP: 1.2.2\n",
-            "TensorFlow: 2.8.0\n",
+            "SLEAP: 1.3.2\n",
+            "TensorFlow: 2.7.0\n",
             "Numpy: 1.21.5\n",
-            "Python: 3.7.13\n",
-            "OS: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\n",
+            "Python: 3.7.12\n",
+            "OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n",
             "GPUs: 1/1 available\n",
             "  Device: /physical_device:GPU:0\n",
             "         Available: True\n",
@@ -276,6 +105,11 @@
             "     Memory growth: None\n"
           ]
         }
+      ],
+      "source": [
+        "import sleap\n",
+        "sleap.versions()\n",
+        "sleap.system_summary()"
       ]
     },
     {
@@ -293,47 +127,55 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 6,
       "metadata": {
-        "id": "sDIF3RKdM86u",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "sDIF3RKdM86u",
         "outputId": "9c267834-935c-4f90-bb77-c0f15814ba2a"
       },
-      "source": [
-        "# !curl -L --output labels.pkg.slp https://www.dropbox.com/s/b990gxjt3d3j3jh/210205.sleap_wt_gold.13pt.pkg.slp?dl=1\n",
-        "!curl -L --output labels.pkg.slp https://storage.googleapis.com/sleap-data/datasets/wt_gold.13pt/tracking_split2/train.pkg.slp\n",
-        "!ls -lah"
-      ],
-      "execution_count": 3,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
             "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-            "100  619M  100  619M    0     0   106M      0  0:00:05  0:00:05 --:--:--  110M\n",
-            "total 620M\n",
-            "drwxr-xr-x 1 root root 4.0K Apr  3 23:48 .\n",
-            "drwxr-xr-x 1 root root 4.0K Apr  3 23:40 ..\n",
-            "drwxr-xr-x 4 root root 4.0K Mar 23 14:21 .config\n",
-            "-rw-r--r-- 1 root root 620M Apr  3 23:48 labels.pkg.slp\n",
-            "drwxr-xr-x 1 root root 4.0K Mar 23 14:22 sample_data\n"
+            "100  619M  100  619M    0     0  32.9M      0  0:00:18  0:00:18 --:--:-- 34.4M\n",
+            "total 622M\n",
+            "drwxrwxr-x  3 talmolab talmolab 4.0K Sep  1 14:23 .\n",
+            "drwxrwxr-x 10 talmolab talmolab 4.0K Aug 31 15:43 ..\n",
+            "drwxrwxr-x  2 talmolab talmolab 4.0K Jun 20 10:00 analysis_example\n",
+            "-rw-rw-r--  1 talmolab talmolab 713K Jun 20 10:00 Analysis_examples.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab 481K Sep  1 14:02 Data_structures.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab 4.1K Jun 20 10:00 index.rst\n",
+            "-rw-rw-r--  1 talmolab talmolab 179K Sep  1 13:58 Interactive_and_realtime_inference.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab 120K Sep  1 14:21 Interactive_and_resumable_training.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab 620M Sep  1 14:24 labels.pkg.slp\n",
+            "-rw-rw-r--  1 talmolab talmolab 157K Sep  1 14:15 Model_evaluation.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab 132K Sep  1 14:18 Post_inference_tracking.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab  94K Sep  1 13:44 Training_and_inference_on_an_example_dataset.ipynb\n",
+            "-rw-rw-r--  1 talmolab talmolab  12K Aug 31 11:39 Training_and_inference_using_Google_Drive.ipynb\n"
           ]
         }
+      ],
+      "source": [
+        "# !curl -L --output labels.pkg.slp https://www.dropbox.com/s/b990gxjt3d3j3jh/210205.sleap_wt_gold.13pt.pkg.slp?dl=1\n",
+        "!curl -L --output labels.pkg.slp https://storage.googleapis.com/sleap-data/datasets/wt_gold.13pt/tracking_split2/train.pkg.slp\n",
+        "!ls -lah"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "TRAINING_SLP_FILE = \"labels.pkg.slp\""
-      ],
+      "execution_count": 7,
       "metadata": {
         "id": "vbpBugZRp_S7"
       },
-      "execution_count": 4,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "TRAINING_SLP_FILE = \"labels.pkg.slp\""
+      ]
     },
     {
       "cell_type": "markdown",
@@ -350,9 +192,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 8,
       "metadata": {
         "id": "Cqt1Bhp-OIsi"
       },
+      "outputs": [],
       "source": [
         "from sleap.nn.config import *\n",
         "\n",
@@ -381,9 +225,7 @@
         "\n",
         "# Setup how we want to save the trained model.\n",
         "cfg.outputs.run_name = \"baseline_model.topdown\""
-      ],
-      "execution_count": 5,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -410,6 +252,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -417,20 +260,19 @@
         "id": "enbK9O5Dv8Pd",
         "outputId": "0e36a6e2-a7e8-4d0f-e1d3-0d1b7abaf490"
       },
-      "source": [
-        "trainer = sleap.nn.training.Trainer.from_config(cfg)"
-      ],
-      "execution_count": 6,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO:sleap.nn.training:Loading training labels from: labels.pkg.slp\n",
             "INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1\n",
             "INFO:sleap.nn.training:  Splits: Training = 1440 / Validation = 160.\n"
           ]
         }
+      ],
+      "source": [
+        "trainer = sleap.nn.training.Trainer.from_config(cfg)"
       ]
     },
     {
@@ -444,6 +286,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -458,20 +301,37 @@
         "id": "L8jNydTEwNA1",
         "outputId": "51828b8c-6d8b-4743-e9d2-9153f5b571c3"
       },
-      "source": [
-        "trainer.train()"
-      ],
-      "execution_count": 7,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO:sleap.nn.training:Setting up for training...\n",
             "INFO:sleap.nn.training:Setting up pipeline builders...\n",
             "INFO:sleap.nn.training:Setting up model...\n",
-            "INFO:sleap.nn.training:Building test pipeline...\n",
-            "INFO:sleap.nn.training:Loaded test example. [6.047s]\n",
+            "INFO:sleap.nn.training:Building test pipeline...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2023-09-01 14:24:11.775633: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
+            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+            "2023-09-01 14:24:11.776555: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 14:24:11.777493: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 14:24:11.778196: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 14:24:12.055738: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 14:24:12.056597: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 14:24:12.057389: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 14:24:12.058046: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21261 MB memory:  -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "INFO:sleap.nn.training:Loaded test example. [1.799s]\n",
             "INFO:sleap.nn.training:  Input shape: (160, 160, 1)\n",
             "INFO:sleap.nn.training:Created Keras model.\n",
             "INFO:sleap.nn.training:  Backbone: UNet(stacks=1, filters=16, filters_rate=2, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=4, middle_block=True, up_blocks=2, up_interpolate=False, block_contraction=False)\n",
@@ -481,6 +341,7 @@
             "INFO:sleap.nn.training:    [0] = CenteredInstanceConfmapsHead(part_names=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], anchor_part='thorax', sigma=1.5, output_stride=4, loss_weight=1.0)\n",
             "INFO:sleap.nn.training:  Outputs: \n",
             "INFO:sleap.nn.training:    [0] = KerasTensor(type_spec=TensorSpec(shape=(None, 40, 40, 13), dtype=tf.float32, name=None), name='CenteredInstanceConfmapsHead/BiasAdd:0', description=\"created by layer 'CenteredInstanceConfmapsHead'\")\n",
+            "INFO:sleap.nn.training:Training from scratch\n",
             "INFO:sleap.nn.training:Setting up data pipelines...\n",
             "INFO:sleap.nn.training:Training set: n = 1440\n",
             "INFO:sleap.nn.training:Validation set: n = 160\n",
@@ -490,132 +351,144 @@
             "INFO:sleap.nn.training:Setting up outputs...\n",
             "INFO:sleap.nn.training:Created run path: models/baseline_model.topdown\n",
             "INFO:sleap.nn.training:Setting up visualization...\n",
-            "Unable to use Qt backend for matplotlib. This probably means Qt is running headless.\n",
-            "INFO:sleap.nn.training:Finished trainer set up. [10.4s]\n",
+            "INFO:sleap.nn.training:Finished trainer set up. [3.3s]\n",
             "INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation...\n",
-            "INFO:sleap.nn.training:Finished creating training datasets. [29.5s]\n",
+            "INFO:sleap.nn.training:Finished creating training datasets. [16.2s]\n",
             "INFO:sleap.nn.training:Starting training loop...\n",
-            "Epoch 1/10\n",
-            "360/360 - 70s - loss: 0.0037 - head: 0.0029 - thorax: 0.0030 - abdomen: 0.0037 - wingL: 0.0041 - wingR: 0.0041 - forelegL4: 0.0037 - forelegR4: 0.0038 - midlegL4: 0.0041 - midlegR4: 0.0041 - hindlegL4: 0.0039 - hindlegR4: 0.0040 - eyeL: 0.0033 - eyeR: 0.0034 - val_loss: 0.0033 - val_head: 0.0017 - val_thorax: 0.0025 - val_abdomen: 0.0035 - val_wingL: 0.0039 - val_wingR: 0.0039 - val_forelegL4: 0.0033 - val_forelegR4: 0.0036 - val_midlegL4: 0.0040 - val_midlegR4: 0.0040 - val_hindlegL4: 0.0040 - val_hindlegR4: 0.0040 - val_eyeL: 0.0022 - val_eyeR: 0.0023 - lr: 1.0000e-04 - 70s/epoch - 194ms/step\n",
+            "Epoch 1/10\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2023-09-01 14:24:32.586040: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n",
+            "2023-09-01 14:24:42.104556: I tensorflow/stream_executor/cuda/cuda_blas.cc:1774] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "360/360 - 12s - loss: 0.0037 - head: 0.0030 - thorax: 0.0030 - abdomen: 0.0036 - wingL: 0.0040 - wingR: 0.0040 - forelegL4: 0.0037 - forelegR4: 0.0038 - midlegL4: 0.0041 - midlegR4: 0.0041 - hindlegL4: 0.0039 - hindlegR4: 0.0040 - eyeL: 0.0035 - eyeR: 0.0035 - val_loss: 0.0033 - val_head: 0.0020 - val_thorax: 0.0029 - val_abdomen: 0.0030 - val_wingL: 0.0033 - val_wingR: 0.0034 - val_forelegL4: 0.0037 - val_forelegR4: 0.0036 - val_midlegL4: 0.0039 - val_midlegR4: 0.0039 - val_hindlegL4: 0.0037 - val_hindlegR4: 0.0038 - val_eyeL: 0.0029 - val_eyeR: 0.0027 - lr: 1.0000e-04 - 12s/epoch - 32ms/step\n",
             "Epoch 2/10\n",
-            "360/360 - 53s - loss: 0.0028 - head: 0.0013 - thorax: 0.0020 - abdomen: 0.0028 - wingL: 0.0031 - wingR: 0.0031 - forelegL4: 0.0032 - forelegR4: 0.0033 - midlegL4: 0.0039 - midlegR4: 0.0039 - hindlegL4: 0.0037 - hindlegR4: 0.0038 - eyeL: 0.0013 - eyeR: 0.0014 - val_loss: 0.0025 - val_head: 9.5906e-04 - val_thorax: 0.0013 - val_abdomen: 0.0023 - val_wingL: 0.0025 - val_wingR: 0.0025 - val_forelegL4: 0.0029 - val_forelegR4: 0.0030 - val_midlegL4: 0.0037 - val_midlegR4: 0.0038 - val_hindlegL4: 0.0037 - val_hindlegR4: 0.0038 - val_eyeL: 8.8668e-04 - val_eyeR: 9.7728e-04 - lr: 1.0000e-04 - 53s/epoch - 148ms/step\n",
+            "360/360 - 7s - loss: 0.0028 - head: 0.0013 - thorax: 0.0018 - abdomen: 0.0026 - wingL: 0.0027 - wingR: 0.0028 - forelegL4: 0.0032 - forelegR4: 0.0033 - midlegL4: 0.0038 - midlegR4: 0.0038 - hindlegL4: 0.0037 - hindlegR4: 0.0038 - eyeL: 0.0015 - eyeR: 0.0015 - val_loss: 0.0025 - val_head: 9.7323e-04 - val_thorax: 0.0011 - val_abdomen: 0.0026 - val_wingL: 0.0024 - val_wingR: 0.0026 - val_forelegL4: 0.0030 - val_forelegR4: 0.0030 - val_midlegL4: 0.0036 - val_midlegR4: 0.0037 - val_hindlegL4: 0.0038 - val_hindlegR4: 0.0037 - val_eyeL: 0.0012 - val_eyeR: 0.0012 - lr: 1.0000e-04 - 7s/epoch - 21ms/step\n",
             "Epoch 3/10\n",
-            "360/360 - 55s - loss: 0.0023 - head: 8.0222e-04 - thorax: 9.4507e-04 - abdomen: 0.0022 - wingL: 0.0022 - wingR: 0.0022 - forelegL4: 0.0027 - forelegR4: 0.0028 - midlegL4: 0.0035 - midlegR4: 0.0036 - hindlegL4: 0.0034 - hindlegR4: 0.0036 - eyeL: 8.5909e-04 - eyeR: 8.8003e-04 - val_loss: 0.0021 - val_head: 7.4704e-04 - val_thorax: 6.8354e-04 - val_abdomen: 0.0020 - val_wingL: 0.0018 - val_wingR: 0.0019 - val_forelegL4: 0.0024 - val_forelegR4: 0.0025 - val_midlegL4: 0.0031 - val_midlegR4: 0.0034 - val_hindlegL4: 0.0032 - val_hindlegR4: 0.0035 - val_eyeL: 7.6220e-04 - val_eyeR: 7.1808e-04 - lr: 1.0000e-04 - 55s/epoch - 154ms/step\n",
+            "360/360 - 7s - loss: 0.0022 - head: 8.0630e-04 - thorax: 6.7199e-04 - abdomen: 0.0022 - wingL: 0.0020 - wingR: 0.0021 - forelegL4: 0.0027 - forelegR4: 0.0027 - midlegL4: 0.0033 - midlegR4: 0.0035 - hindlegL4: 0.0034 - hindlegR4: 0.0035 - eyeL: 8.7345e-04 - eyeR: 8.4145e-04 - val_loss: 0.0020 - val_head: 8.6439e-04 - val_thorax: 5.9914e-04 - val_abdomen: 0.0020 - val_wingL: 0.0019 - val_wingR: 0.0020 - val_forelegL4: 0.0025 - val_forelegR4: 0.0024 - val_midlegL4: 0.0030 - val_midlegR4: 0.0031 - val_hindlegL4: 0.0030 - val_hindlegR4: 0.0031 - val_eyeL: 8.9466e-04 - val_eyeR: 9.5174e-04 - lr: 1.0000e-04 - 7s/epoch - 20ms/step\n",
             "Epoch 4/10\n",
-            "360/360 - 61s - loss: 0.0019 - head: 6.5537e-04 - thorax: 5.3996e-04 - abdomen: 0.0019 - wingL: 0.0018 - wingR: 0.0018 - forelegL4: 0.0023 - forelegR4: 0.0024 - midlegL4: 0.0027 - midlegR4: 0.0029 - hindlegL4: 0.0029 - hindlegR4: 0.0032 - eyeL: 7.4337e-04 - eyeR: 7.2396e-04 - val_loss: 0.0017 - val_head: 5.5193e-04 - val_thorax: 3.6303e-04 - val_abdomen: 0.0018 - val_wingL: 0.0016 - val_wingR: 0.0016 - val_forelegL4: 0.0020 - val_forelegR4: 0.0020 - val_midlegL4: 0.0023 - val_midlegR4: 0.0026 - val_hindlegL4: 0.0027 - val_hindlegR4: 0.0031 - val_eyeL: 6.5068e-04 - val_eyeR: 6.0169e-04 - lr: 1.0000e-04 - 61s/epoch - 169ms/step\n",
+            "360/360 - 7s - loss: 0.0018 - head: 6.7854e-04 - thorax: 4.6945e-04 - abdomen: 0.0020 - wingL: 0.0017 - wingR: 0.0018 - forelegL4: 0.0023 - forelegR4: 0.0023 - midlegL4: 0.0026 - midlegR4: 0.0027 - hindlegL4: 0.0028 - hindlegR4: 0.0029 - eyeL: 7.4546e-04 - eyeR: 6.9585e-04 - val_loss: 0.0018 - val_head: 7.7640e-04 - val_thorax: 5.3180e-04 - val_abdomen: 0.0020 - val_wingL: 0.0018 - val_wingR: 0.0018 - val_forelegL4: 0.0022 - val_forelegR4: 0.0022 - val_midlegL4: 0.0024 - val_midlegR4: 0.0025 - val_hindlegL4: 0.0026 - val_hindlegR4: 0.0026 - val_eyeL: 9.2650e-04 - val_eyeR: 9.0064e-04 - lr: 1.0000e-04 - 7s/epoch - 20ms/step\n",
             "Epoch 5/10\n",
-            "360/360 - 57s - loss: 0.0016 - head: 5.6982e-04 - thorax: 4.1064e-04 - abdomen: 0.0017 - wingL: 0.0016 - wingR: 0.0016 - forelegL4: 0.0020 - forelegR4: 0.0020 - midlegL4: 0.0021 - midlegR4: 0.0022 - hindlegL4: 0.0024 - hindlegR4: 0.0028 - eyeL: 6.5447e-04 - eyeR: 6.3768e-04 - val_loss: 0.0014 - val_head: 4.9811e-04 - val_thorax: 3.0411e-04 - val_abdomen: 0.0015 - val_wingL: 0.0014 - val_wingR: 0.0014 - val_forelegL4: 0.0017 - val_forelegR4: 0.0019 - val_midlegL4: 0.0018 - val_midlegR4: 0.0020 - val_hindlegL4: 0.0023 - val_hindlegR4: 0.0026 - val_eyeL: 5.9634e-04 - val_eyeR: 5.8405e-04 - lr: 1.0000e-04 - 57s/epoch - 157ms/step\n",
+            "360/360 - 7s - loss: 0.0015 - head: 5.8714e-04 - thorax: 4.0531e-04 - abdomen: 0.0017 - wingL: 0.0015 - wingR: 0.0015 - forelegL4: 0.0020 - forelegR4: 0.0019 - midlegL4: 0.0020 - midlegR4: 0.0021 - hindlegL4: 0.0023 - hindlegR4: 0.0024 - eyeL: 6.7827e-04 - eyeR: 6.2254e-04 - val_loss: 0.0015 - val_head: 6.5523e-04 - val_thorax: 4.4019e-04 - val_abdomen: 0.0016 - val_wingL: 0.0016 - val_wingR: 0.0015 - val_forelegL4: 0.0019 - val_forelegR4: 0.0020 - val_midlegL4: 0.0021 - val_midlegR4: 0.0020 - val_hindlegL4: 0.0021 - val_hindlegR4: 0.0021 - val_eyeL: 7.9871e-04 - val_eyeR: 7.8608e-04 - lr: 1.0000e-04 - 7s/epoch - 20ms/step\n",
             "Epoch 6/10\n",
-            "360/360 - 54s - loss: 0.0014 - head: 5.1206e-04 - thorax: 3.4952e-04 - abdomen: 0.0015 - wingL: 0.0014 - wingR: 0.0014 - forelegL4: 0.0017 - forelegR4: 0.0018 - midlegL4: 0.0017 - midlegR4: 0.0018 - hindlegL4: 0.0020 - hindlegR4: 0.0023 - eyeL: 6.0045e-04 - eyeR: 5.7847e-04 - val_loss: 0.0012 - val_head: 4.3860e-04 - val_thorax: 2.5352e-04 - val_abdomen: 0.0014 - val_wingL: 0.0013 - val_wingR: 0.0012 - val_forelegL4: 0.0015 - val_forelegR4: 0.0016 - val_midlegL4: 0.0014 - val_midlegR4: 0.0017 - val_hindlegL4: 0.0020 - val_hindlegR4: 0.0022 - val_eyeL: 5.1261e-04 - val_eyeR: 5.5203e-04 - lr: 1.0000e-04 - 54s/epoch - 151ms/step\n",
+            "360/360 - 7s - loss: 0.0013 - head: 5.3215e-04 - thorax: 3.5232e-04 - abdomen: 0.0016 - wingL: 0.0014 - wingR: 0.0014 - forelegL4: 0.0017 - forelegR4: 0.0018 - midlegL4: 0.0017 - midlegR4: 0.0018 - hindlegL4: 0.0020 - hindlegR4: 0.0021 - eyeL: 5.9826e-04 - eyeR: 5.6906e-04 - val_loss: 0.0013 - val_head: 5.3776e-04 - val_thorax: 3.7946e-04 - val_abdomen: 0.0014 - val_wingL: 0.0014 - val_wingR: 0.0013 - val_forelegL4: 0.0017 - val_forelegR4: 0.0018 - val_midlegL4: 0.0016 - val_midlegR4: 0.0017 - val_hindlegL4: 0.0017 - val_hindlegR4: 0.0018 - val_eyeL: 6.6378e-04 - val_eyeR: 6.5611e-04 - lr: 1.0000e-04 - 7s/epoch - 19ms/step\n",
             "Epoch 7/10\n",
-            "360/360 - 54s - loss: 0.0012 - head: 4.7131e-04 - thorax: 3.1231e-04 - abdomen: 0.0014 - wingL: 0.0012 - wingR: 0.0012 - forelegL4: 0.0016 - forelegR4: 0.0016 - midlegL4: 0.0015 - midlegR4: 0.0016 - hindlegL4: 0.0018 - hindlegR4: 0.0020 - eyeL: 5.7016e-04 - eyeR: 5.4539e-04 - val_loss: 0.0011 - val_head: 4.3133e-04 - val_thorax: 2.2694e-04 - val_abdomen: 0.0013 - val_wingL: 0.0011 - val_wingR: 0.0011 - val_forelegL4: 0.0014 - val_forelegR4: 0.0015 - val_midlegL4: 0.0013 - val_midlegR4: 0.0015 - val_hindlegL4: 0.0018 - val_hindlegR4: 0.0020 - val_eyeL: 5.5373e-04 - val_eyeR: 5.0355e-04 - lr: 1.0000e-04 - 54s/epoch - 149ms/step\n",
+            "360/360 - 7s - loss: 0.0012 - head: 4.8557e-04 - thorax: 3.1089e-04 - abdomen: 0.0014 - wingL: 0.0012 - wingR: 0.0012 - forelegL4: 0.0016 - forelegR4: 0.0016 - midlegL4: 0.0015 - midlegR4: 0.0016 - hindlegL4: 0.0018 - hindlegR4: 0.0019 - eyeL: 5.6096e-04 - eyeR: 5.3123e-04 - val_loss: 0.0012 - val_head: 5.2092e-04 - val_thorax: 3.4376e-04 - val_abdomen: 0.0014 - val_wingL: 0.0012 - val_wingR: 0.0012 - val_forelegL4: 0.0015 - val_forelegR4: 0.0017 - val_midlegL4: 0.0015 - val_midlegR4: 0.0015 - val_hindlegL4: 0.0017 - val_hindlegR4: 0.0017 - val_eyeL: 6.4288e-04 - val_eyeR: 6.0581e-04 - lr: 1.0000e-04 - 7s/epoch - 19ms/step\n",
             "Epoch 8/10\n",
-            "360/360 - 53s - loss: 0.0011 - head: 4.3369e-04 - thorax: 2.6750e-04 - abdomen: 0.0013 - wingL: 0.0011 - wingR: 0.0011 - forelegL4: 0.0015 - forelegR4: 0.0015 - midlegL4: 0.0014 - midlegR4: 0.0014 - hindlegL4: 0.0017 - hindlegR4: 0.0018 - eyeL: 5.2745e-04 - eyeR: 5.0480e-04 - val_loss: 0.0011 - val_head: 4.1774e-04 - val_thorax: 2.4407e-04 - val_abdomen: 0.0013 - val_wingL: 0.0011 - val_wingR: 0.0010 - val_forelegL4: 0.0013 - val_forelegR4: 0.0014 - val_midlegL4: 0.0012 - val_midlegR4: 0.0014 - val_hindlegL4: 0.0017 - val_hindlegR4: 0.0018 - val_eyeL: 6.2877e-04 - val_eyeR: 5.7243e-04 - lr: 1.0000e-04 - 53s/epoch - 148ms/step\n",
+            "360/360 - 7s - loss: 0.0011 - head: 4.3752e-04 - thorax: 2.7513e-04 - abdomen: 0.0013 - wingL: 0.0011 - wingR: 0.0011 - forelegL4: 0.0015 - forelegR4: 0.0015 - midlegL4: 0.0014 - midlegR4: 0.0014 - hindlegL4: 0.0017 - hindlegR4: 0.0017 - eyeL: 5.1807e-04 - eyeR: 4.9554e-04 - val_loss: 0.0011 - val_head: 5.6743e-04 - val_thorax: 3.5883e-04 - val_abdomen: 0.0014 - val_wingL: 0.0012 - val_wingR: 0.0011 - val_forelegL4: 0.0015 - val_forelegR4: 0.0016 - val_midlegL4: 0.0014 - val_midlegR4: 0.0014 - val_hindlegL4: 0.0015 - val_hindlegR4: 0.0015 - val_eyeL: 6.2925e-04 - val_eyeR: 6.5965e-04 - lr: 1.0000e-04 - 7s/epoch - 19ms/step\n",
             "Epoch 9/10\n",
-            "360/360 - 53s - loss: 0.0010 - head: 4.0425e-04 - thorax: 2.3597e-04 - abdomen: 0.0012 - wingL: 0.0010 - wingR: 0.0011 - forelegL4: 0.0014 - forelegR4: 0.0014 - midlegL4: 0.0013 - midlegR4: 0.0013 - hindlegL4: 0.0016 - hindlegR4: 0.0017 - eyeL: 5.0906e-04 - eyeR: 4.9227e-04 - val_loss: 0.0010 - val_head: 3.9088e-04 - val_thorax: 2.1458e-04 - val_abdomen: 0.0012 - val_wingL: 0.0010 - val_wingR: 9.4879e-04 - val_forelegL4: 0.0012 - val_forelegR4: 0.0013 - val_midlegL4: 0.0011 - val_midlegR4: 0.0014 - val_hindlegL4: 0.0016 - val_hindlegR4: 0.0017 - val_eyeL: 4.6829e-04 - val_eyeR: 4.7323e-04 - lr: 1.0000e-04 - 53s/epoch - 147ms/step\n",
+            "360/360 - 7s - loss: 0.0011 - head: 4.2635e-04 - thorax: 2.4829e-04 - abdomen: 0.0012 - wingL: 0.0010 - wingR: 0.0010 - forelegL4: 0.0015 - forelegR4: 0.0014 - midlegL4: 0.0013 - midlegR4: 0.0013 - hindlegL4: 0.0016 - hindlegR4: 0.0017 - eyeL: 5.0197e-04 - eyeR: 4.8384e-04 - val_loss: 0.0011 - val_head: 4.8699e-04 - val_thorax: 3.5631e-04 - val_abdomen: 0.0013 - val_wingL: 0.0011 - val_wingR: 0.0011 - val_forelegL4: 0.0014 - val_forelegR4: 0.0016 - val_midlegL4: 0.0013 - val_midlegR4: 0.0015 - val_hindlegL4: 0.0014 - val_hindlegR4: 0.0015 - val_eyeL: 6.1692e-04 - val_eyeR: 5.8370e-04 - lr: 1.0000e-04 - 7s/epoch - 19ms/step\n",
             "Epoch 10/10\n",
-            "360/360 - 55s - loss: 9.7632e-04 - head: 3.7896e-04 - thorax: 2.1828e-04 - abdomen: 0.0011 - wingL: 9.9185e-04 - wingR: 9.9033e-04 - forelegL4: 0.0014 - forelegR4: 0.0013 - midlegL4: 0.0012 - midlegR4: 0.0012 - hindlegL4: 0.0015 - hindlegR4: 0.0016 - eyeL: 4.7323e-04 - eyeR: 4.5868e-04 - val_loss: 9.2870e-04 - val_head: 3.3704e-04 - val_thorax: 1.5806e-04 - val_abdomen: 0.0010 - val_wingL: 9.5121e-04 - val_wingR: 9.2122e-04 - val_forelegL4: 0.0012 - val_forelegR4: 0.0014 - val_midlegL4: 0.0010 - val_midlegR4: 0.0012 - val_hindlegL4: 0.0015 - val_hindlegR4: 0.0016 - val_eyeL: 4.2130e-04 - val_eyeR: 4.1479e-04 - lr: 1.0000e-04 - 55s/epoch - 154ms/step\n",
-            "INFO:sleap.nn.training:Finished training loop. [9.4 min]\n",
+            "360/360 - 7s - loss: 9.8454e-04 - head: 3.9611e-04 - thorax: 2.2278e-04 - abdomen: 0.0012 - wingL: 9.4893e-04 - wingR: 9.5555e-04 - forelegL4: 0.0014 - forelegR4: 0.0014 - midlegL4: 0.0012 - midlegR4: 0.0012 - hindlegL4: 0.0015 - hindlegR4: 0.0016 - eyeL: 4.7396e-04 - eyeR: 4.4770e-04 - val_loss: 0.0010 - val_head: 4.9330e-04 - val_thorax: 2.9460e-04 - val_abdomen: 0.0013 - val_wingL: 9.5190e-04 - val_wingR: 9.9289e-04 - val_forelegL4: 0.0014 - val_forelegR4: 0.0015 - val_midlegL4: 0.0012 - val_midlegR4: 0.0012 - val_hindlegL4: 0.0014 - val_hindlegR4: 0.0014 - val_eyeL: 5.5512e-04 - val_eyeR: 5.3737e-04 - lr: 1.0000e-04 - 7s/epoch - 19ms/step\n",
+            "INFO:sleap.nn.training:Finished training loop. [1.3 min]\n",
             "INFO:sleap.nn.training:Deleting visualization directory: models/baseline_model.topdown/viz\n",
             "INFO:sleap.nn.training:Saving evaluation metrics to model folder...\n"
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         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.train.slp\n",
             "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.train.npz\n",
-            "INFO:sleap.nn.evals:OKS mAP: 0.518988\n"
+            "INFO:sleap.nn.evals:OKS mAP: 0.508754\n"
           ]
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         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.val.slp\n",
             "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.val.npz\n",
-            "INFO:sleap.nn.evals:OKS mAP: 0.520377\n"
+            "INFO:sleap.nn.evals:OKS mAP: 0.477220\n"
           ]
         }
+      ],
+      "source": [
+        "trainer.train()"
       ]
     },
     {
@@ -631,6 +504,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -645,126 +519,121 @@
         "id": "ENOiptvQwrtI",
         "outputId": "ccdec444-17ae-4040-9aa3-509086e3dc37"
       },
-      "source": [
-        "trainer.config.optimization.epochs = 3\n",
-        "trainer.train()"
-      ],
-      "execution_count": 8,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation...\n",
-            "INFO:sleap.nn.training:Finished creating training datasets. [29.4s]\n",
+            "INFO:sleap.nn.training:Finished creating training datasets. [17.1s]\n",
             "INFO:sleap.nn.training:Starting training loop...\n",
             "Epoch 1/3\n",
-            "360/360 - 57s - loss: 9.1732e-04 - head: 3.5629e-04 - thorax: 1.9609e-04 - abdomen: 0.0010 - wingL: 9.1318e-04 - wingR: 9.1330e-04 - forelegL4: 0.0013 - forelegR4: 0.0013 - midlegL4: 0.0011 - midlegR4: 0.0011 - hindlegL4: 0.0014 - hindlegR4: 0.0015 - eyeL: 4.4475e-04 - eyeR: 4.3944e-04 - val_loss: 9.2727e-04 - val_head: 3.8719e-04 - val_thorax: 1.5200e-04 - val_abdomen: 0.0011 - val_wingL: 9.3115e-04 - val_wingR: 8.9376e-04 - val_forelegL4: 0.0012 - val_forelegR4: 0.0012 - val_midlegL4: 9.9703e-04 - val_midlegR4: 0.0012 - val_hindlegL4: 0.0015 - val_hindlegR4: 0.0016 - val_eyeL: 4.5374e-04 - val_eyeR: 5.1839e-04 - lr: 1.0000e-04 - 57s/epoch - 158ms/step\n",
+            "360/360 - 7s - loss: 9.3201e-04 - head: 3.7118e-04 - thorax: 2.0303e-04 - abdomen: 0.0011 - wingL: 8.9319e-04 - wingR: 9.0134e-04 - forelegL4: 0.0013 - forelegR4: 0.0013 - midlegL4: 0.0011 - midlegR4: 0.0011 - hindlegL4: 0.0014 - hindlegR4: 0.0015 - eyeL: 4.4919e-04 - eyeR: 4.2012e-04 - val_loss: 9.4680e-04 - val_head: 3.9131e-04 - val_thorax: 2.4191e-04 - val_abdomen: 0.0010 - val_wingL: 8.9155e-04 - val_wingR: 8.9295e-04 - val_forelegL4: 0.0013 - val_forelegR4: 0.0014 - val_midlegL4: 0.0012 - val_midlegR4: 0.0012 - val_hindlegL4: 0.0013 - val_hindlegR4: 0.0013 - val_eyeL: 5.3658e-04 - val_eyeR: 5.0085e-04 - lr: 1.0000e-04 - 7s/epoch - 20ms/step\n",
             "Epoch 2/3\n",
-            "360/360 - 56s - loss: 8.7900e-04 - head: 3.4532e-04 - thorax: 1.7895e-04 - abdomen: 0.0010 - wingL: 8.7539e-04 - wingR: 8.8524e-04 - forelegL4: 0.0012 - forelegR4: 0.0012 - midlegL4: 0.0010 - midlegR4: 0.0010 - hindlegL4: 0.0014 - hindlegR4: 0.0014 - eyeL: 4.3484e-04 - eyeR: 4.2888e-04 - val_loss: 8.5310e-04 - val_head: 3.0429e-04 - val_thorax: 1.4837e-04 - val_abdomen: 0.0010 - val_wingL: 8.2237e-04 - val_wingR: 8.3093e-04 - val_forelegL4: 0.0011 - val_forelegR4: 0.0012 - val_midlegL4: 8.5634e-04 - val_midlegR4: 0.0011 - val_hindlegL4: 0.0014 - val_hindlegR4: 0.0015 - val_eyeL: 4.0362e-04 - val_eyeR: 3.8104e-04 - lr: 1.0000e-04 - 56s/epoch - 156ms/step\n",
+            "360/360 - 7s - loss: 8.8906e-04 - head: 3.6015e-04 - thorax: 1.9128e-04 - abdomen: 0.0010 - wingL: 8.5054e-04 - wingR: 8.5352e-04 - forelegL4: 0.0013 - forelegR4: 0.0013 - midlegL4: 0.0010 - midlegR4: 0.0011 - hindlegL4: 0.0014 - hindlegR4: 0.0014 - eyeL: 4.3093e-04 - eyeR: 4.0690e-04 - val_loss: 8.9501e-04 - val_head: 4.1907e-04 - val_thorax: 2.3487e-04 - val_abdomen: 0.0010 - val_wingL: 8.6145e-04 - val_wingR: 8.4151e-04 - val_forelegL4: 0.0013 - val_forelegR4: 0.0014 - val_midlegL4: 0.0010 - val_midlegR4: 0.0011 - val_hindlegL4: 0.0013 - val_hindlegR4: 0.0012 - val_eyeL: 5.2130e-04 - val_eyeR: 4.9293e-04 - lr: 1.0000e-04 - 7s/epoch - 19ms/step\n",
             "Epoch 3/3\n",
-            "360/360 - 56s - loss: 8.4466e-04 - head: 3.4540e-04 - thorax: 1.6180e-04 - abdomen: 9.6890e-04 - wingL: 8.4974e-04 - wingR: 8.5187e-04 - forelegL4: 0.0012 - forelegR4: 0.0012 - midlegL4: 9.5015e-04 - midlegR4: 9.8870e-04 - hindlegL4: 0.0013 - hindlegR4: 0.0014 - eyeL: 4.2245e-04 - eyeR: 4.0856e-04 - val_loss: 8.2153e-04 - val_head: 3.1832e-04 - val_thorax: 1.4803e-04 - val_abdomen: 9.4013e-04 - val_wingL: 8.4738e-04 - val_wingR: 8.4686e-04 - val_forelegL4: 0.0010 - val_forelegR4: 0.0011 - val_midlegL4: 8.5740e-04 - val_midlegR4: 0.0010 - val_hindlegL4: 0.0014 - val_hindlegR4: 0.0015 - val_eyeL: 3.7928e-04 - val_eyeR: 3.8285e-04 - lr: 1.0000e-04 - 56s/epoch - 156ms/step\n",
-            "INFO:sleap.nn.training:Finished training loop. [2.8 min]\n",
+            "360/360 - 7s - loss: 8.5396e-04 - head: 3.4440e-04 - thorax: 1.7180e-04 - abdomen: 9.9867e-04 - wingL: 8.1743e-04 - wingR: 8.2288e-04 - forelegL4: 0.0012 - forelegR4: 0.0012 - midlegL4: 9.7110e-04 - midlegR4: 0.0010 - hindlegL4: 0.0013 - hindlegR4: 0.0014 - eyeL: 4.1497e-04 - eyeR: 3.9294e-04 - val_loss: 8.8076e-04 - val_head: 3.7130e-04 - val_thorax: 2.4712e-04 - val_abdomen: 0.0010 - val_wingL: 8.2889e-04 - val_wingR: 8.5931e-04 - val_forelegL4: 0.0012 - val_forelegR4: 0.0014 - val_midlegL4: 9.9400e-04 - val_midlegR4: 0.0011 - val_hindlegL4: 0.0012 - val_hindlegR4: 0.0012 - val_eyeL: 4.9486e-04 - val_eyeR: 4.6961e-04 - lr: 1.0000e-04 - 7s/epoch - 19ms/step\n",
+            "INFO:sleap.nn.training:Finished training loop. [0.4 min]\n",
             "INFO:sleap.nn.training:Deleting visualization directory: models/baseline_model.topdown/viz\n",
             "INFO:sleap.nn.training:Saving evaluation metrics to model folder...\n"
           ]
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         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.train.slp\n",
             "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.train.npz\n",
-            "INFO:sleap.nn.evals:OKS mAP: 0.551905\n"
+            "INFO:sleap.nn.evals:OKS mAP: 0.559100\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
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         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.val.slp\n",
             "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.val.npz\n",
-            "INFO:sleap.nn.evals:OKS mAP: 0.551469\n"
+            "INFO:sleap.nn.evals:OKS mAP: 0.529680\n"
           ]
         }
+      ],
+      "source": [
+        "trainer.config.optimization.epochs = 3\n",
+        "trainer.train()"
       ]
     },
     {
@@ -789,6 +658,7 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -796,23 +666,10 @@
         "id": "NDL6ScTDxrso",
         "outputId": "f63c3ef8-97d0-4484-e951-b120dcbbffac"
       },
-      "source": [
-        "# Load config.\n",
-        "cfg = sleap.load_config(\"models/baseline_model.topdown\")\n",
-        "# cfg.outputs.run_name = \"new_folder\"  # Set the run_name to a new value if you want the model to be saved to a different folder.\n",
-        "\n",
-        "# Create and initialize the trainer.\n",
-        "trainer = sleap.nn.training.Trainer.from_config(cfg)\n",
-        "trainer.setup()\n",
-        "\n",
-        "# Replace the randomly initialized weights with the saved weights.\n",
-        "trainer.keras_model.load_weights(\"models/baseline_model.topdown/best_model.h5\")"
-      ],
-      "execution_count": 9,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO:sleap.nn.training:Loading training labels from: labels.pkg.slp\n",
             "INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1\n",
@@ -821,7 +678,7 @@
             "INFO:sleap.nn.training:Setting up pipeline builders...\n",
             "INFO:sleap.nn.training:Setting up model...\n",
             "INFO:sleap.nn.training:Building test pipeline...\n",
-            "INFO:sleap.nn.training:Loaded test example. [1.909s]\n",
+            "INFO:sleap.nn.training:Loaded test example. [0.925s]\n",
             "INFO:sleap.nn.training:  Input shape: (160, 160, 1)\n",
             "INFO:sleap.nn.training:Created Keras model.\n",
             "INFO:sleap.nn.training:  Backbone: UNet(stacks=1, filters=16, filters_rate=2.0, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=4, middle_block=True, up_blocks=2, up_interpolate=False, block_contraction=False)\n",
@@ -831,6 +688,7 @@
             "INFO:sleap.nn.training:    [0] = CenteredInstanceConfmapsHead(part_names=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], anchor_part='thorax', sigma=1.5, output_stride=4, loss_weight=1.0)\n",
             "INFO:sleap.nn.training:  Outputs: \n",
             "INFO:sleap.nn.training:    [0] = KerasTensor(type_spec=TensorSpec(shape=(None, 40, 40, 13), dtype=tf.float32, name=None), name='CenteredInstanceConfmapsHead/BiasAdd:0', description=\"created by layer 'CenteredInstanceConfmapsHead'\")\n",
+            "INFO:sleap.nn.training:Training from scratch\n",
             "INFO:sleap.nn.training:Setting up data pipelines...\n",
             "INFO:sleap.nn.training:Training set: n = 1440\n",
             "INFO:sleap.nn.training:Validation set: n = 160\n",
@@ -840,13 +698,26 @@
             "INFO:sleap.nn.training:Setting up outputs...\n",
             "INFO:sleap.nn.training:Created run path: models/baseline_model.topdown\n",
             "INFO:sleap.nn.training:Setting up visualization...\n",
-            "INFO:sleap.nn.training:Finished trainer set up. [6.0s]\n"
+            "INFO:sleap.nn.training:Finished trainer set up. [2.2s]\n"
           ]
         }
+      ],
+      "source": [
+        "# Load config.\n",
+        "cfg = sleap.load_config(\"models/baseline_model.topdown\")\n",
+        "# cfg.outputs.run_name = \"new_folder\"  # Set the run_name to a new value if you want the model to be saved to a different folder.\n",
+        "\n",
+        "# Create and initialize the trainer.\n",
+        "trainer = sleap.nn.training.Trainer.from_config(cfg)\n",
+        "trainer.setup()\n",
+        "\n",
+        "# Replace the randomly initialized weights with the saved weights.\n",
+        "trainer.keras_model.load_weights(\"models/baseline_model.topdown/best_model.h5\")"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 13,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -861,126 +732,121 @@
         "id": "HlGP3dYMy2NG",
         "outputId": "c32a4240-1abd-401b-caab-4d64bec8348d"
       },
-      "source": [
-        "trainer.config.optimization.epochs = 3\n",
-        "trainer.train()"
-      ],
-      "execution_count": 10,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation...\n",
-            "INFO:sleap.nn.training:Finished creating training datasets. [28.9s]\n",
+            "INFO:sleap.nn.training:Finished creating training datasets. [17.7s]\n",
             "INFO:sleap.nn.training:Starting training loop...\n",
             "Epoch 1/3\n",
-            "360/360 - 63s - loss: 8.2769e-04 - head: 3.4427e-04 - thorax: 1.6900e-04 - abdomen: 9.4941e-04 - wingL: 8.1514e-04 - wingR: 8.1826e-04 - forelegL4: 0.0012 - forelegR4: 0.0012 - midlegL4: 9.2980e-04 - midlegR4: 9.6439e-04 - hindlegL4: 0.0013 - hindlegR4: 0.0013 - eyeL: 4.2129e-04 - eyeR: 4.0767e-04 - val_loss: 7.8855e-04 - val_head: 3.2701e-04 - val_thorax: 1.8405e-04 - val_abdomen: 0.0010 - val_wingL: 7.3709e-04 - val_wingR: 7.1027e-04 - val_forelegL4: 0.0010 - val_forelegR4: 0.0011 - val_midlegL4: 9.3918e-04 - val_midlegR4: 9.0288e-04 - val_hindlegL4: 0.0012 - val_hindlegR4: 0.0013 - val_eyeL: 3.8746e-04 - val_eyeR: 3.3939e-04 - lr: 1.0000e-04 - 63s/epoch - 174ms/step\n",
+            "360/360 - 9s - loss: 8.3664e-04 - head: 3.5190e-04 - thorax: 1.7037e-04 - abdomen: 9.8467e-04 - wingL: 7.9929e-04 - wingR: 8.0385e-04 - forelegL4: 0.0012 - forelegR4: 0.0012 - midlegL4: 9.5228e-04 - midlegR4: 9.8510e-04 - hindlegL4: 0.0013 - hindlegR4: 0.0013 - eyeL: 4.0772e-04 - eyeR: 3.9413e-04 - val_loss: 8.7351e-04 - val_head: 4.0943e-04 - val_thorax: 1.7453e-04 - val_abdomen: 9.4413e-04 - val_wingL: 8.3617e-04 - val_wingR: 8.4860e-04 - val_forelegL4: 0.0012 - val_forelegR4: 0.0012 - val_midlegL4: 9.4441e-04 - val_midlegR4: 0.0011 - val_hindlegL4: 0.0014 - val_hindlegR4: 0.0014 - val_eyeL: 4.4847e-04 - val_eyeR: 4.4179e-04 - lr: 1.0000e-04 - 9s/epoch - 24ms/step\n",
             "Epoch 2/3\n",
-            "360/360 - 58s - loss: 7.9662e-04 - head: 3.2407e-04 - thorax: 1.5127e-04 - abdomen: 9.1911e-04 - wingL: 7.6866e-04 - wingR: 7.8884e-04 - forelegL4: 0.0011 - forelegR4: 0.0011 - midlegL4: 8.8560e-04 - midlegR4: 9.3151e-04 - hindlegL4: 0.0012 - hindlegR4: 0.0013 - eyeL: 4.1677e-04 - eyeR: 3.9983e-04 - val_loss: 7.3673e-04 - val_head: 2.8314e-04 - val_thorax: 1.1026e-04 - val_abdomen: 9.4263e-04 - val_wingL: 6.7871e-04 - val_wingR: 6.4992e-04 - val_forelegL4: 0.0011 - val_forelegR4: 0.0011 - val_midlegL4: 8.0315e-04 - val_midlegR4: 8.3331e-04 - val_hindlegL4: 0.0012 - val_hindlegR4: 0.0012 - val_eyeL: 3.4531e-04 - val_eyeR: 3.5707e-04 - lr: 1.0000e-04 - 58s/epoch - 162ms/step\n",
+            "360/360 - 7s - loss: 8.0541e-04 - head: 3.4627e-04 - thorax: 1.6070e-04 - abdomen: 9.4325e-04 - wingL: 7.7257e-04 - wingR: 7.7434e-04 - forelegL4: 0.0012 - forelegR4: 0.0012 - midlegL4: 8.9573e-04 - midlegR4: 9.3483e-04 - hindlegL4: 0.0013 - hindlegR4: 0.0013 - eyeL: 4.0939e-04 - eyeR: 3.8417e-04 - val_loss: 8.2339e-04 - val_head: 3.9561e-04 - val_thorax: 1.2637e-04 - val_abdomen: 8.6513e-04 - val_wingL: 7.1751e-04 - val_wingR: 7.5540e-04 - val_forelegL4: 0.0012 - val_forelegR4: 0.0012 - val_midlegL4: 8.5588e-04 - val_midlegR4: 0.0010 - val_hindlegL4: 0.0013 - val_hindlegR4: 0.0014 - val_eyeL: 4.8189e-04 - val_eyeR: 4.2402e-04 - lr: 1.0000e-04 - 7s/epoch - 20ms/step\n",
             "Epoch 3/3\n",
-            "360/360 - 58s - loss: 7.6463e-04 - head: 3.0854e-04 - thorax: 1.3497e-04 - abdomen: 8.9188e-04 - wingL: 7.4921e-04 - wingR: 7.5430e-04 - forelegL4: 0.0011 - forelegR4: 0.0011 - midlegL4: 8.3320e-04 - midlegR4: 8.7736e-04 - hindlegL4: 0.0012 - hindlegR4: 0.0013 - eyeL: 3.9640e-04 - eyeR: 3.7940e-04 - val_loss: 7.0126e-04 - val_head: 2.8905e-04 - val_thorax: 1.1305e-04 - val_abdomen: 9.0676e-04 - val_wingL: 6.4827e-04 - val_wingR: 6.2576e-04 - val_forelegL4: 0.0010 - val_forelegR4: 9.8253e-04 - val_midlegL4: 8.0471e-04 - val_midlegR4: 7.3788e-04 - val_hindlegL4: 0.0011 - val_hindlegR4: 0.0012 - val_eyeL: 3.1543e-04 - val_eyeR: 3.4044e-04 - lr: 1.0000e-04 - 58s/epoch - 161ms/step\n",
-            "INFO:sleap.nn.training:Finished training loop. [3.0 min]\n",
+            "360/360 - 7s - loss: 7.7741e-04 - head: 3.2087e-04 - thorax: 1.4398e-04 - abdomen: 9.1826e-04 - wingL: 7.4005e-04 - wingR: 7.5282e-04 - forelegL4: 0.0011 - forelegR4: 0.0011 - midlegL4: 8.6551e-04 - midlegR4: 8.9726e-04 - hindlegL4: 0.0012 - hindlegR4: 0.0013 - eyeL: 3.8423e-04 - eyeR: 3.7468e-04 - val_loss: 8.4657e-04 - val_head: 3.5649e-04 - val_thorax: 1.2162e-04 - val_abdomen: 8.9171e-04 - val_wingL: 7.9007e-04 - val_wingR: 8.2471e-04 - val_forelegL4: 0.0013 - val_forelegR4: 0.0013 - val_midlegL4: 8.1375e-04 - val_midlegR4: 9.8217e-04 - val_hindlegL4: 0.0014 - val_hindlegR4: 0.0013 - val_eyeL: 4.7370e-04 - val_eyeR: 4.2098e-04 - lr: 1.0000e-04 - 7s/epoch - 19ms/step\n",
+            "INFO:sleap.nn.training:Finished training loop. [0.4 min]\n",
             "INFO:sleap.nn.training:Deleting visualization directory: models/baseline_model.topdown/viz\n",
             "INFO:sleap.nn.training:Saving evaluation metrics to model folder...\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b94057057f6442c990c6fc548910a685",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "c74d0a9e497146acaf8da36faf5f496a"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              ""
-            ],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "\n"
-            ],
             "text/html": [
               "
\n",
               "
\n"
+            ],
+            "text/plain": [
+              "\n"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.train.slp\n",
             "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.train.npz\n",
-            "INFO:sleap.nn.evals:OKS mAP: 0.597609\n"
+            "INFO:sleap.nn.evals:OKS mAP: 0.585451\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Output()"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8f2e64c8d4d6457986ee8b43b47e2876",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "bf6a847899a24fcea5f14409a7ee1c33"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              ""
-            ],
             "text/html": [
               "
\n"
-            ]
+            ],
+            "text/plain": []
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "\n"
-            ],
             "text/html": [
               "
\n",
               "
\n"
+            ],
+            "text/plain": [
+              "\n"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.val.slp\n",
             "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.val.npz\n",
-            "INFO:sleap.nn.evals:OKS mAP: 0.621393\n"
+            "INFO:sleap.nn.evals:OKS mAP: 0.574921\n"
           ]
         }
+      ],
+      "source": [
+        "trainer.config.optimization.epochs = 3\n",
+        "trainer.train()"
       ]
     },
     {
@@ -994,5 +860,32 @@
         "The resulting model can be used as usual for inference on new data."
       ]
     }
-  ]
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "machine_shape": "hm",
+      "name": "SLEAP - Interactive and resumable training.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
diff --git a/docs/notebooks/Model_evaluation.ipynb b/docs/notebooks/Model_evaluation.ipynb
index 4368e92e7..9bc55953d 100644
--- a/docs/notebooks/Model_evaluation.ipynb
+++ b/docs/notebooks/Model_evaluation.ipynb
@@ -24,17 +24,26 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 3,
       "metadata": {
         "id": "5bNDjxe1BZXV"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1;31mE: \u001b[0mCould not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\u001b[0m\n",
+            "\u001b[1;31mE: \u001b[0mUnable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?\u001b[0m\n"
+          ]
+        }
+      ],
       "source": [
-        "!pip uninstall -y opencv-python opencv-contrib-python > /dev/null 2>&1\n",
-        "!pip install sleap > /dev/null 2>&1\n",
-        "!apt install tree > /dev/null 2>&1\n",
-        "!wget https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip > /dev/null 2>&1\n",
-        "!unzip -o -d \"td_fast.210505_012601.centered_instance.n=1800\" \"td_fast.210505_012601.centered_instance.n=1800.zip\" > /dev/null 2>&1"
+        "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n",
+        "!pip install -qqq sleap[pypi]\n",
+        "!apt -qq install tree\n",
+        "!wget -q https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip\n",
+        "!unzip -qq -o -d \"td_fast.210505_012601.centered_instance.n=1800\" \"td_fast.210505_012601.centered_instance.n=1800.zip\""
       ]
     },
     {
@@ -53,7 +62,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -66,7 +75,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "td_fast.210505_012601.centered_instance.n=1800\n",
+            "\u001b[01;34mtd_fast.210505_012601.centered_instance.n=1800\u001b[00m\n",
             "├── best_model.h5\n",
             "├── initial_config.json\n",
             "├── labels_gt.test.slp\n",
@@ -107,7 +116,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -116,15 +125,23 @@
         "outputId": "fedb9d7b-6dcc-4048-d030-eba38a006086"
       },
       "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2023-09-01 14:13:14.982109: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n",
+            "2023-09-01 14:13:14.982120: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+          ]
+        },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "SLEAP: 1.1.5\n",
-            "TensorFlow: 2.3.1\n",
-            "Numpy: 1.19.5\n",
-            "Python: 3.7.11\n",
-            "OS: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\n"
+            "SLEAP: 1.3.1\n",
+            "TensorFlow: 2.8.4\n",
+            "Numpy: 1.21.6\n",
+            "Python: 3.7.12\n",
+            "OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n"
           ]
         }
       ],
@@ -151,7 +168,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -216,7 +233,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -284,7 +301,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -322,7 +339,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -332,23 +349,14 @@
         "outputId": "59d0c939-53a3-4580-cf0b-be85b58ad067"
       },
       "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n"
-          ]
-        },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "
"
             ]
           },
-          "metadata": {
-            "tags": []
-          },
+          "metadata": {},
           "output_type": "display_data"
         }
       ],
@@ -373,7 +381,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -385,14 +393,12 @@
       "outputs": [
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "
"
             ]
           },
-          "metadata": {
-            "tags": []
-          },
+          "metadata": {},
           "output_type": "display_data"
         }
       ],
@@ -417,7 +423,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -429,14 +435,12 @@
       "outputs": [
         {
           "data": {
-            "image/png": 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",
+            "image/png": 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",
             "text/plain": [
               "
"
             ]
           },
-          "metadata": {
-            "tags": []
-          },
+          "metadata": {},
           "output_type": "display_data"
         }
       ],
@@ -462,7 +466,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -500,13 +504,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 13,
       "metadata": {
         "id": "YHCLd3pkRhGT"
       },
       "outputs": [],
       "source": [
-        "!wget https://storage.googleapis.com/sleap-data/datasets/wt_gold.13pt/tracking_split2/test.pkg.slp > /dev/null 2>&1"
+        "!wget -q https://storage.googleapis.com/sleap-data/datasets/wt_gold.13pt/tracking_split2/test.pkg.slp"
       ]
     },
     {
@@ -520,11 +524,76 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 14,
       "metadata": {
         "id": "OMXHY-7YRyTB"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2023-09-01 14:14:04.208933: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 14:14:04.209734: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n",
+            "2023-09-01 14:14:04.209771: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n",
+            "2023-09-01 14:14:04.209801: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n",
+            "2023-09-01 14:14:04.209829: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n",
+            "2023-09-01 14:14:04.209859: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n",
+            "2023-09-01 14:14:04.209886: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n",
+            "2023-09-01 14:14:04.209912: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n",
+            "2023-09-01 14:14:04.209939: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:\n",
+            "2023-09-01 14:14:04.209945: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
+            "Skipping registering GPU devices...\n",
+            "2023-09-01 14:14:04.245745: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
+            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "061ef3f7278a47bbbe199d38ccd6be37",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2023-09-01 14:14:07.317060: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: \"CropAndResize\" attr { key: \"T\" value { type: DT_UINT8 } } attr { key: \"extrapolation_value\" value { f: 0 } } attr { key: \"method\" value { s: \"bilinear\" } } inputs { dtype: DT_UINT8 shape { dim { size: 4 } dim { size: 1024 } dim { size: 1024 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: \"CPU\" vendor: \"GenuineIntel\" model: \"103\" frequency: 3600 num_cores: 16 environment { key: \"cpu_instruction_set\" value: \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\" } environment { key: \"eigen\" value: \"3.4.90\" } l1_cache_size: 49152 l2_cache_size: 524288 l3_cache_size: 16777216 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: -27 } dim { size: -28 } dim { size: 1 } } }\n",
+            "2023-09-01 14:14:07.320224: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: \"CropAndResize\" attr { key: \"T\" value { type: DT_FLOAT } } attr { key: \"extrapolation_value\" value { f: 0 } } attr { key: \"method\" value { s: \"bilinear\" } } inputs { dtype: DT_FLOAT shape { dim { size: -42 } dim { size: -43 } dim { size: -44 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -10 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -10 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: \"CPU\" vendor: \"GenuineIntel\" model: \"103\" frequency: 3600 num_cores: 16 environment { key: \"cpu_instruction_set\" value: \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\" } environment { key: \"eigen\" value: \"3.4.90\" } l1_cache_size: 49152 l2_cache_size: 524288 l3_cache_size: 16777216 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -10 } dim { size: -48 } dim { size: -49 } dim { size: 1 } } }\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "predictor = sleap.load_model(\"td_fast.210505_012601.centered_instance.n=1800\")\n",
         "labels_gt = sleap.load_file(\"test.pkg.slp\")\n",
@@ -542,7 +611,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 15,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -557,7 +626,7 @@
           "text": [
             "Error distance (50%): 0.8984147543126978\n",
             "Error distance (90%): 2.197896466395166\n",
-            "Error distance (95%): 3.148422807907632\n",
+            "Error distance (95%): 3.1484228079076315\n",
             "mAP: 0.797836431061851\n",
             "mAR: 0.8782499999999999\n"
           ]
@@ -585,7 +654,16 @@
       "name": "python3"
     },
     "language_info": {
-      "name": "python"
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,
diff --git a/docs/notebooks/Post_inference_tracking.ipynb b/docs/notebooks/Post_inference_tracking.ipynb
index 20e835138..cfd73c99f 100644
--- a/docs/notebooks/Post_inference_tracking.ipynb
+++ b/docs/notebooks/Post_inference_tracking.ipynb
@@ -1,20 +1,4 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "SLEAP - Post-inference tracking.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
@@ -28,6 +12,9 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "gQXmUCj9ljP3"
+      },
       "source": [
         "# Post-inference tracking\n",
         "\n",
@@ -38,40 +25,31 @@
         "In this notebook, we will explore how to re-run the tracking given an existing predictions SLP file.\n",
         "\n",
         "**Note:** Tracking does not run on the GPU, so this notebook can be run locally on your computer without the hassle of uploading your data if desired."
-      ],
-      "metadata": {
-        "id": "gQXmUCj9ljP3"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "WL67LNf10hev"
+      },
       "source": [
         "## 1. Setup SLEAP\n",
         "\n",
         "Run this cell first to install SLEAP. If you get a dependency error in subsequent cells, just click **Runtime** → **Restart runtime** to reload the packages.\n"
-      ],
-      "metadata": {
-        "id": "WL67LNf10hev"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Install"
-      ],
       "metadata": {
         "id": "UtfcHSZCDnvS"
-      }
+      },
+      "source": [
+        "### Install"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "# This should take care of all the dependencies on colab:\n",
-        "!pip uninstall -y opencv-python opencv-contrib-python && pip install sleap\n",
-        "\n",
-        "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n",
-        "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap"
-      ],
+      "execution_count": 1,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -79,187 +57,28 @@
         "id": "HH0weH9f-T1N",
         "outputId": "d6f69d8d-9aed-4793-c346-2ab60f110316"
       },
-      "execution_count": 1,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Found existing installation: opencv-python 4.1.2.30\n",
-            "Uninstalling opencv-python-4.1.2.30:\n",
-            "  Successfully uninstalled opencv-python-4.1.2.30\n",
-            "Found existing installation: opencv-contrib-python 4.1.2.30\n",
-            "Uninstalling opencv-contrib-python-4.1.2.30:\n",
-            "  Successfully uninstalled opencv-contrib-python-4.1.2.30\n",
-            "Collecting sleap\n",
-            "  Downloading sleap-1.2.2-py3-none-any.whl (62.0 MB)\n",
-            "\u001b[K     |████████████████████████████████| 62.0 MB 19 kB/s \n",
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-            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (3.3.0)\n",
-            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (1.1.0)\n",
-            "Requirement already satisfied: keras<2.9,>=2.8.0rc0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (2.8.0)\n",
-            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (1.14.0)\n",
-            "Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (1.1.2)\n",
-            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (0.2.0)\n",
-            "Collecting tf-estimator-nightly==2.8.0.dev2021122109\n",
-            "  Downloading tf_estimator_nightly-2.8.0.dev2021122109-py2.py3-none-any.whl (462 kB)\n",
-            "\u001b[K     |████████████████████████████████| 462 kB 49.8 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (3.17.3)\n",
-            "Requirement already satisfied: tensorboard<2.9,>=2.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (2.8.0)\n",
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-            "Requirement already satisfied: absl-py>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (1.0.0)\n",
-            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9.0,>=2.6.3->sleap) (1.44.0)\n",
-            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from astunparse>=1.6.0->tensorflow<2.9.0,>=2.6.3->sleap) (0.37.1)\n",
-            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.4.6)\n",
-            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.8.1)\n",
-            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.3.6)\n",
-            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.6.1)\n",
-            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.0.1)\n",
-            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (2.23.0)\n",
-            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.35.0)\n",
-            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (4.8)\n",
-            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.2.8)\n",
-            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (4.2.4)\n",
-            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.3.1)\n",
-            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (4.11.3)\n",
-            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.7.0)\n",
-            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.4.8)\n",
-            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (2.10)\n",
-            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.0.4)\n",
-            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.24.3)\n",
-            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.2.0)\n",
-            "Building wheels for collected packages: pykalman, jsmin\n",
-            "  Building wheel for pykalman (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for pykalman: filename=pykalman-0.9.5-py3-none-any.whl size=48462 sha256=5de7d8c6487261ac5359426edf6b9d6ff977786a758424aaa6462a743fae77e4\n",
-            "  Stored in directory: /root/.cache/pip/wheels/6a/04/02/2dda6ea59c66d9e685affc8af3a31ad3a5d87b7311689efce6\n",
-            "  Building wheel for jsmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for jsmin: filename=jsmin-3.0.1-py3-none-any.whl size=13782 sha256=353b91b543700f74d4c7801c636ff32de6e99c9578162db575ea8d5e0b29d64e\n",
-            "  Stored in directory: /root/.cache/pip/wheels/a4/0b/64/fb4f87526ecbdf7921769a39d91dcfe4860e621cf15b8250d6\n",
-            "Successfully built pykalman jsmin\n",
-            "Installing collected packages: keras-applications, tf-estimator-nightly, shiboken2, opencv-python, image-classifiers, efficientnet, commonmark, colorama, attrs, segmentation-models, scikit-video, rich, qimage2ndarray, python-rapidjson, PySide2, pykalman, opencv-python-headless, jsonpickle, jsmin, imgstore, imgaug, cattrs, sleap\n",
-            "  Attempting uninstall: attrs\n",
-            "    Found existing installation: attrs 21.4.0\n",
-            "    Uninstalling attrs-21.4.0:\n",
-            "      Successfully uninstalled attrs-21.4.0\n",
-            "  Attempting uninstall: imgaug\n",
-            "    Found existing installation: imgaug 0.2.9\n",
-            "    Uninstalling imgaug-0.2.9:\n",
-            "      Successfully uninstalled imgaug-0.2.9\n",
-            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
-            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n",
-            "Successfully installed PySide2-5.14.1 attrs-21.2.0 cattrs-1.1.1 colorama-0.4.4 commonmark-0.9.1 efficientnet-1.0.0 image-classifiers-1.0.0 imgaug-0.4.0 imgstore-0.2.9 jsmin-3.0.1 jsonpickle-1.2 keras-applications-1.0.8 opencv-python-4.5.5.64 opencv-python-headless-4.5.5.62 pykalman-0.9.5 python-rapidjson-1.6 qimage2ndarray-1.8.3 rich-10.16.1 scikit-video-1.1.11 segmentation-models-1.0.1 shiboken2-5.14.1 sleap-1.2.2 tf-estimator-nightly-2.8.0.dev2021122109\n"
-          ]
-        }
+      "outputs": [],
+      "source": [
+        "# This should take care of all the dependencies on colab:\n",
+        "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n",
+        "!pip install -qqq sleap[pypi]\n",
+        "\n",
+        "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n",
+        "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Test"
-      ],
       "metadata": {
         "id": "d10pcIu70oLb"
-      }
+      },
+      "source": [
+        "### Test"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "#@title SLEAP and system versions: { display-mode: \"form\" }\n",
-        "import sleap\n",
-        "sleap.versions()\n",
-        "sleap.system_summary()"
-      ],
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -267,34 +86,63 @@
         "id": "WBGKYmLj9Zc2",
         "outputId": "8f044c67-3abe-4b8b-8552-db2b5c756c7c"
       },
-      "execution_count": 1,
       "outputs": [
         {
+          "name": "stderr",
           "output_type": "stream",
+          "text": [
+            "2023-09-01 14:17:16.250591: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:/home/talmolab/micromamba/envs/sleap_jupyter/lib:\n",
+            "2023-09-01 14:17:16.250602: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+          ]
+        },
+        {
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
-            "SLEAP: 1.2.2\n",
-            "TensorFlow: 2.8.0\n",
-            "Numpy: 1.21.5\n",
-            "Python: 3.7.13\n",
-            "OS: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\n",
+            "SLEAP: 1.3.1\n",
+            "TensorFlow: 2.8.4\n",
+            "Numpy: 1.21.6\n",
+            "Python: 3.7.12\n",
+            "OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n",
             "GPUs: None detected.\n"
           ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2023-09-01 14:17:17.389239: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+            "2023-09-01 14:17:17.390139: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:/home/talmolab/micromamba/envs/sleap_jupyter/lib:\n",
+            "2023-09-01 14:17:17.390188: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:/home/talmolab/micromamba/envs/sleap_jupyter/lib:\n",
+            "2023-09-01 14:17:17.390230: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:/home/talmolab/micromamba/envs/sleap_jupyter/lib:\n",
+            "2023-09-01 14:17:17.390267: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:/home/talmolab/micromamba/envs/sleap_jupyter/lib:\n",
+            "2023-09-01 14:17:17.390306: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:/home/talmolab/micromamba/envs/sleap_jupyter/lib:\n",
+            "2023-09-01 14:17:17.390345: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:/home/talmolab/micromamba/envs/sleap_jupyter/lib:\n",
+            "2023-09-01 14:17:17.390383: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:/home/talmolab/micromamba/envs/sleap_jupyter/lib:\n",
+            "2023-09-01 14:17:17.390421: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:/home/talmolab/micromamba/envs/sleap_jupyter/lib:\n",
+            "2023-09-01 14:17:17.390425: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
+            "Skipping registering GPU devices...\n"
+          ]
         }
+      ],
+      "source": [
+        "#@title SLEAP and system versions: { display-mode: \"form\" }\n",
+        "import sleap\n",
+        "sleap.versions()\n",
+        "sleap.system_summary()"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "hYBojEjY9qyr"
+      },
       "source": [
         "# 2. Setup data\n",
         "Here we're downloading an existing `.slp` file with predictions and the corresponding `.mp4` video.\n",
         "\n",
         "You should replace this with Google Drive mounting if running this on Google Colab, or simply skip it altogether and just set the paths below if running locally."
-      ],
-      "metadata": {
-        "id": "hYBojEjY9qyr"
-      }
+      ]
     },
     {
       "cell_type": "code",
@@ -306,91 +154,35 @@
         "id": "akfAyAo-9cAd",
         "outputId": "456bd33c-c1f6-4d57-dc37-a58ef8717472"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--2022-04-04 00:10:34--  https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/fly_clip.mp4?raw=true\n",
-            "Resolving github.com (github.com)... 13.114.40.48\n",
-            "Connecting to github.com (github.com)|13.114.40.48|:443... connected.\n",
-            "HTTP request sent, awaiting response... 302 Found\n",
-            "Location: https://github.com/talmolab/sleap-tutorial-uo/raw/main/data/fly_clip.mp4 [following]\n",
-            "--2022-04-04 00:10:34--  https://github.com/talmolab/sleap-tutorial-uo/raw/main/data/fly_clip.mp4\n",
-            "Reusing existing connection to github.com:443.\n",
-            "HTTP request sent, awaiting response... 302 Found\n",
-            "Location: https://raw.githubusercontent.com/talmolab/sleap-tutorial-uo/main/data/fly_clip.mp4 [following]\n",
-            "--2022-04-04 00:10:34--  https://raw.githubusercontent.com/talmolab/sleap-tutorial-uo/main/data/fly_clip.mp4\n",
-            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
-            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
-            "HTTP request sent, awaiting response... 200 OK\n",
-            "Length: 676194 (660K) [application/octet-stream]\n",
-            "Saving to: ‘fly_clip.mp4’\n",
-            "\n",
-            "fly_clip.mp4        100%[===================>] 660.35K  --.-KB/s    in 0.05s   \n",
-            "\n",
-            "2022-04-04 00:10:36 (12.1 MB/s) - ‘fly_clip.mp4’ saved [676194/676194]\n",
-            "\n",
-            "--2022-04-04 00:10:36--  https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/predictions.slp?raw=true\n",
-            "Resolving github.com (github.com)... 52.69.186.44\n",
-            "Connecting to github.com (github.com)|52.69.186.44|:443... connected.\n",
-            "HTTP request sent, awaiting response... 302 Found\n",
-            "Location: https://github.com/talmolab/sleap-tutorial-uo/raw/main/data/predictions.slp [following]\n",
-            "--2022-04-04 00:10:37--  https://github.com/talmolab/sleap-tutorial-uo/raw/main/data/predictions.slp\n",
-            "Reusing existing connection to github.com:443.\n",
-            "HTTP request sent, awaiting response... 302 Found\n",
-            "Location: https://raw.githubusercontent.com/talmolab/sleap-tutorial-uo/main/data/predictions.slp [following]\n",
-            "--2022-04-04 00:10:37--  https://raw.githubusercontent.com/talmolab/sleap-tutorial-uo/main/data/predictions.slp\n",
-            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
-            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
-            "HTTP request sent, awaiting response... 200 OK\n",
-            "Length: 420976 (411K) [application/octet-stream]\n",
-            "Saving to: ‘predictions.slp’\n",
-            "\n",
-            "predictions.slp     100%[===================>] 411.11K  --.-KB/s    in 0.04s   \n",
-            "\n",
-            "2022-04-04 00:10:38 (9.66 MB/s) - ‘predictions.slp’ saved [420976/420976]\n",
-            "\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "!wget -O fly_clip.mp4 https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/fly_clip.mp4?raw=true\n",
-        "!wget -O predictions.slp https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/predictions.slp?raw=true"
+        "!wget -q -O fly_clip.mp4 https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/fly_clip.mp4?raw=true\n",
+        "!wget -q -O predictions.slp https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/predictions.slp?raw=true"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "PREDICTIONS_FILE = \"predictions.slp\""
-      ],
+      "execution_count": 4,
       "metadata": {
         "id": "gQSc_ZjFnHl9"
       },
-      "execution_count": 2,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "PREDICTIONS_FILE = \"predictions.slp\""
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# 3. Track"
-      ],
       "metadata": {
         "id": "9z5rbej_-_Ea"
-      }
+      },
+      "source": [
+        "# 3. Track"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "# Load predictions\n",
-        "labels = sleap.load_file(PREDICTIONS_FILE)\n",
-        "\n",
-        "# Here I'm removing the tracks so we just have instances without any tracking applied.\n",
-        "for instance in labels.instances():\n",
-        "    instance.track = None\n",
-        "labels.tracks = []\n",
-        "labels"
-      ],
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -398,31 +190,45 @@
         "id": "MhHCTkdr-wTz",
         "outputId": "2e286994-eb4c-4648-c6b9-ab3e7d0cc605"
       },
-      "execution_count": 3,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "Labels(labeled_frames=1350, videos=1, skeletons=1, tracks=0)"
             ]
           },
+          "execution_count": 5,
           "metadata": {},
-          "execution_count": 3
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "# Load predictions\n",
+        "labels = sleap.load_file(PREDICTIONS_FILE)\n",
+        "\n",
+        "# Here I'm removing the tracks so we just have instances without any tracking applied.\n",
+        "for instance in labels.instances():\n",
+        "    instance.track = None\n",
+        "labels.tracks = []\n",
+        "labels"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Here we create a tracker with the options we want to experiment with. You can [read more about tracking in the documentation](https://sleap.ai/guides/proofreading.html#tracking-methods) or the parameters in the [`sleap-track` CLI help](https://sleap.ai/guides/cli.html#sleap-track)."
-      ],
       "metadata": {
         "id": "hwFC2WYWBQXe"
-      }
+      },
+      "source": [
+        "Here we create a tracker with the options we want to experiment with. You can [read more about tracking in the documentation](https://sleap.ai/guides/proofreading.html#tracking-methods) or the parameters in the [`sleap-track` CLI help](https://sleap.ai/guides/cli.html#sleap-track)."
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "AgDVuL-u9_iv"
+      },
+      "outputs": [],
       "source": [
         "# Create tracker\n",
         "tracker = sleap.nn.tracking.Tracker.make_tracker_by_name(\n",
@@ -451,32 +257,20 @@
         "    clean_instance_count=0,\n",
         "    clean_iou_threshold=None,\n",
         ")"
-      ],
-      "metadata": {
-        "id": "AgDVuL-u9_iv"
-      },
-      "execution_count": 4,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Next we'll actually run the tracking on each frame. This might take a bit longer when using the `\"flow\"` method."
-      ],
       "metadata": {
         "id": "EfMhLxWcBqBg"
-      }
+      },
+      "source": [
+        "Next we'll actually run the tracking on each frame. This might take a bit longer when using the `\"flow\"` method."
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "tracked_lfs = []\n",
-        "for lf in labels:\n",
-        "    lf.instances = tracker.track(lf.instances, img=lf.image)\n",
-        "    tracked_lfs.append(lf)\n",
-        "tracked_labels = sleap.Labels(tracked_lfs)\n",
-        "tracked_labels"
-      ],
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -484,36 +278,41 @@
         "id": "q-EE7r0pBpfD",
         "outputId": "eabfe089-b122-494d-c41e-996b0243ab71"
       },
-      "execution_count": 5,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "Labels(labeled_frames=1350, videos=1, skeletons=1, tracks=2)"
             ]
           },
+          "execution_count": 7,
           "metadata": {},
-          "execution_count": 5
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "tracked_lfs = []\n",
+        "for lf in labels:\n",
+        "    lf.instances = tracker.track(lf.instances, img=lf.image)\n",
+        "    tracked_lfs.append(lf)\n",
+        "tracked_labels = sleap.Labels(tracked_lfs)\n",
+        "tracked_labels"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "OjUvwRzWCJ_G"
+      },
       "source": [
         "# 4. Inspect and save\n",
         "\n",
         "Let's see the results and save out the tracked predictions."
-      ],
-      "metadata": {
-        "id": "OjUvwRzWCJ_G"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "tracked_labels[0].plot(scale=0.25)"
-      ],
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -522,25 +321,25 @@
         "id": "g-ia6hYGCXZX",
         "outputId": "2652a6e2-6f63-4b81-dd54-d8a01c6c25a4"
       },
-      "execution_count": 6,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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YxS5eESo7OtIqla5F3K1eUcjkFxNue+SfLhtdGO/vmOAc+Zmh3xovI7pKnKtSziPGEIQOhbK4msIJLQQreaQboXs/+eSTjqxKVtiHra2tmM1mpUy129t6fn6eGgv21/nKEAFdIdXlWx9q9KAzR73CrMG1wefvHOQ+OCWq+Wv8k4BT6FerVUmoyiCzBlgTnzENRsiZIHV1ddXZf9C2bbmmeIGuU8Bvbm46E4aCnCmJsdDdn+9TOOLJxcVFR/kxwEuBpaJVO3R6mvorninhTNe2t7cLwrq6urpn/Ti2qpMbCGt9GvpcQxNEmmqzEvbIa01qXZcRkvtGOZJcUQFPJpOiZMTzZ8+eleQ6xkh8bBmLc5Tksp4lk9XGn+2rzVON2+PHjxPJQVm9vxplkzajMRbDP2cddUZl7fF2cDCZ1xFxl0PAPw0AB4l+bdYWLRvSKkd0Bd6DZ/prmtvlyAw2Z1ZljEIljUEobnlkWX3p1CErrbtv+lPeiSanKxqiBBH3b7Cfap/XQfI4xRiXhlC9aZqS/v348eP44osv7pUnpKEJquVwjfPl5WWcnp52llxlPLRVgEHg58+fx4sXLzpJYOKb+ssVOtXFfvh48I/3eU6N9ytz5VRm09xmLvfRxjEODfJDqeaDRdxPe3XivRlDqAyUcn1wcFDK1sAvl8viXuhQ4p2dnVgul6UcoYmDg4MSwNJKydbWVuzu7hbFIfdkuVyWdjGQpvap7IuLiwLVPdbT53/X+DKW76yHgU3xZz6fl6CxFIB8aa58KFOVCU5Syru7uyUDUsKsgLFiRHT99Bwj/J5Y5pTJzkN4JaX+8uXL9Fn1TYlglKvpdFrQqa5xdUQIRatIQqQvX76M5XIZOzs7JR7GLQ1Nc5v85whIhpAKzxW+0JSv8tTiHe41qC2OzDLaSHFocr19+3aTx6rE+AGv8T8pUxYkDRIDbZrUTdMUISUUb9u2pJRnGvz09LTcp5iELKn2cXDgmQxEV0fukJSZ8jXevHlzb5A4uO+DMr5pn4n6I6XgsQ71hbsqMxdT/JYg0/UQSWlozN1t4RioXWq/L9+P5Q0V2JBMZRZacQ3JDdGRMlepwBgDYdCRCmR3dzeOj48LP2kMnX8MsnOrAtshWWNmca2/YxBsnzch2khxKFbw6yJC1TH3Uvgk+EQvmriapGKqhEHuCF0EDYCUgtqke8/PzzvQlK9dYHxFykl+vcq5vr6O169fx/b2djx9+rRkhqqMvqXCh6KNzO+lL846mU5NmKy0aPGSvPF4Dl0v1q96famQ/RMS1HOZEt1EsdYSuFSO6qHyE6kvWqEjmhSa4D4dxYukaHZ2duLp06fx85//vLOyJ1dO+7WI7rQkzsC+iGeoiJfqo9wd8swDtJu6c320cR7H+yQ2cJOyXYhcwGj5FewT5NSgRdwyfrlcdtLAqfXpp+t5+p9NcxuvaNu2BP8mk0kcHByUCeBoSkhlf38/Xrx4cU+xsD/OK0cz5AMnMZVYRnr24uKiuG1y5aQUF4tFURxy0ZjlqtwPKl/57BFRoDah9WQyidlsVnijnaI+9mpP5sLpmiOQPkVSQxoZlHc+USay8oQOiEZ0//X1dfziF7/oPCek53V5H7N++Zh7X3zZ1pO6XK589YTIZ0hxbBQc9ePO3oWoFR+qkGoWmv6bymY+hq8eaBJwf4CsCVcbODBsO90jKqDMAmv/x9u3b+Pt27edCaKJyui3eCXSRJVlY6BW/XRXwMfLUZr4RGGVJSXc1n3czcp0a9avduk+ITgXbvLI2yf+Zq5Ghk7HymTGVy9fYyCFyICnlCJX0TS2ui6lqnGRbK1Wq3LuhRR1xB1qy9qYtZMoqSYrlMFaf7P544oyo40Qx1DAZFNiZ10oxgoBtTGTdTToSqaRECpr02MrCnhF3Lkaet4VAtOyNSmEXvQ7T7sSbW9vx6effhpv374ty3oSOsJVtXeIb4T9hKUZT6UotOFJll/1RkQn5V5Lq4LYDHj688vlshz+s1wuO8pEE00og4qWy7O0fr5CoDKy08uyvvbJie53ZOeoQ22iC6Ox5hjomoLK6rvHguh6yGAw5qNzXbR6I3Tndel/be6Q+txe500fEsnoQSnnY/3LvnIiuhPgIUrJNa/+mJjDAKCsHiPgyjngqWAMQqldelYTSIPMXH/GVGSd2E4hDQVcxQMpO61InJ2d3RPQjH8+wZy3LlxESEINEV2DIIQhS0p3hJNe37e2tuKjjz6Kr776Kk5PTwsPxLO2bePJkyexWq3i9PS0lEF30q2qTj+T365J5n1SfzdBv0QzGfohj3UP9+BIJogwaNGlTMVr3svVFvVbipzt4yoNZZttI8rJMn7VfmUq11ycjI8cwxr1Ko5MQYxRGAxwZWV5QC4rc0ggqDAytEIorgFVvbSGqkPCKYZpAi0Wi7i8vCzCo/qyvmjAeS4pB0cnkpFHW1tb8cknn5SJpecyBeBt9nM8M1grl2a9XhclmVGWQ0IEwLKJHJqmiWfPnpXVKx+3prlLw+dk9THjc4wleb9qijSTVb+XE4PPZkSjpHF1HsgoUa7Eb6LHtm07br4Ux3K5LMaEeRsKmLr7GBFF0VxeXsZ0Oo3lchlv3rwpMk6jN5/P4+nTp+UdyEKJz549u4ewWAeVYY2atkcT7O3tVTVVH9OHEImf1pQFyFgPiVaT91AohDSo2RWplsbXEX+LxaIc+ScLoD0AjIDLZdEZC5oIel7nh2xtbcX+/n5BI0RoUj6KvqsO90WzPovfPilrfPKxkMCqj7Lo7KuCmNqXoeVnKTMKtHz22WwW3/nOd+Lly5dxdHRUjilU0JgKgDsy6eqRB5nBcEGmD+6Zk/6cyyqXyN140ehwE6Pq80Aok9OYg0K3WbKiYx64bYFBePKBAW5OcC5TOw9ccUTcvjS+aZo4PT2Njz/+uBxg/dVXX8WzZ8/uyRtjUhFR3TQY8YAEsCHywecEV+c5oTYh18K87i6UC6IGQUukGnwJOOE/YanK0kDTErkCI/TN8geYF0ErmQk+76mhMz0jgc4svlsW98MzYv/Zx9Vq1clp0OR7/vx5J2bjfFAynXYfE4Jz6bWGMr1vDB5nqwUiTiT+kXdN05RkvpOTk05Qkys/WnlTQFrt0j0aAy6rcnKzD56uTveYsSrx0g0xZZ19p6Jpmru3za9Wqzg+Po5nz57FYrGI+Xwe0+m0c1ZvVm4fvXfFEdG1jJmrI+aJyMQxLoqXy/uZfCPLIj9TjOXBsXqGUDSD6HqeOReKdVxdXZWArJ6h5nelRGHy/vC3LEjovHBftMY7V6gKBKu96/W68xoA9lvPMs7B7E7tFmUglWnUQi/sgyaL+kwrSmWaTZrlctnJn5DSyvqcIVNOjvV6HScnJwX2Hx8flz0plBUu41NJaHKrPVqSV78lN0JiOiembdtyJMPOzk7ZcKeANJWRzyf2wSc++6zs1uVyWdwVof2MZwyKvtfgqA9K7XMNdnNyeOfV0LEohIzMtKPqYD6GJq7cDk366+vrmM1mRZHoXgaJBFE/+uijuLm53QnLvAUJiqOVTBH2afQs4JlBd5ZPxaS+13im9kk4NQH0u3hAd0yJS+KBUs7ptuh3LmGrbF+a9eXBLLDn/afsbG9vx/7+fqzX6zg/P+8oN/LV0UamsFW/JpLKVXCYyX6+WuRjIFdEyIzGUH9SqKpXE5lBfJ5Bmo1hjbxfUhxCH01zu2lPy8Hc/s90giG0ETHyBDAfyExo+V3XiCpoRTIrmUXX+9oUcYcuvF2Kb2xtbZXAHMtmlh3z+xnpdzcjohuMlICw/exPNqG9D4SltcHyyeP/aZEy/mXlZoonc8PUbvJLzxFa814fR/GYSkP3KljHU+4dGbBMre4o5vT69evY2dnpnI3ifVbfpLz4nl7yt23bsgNacarZbBYvXryIiLsXe1GO+aoDxie4d4TWX/Uxp0h8oFGj0q3NvYwcma3X69jb24ujo6OOjPDwJJatMsYs445OAMs0LAWMVlZQzZ+nFh2LLJwy6+uTRm3xczekFBTMjIiCEq6uruL8/LyzXKvB0zUtEwptfOMb3yhvS1OS0Hq97qzBq+9sK310fVadfg+tJHnPGIQH+ni/f1bZ4o1cLAm0FCNRgn7TxNUqjYKn2jDH1RDVIwFmLEJ/n3zySXzzm9/suIouJ7XxPzw8jKurqxLAq8kTeek8zJSt5OPk5CQWi0V5744bH4/VMa4j94bKnWPOuSO+UVHIBa7JekauUNQWHX2ga3xlht/vSqqPNjpz1C0jIVxE3NNi7p+yTEcK2WrJUEcyxqo8+dlSHFIQEXfHFkoZCD3ofk0Manz68pqsH330URkYXefuSU1C7mfJ+uR9ZR5KNg41cgVFoa7xSJZcbdd1JblJyWglSGPOlSiNHSen6ppOp8XiaRzU31evXpWJ6RON/CLC4mqFn+SWITe6BVwZ4TI6ea12K+g7m83KW/0iouNSMKdHgdjJZBKffvppQUXPnj3rnBnrKEoISuPgCM4p4xPHleVomTfiTtb5ojCXB47fkPIY9e5YIgmnPr+aWphJMA6VVA99XxeAzIK7D+sCQGvNgZUPTmbT7SEqohXgZ00o3ee7Ij0gmyncPt+ek0W/udtCvrDdPjaZAvIt/RFd15ITlQhC99M/9gmo50RKUnKLeXl5WQ7YJe9raMl5xFWhTLk6YmM9zl+S2iiD0DRNQZPKDp5MJvdiOxG3S5hfffVV7O/vx1dffVUM2NXVVXHN2Ecd+8etEGq7IyPvP5Xf9vZ2WTGhUhVylHF8/fr1vZUanwtjDNVgApj7rC7oWcd8kFRO9tb5jGoNz57LylOds9kslstlByVod2dElNOp2rYtUW0mr+leCaAUnpSPskClOBhE9FUC8oJKUgPbtneuVS2I16c8fHyGFK+CnlKOcud0L5cVdZ9iBHLHmPUoRUlkFxElqu/vhHEl1TfG3MchV4IKTdf5rCYLeebuXZ+yEX9ZB+vxAD/3/BwdHcXJyUl5mx9dQ7VNc0JuhPooZSSUWkPuatNkcrtxcHd3Ny4uLuL169edNqu/NIxubGSAM8+hRr2Kg8sztcnOSijoDIhlDc4sSR9M8kmj/27FeL+En5vX6E/r3FAxj4FRWYCmaWJ/fz9Wq1VZ91Y7CQXV57a9e48FrWhNk6suWaaMJ5nSFu/Jg0yBRkQRrMPDw04QkzxS+4XGPL9Cv7vbQ8UjtKXfdI1ZpYT43kciFSlq9pU8ZpA6czkyRcox8XJ1nWhRwXDuweHYuGKKiGLxGTDd2toqCWVUXFzN0methNTmgvgk9N62t1sZ3rx500GQQytX6sP+/n4cHR11eJjd6zQqxuHKgZ3IqGnu9oi4VfTJ4OW5lc1+q6EWv8bApnIUmGMgq8l4iPxAte3m5iaOjo7uvW5R/rqUjdqslGNZZhGzJMlLPce9LeyPLGDNQtYUJ2l/f79zNCKtDBEWlSvhNFdQfMVAqIy81fi7cmDGMIOt7tpQoTmKEA+J7Nw4MbCcGSRHDFRyWraM6L7Cg7zy1UEu+WtXNdsjl4HxEfGDuRoqi8iVBpiJaUTFDD77ahf7Ld6yT3qHjxufIXelV3FkcQj/XrNyNStZUw5kck0x8Dm6UJlFl0aX4tARgEzk2dvbK4PO08m510DJMlwh0J9WGHjmh1ykvqU0WhrVUUNcNQXp/a2NQ0TEmzdvCk/0m3ig1002TXMvZiMBZRBQxPNCmbvhL12OiILUGJx0NMaEObaVfFPbPFbhiFbEfR3ON9XrPOWE4/It0aojcXeHPPmQc0G5Q94X9dMNhO6JuNsLRQWhIwiFQIgUxxDv60O3ToMxjqwCr7hW2ZDrwc/O8E3JUYwmNy2L2iJXRUG7iG703ffScGlMZWqySONT2XhQzlEXhYGwmwJEyvjR56LwHlfSmhzqo0gKU7yQQqXrISUZ0Y07qA5NVLXXLa2+k5eeYZu5GPxcMzpOmlyOnsgDuqaMAeh58eXy8rK4tPP5vIy5XAomD8qIeEKgkroUIJWxEvKg60EZcTlyRMGlVirToYmf8WuTZ34tKedOHLRsDwfJ4eXY8rNO6xwIwkdODrkUEVFegqyAacTdaxL1Wahke3u7BL64QsMDZj/99NOIiHKqNSexH/gi4aohDe+bx50ySB9R33Z/c3NT8gckoDc3N2Xfju6TQszS8aUopXDOzs466dRchVosFp3gqBuNDMmyzQq6KnY0xj1j2XTNvB8+MclLrQQxr4LxGSkeoVryjRmiKo+yT+PB818lj3KXdW82H9wA+Tjze40/myoY0YPeq0KqVdyHUPxZQkjC17FlZr8TdazXt6nJDuPOzs46mX+0Du5OnJ2ddWCrTirn8pmEbLValYCqiEtki8UivvWtb3WWsv1cBn5mpH/shFFb+Bz5qv5x8576LZ9ZE0V/2gHsrwmQsGuSCk5H3B1Q7BBb99fkgf1o27v3otZiPXxOxJiNKw1+duXB5UquxHGlhkiBLr2+60/fGXTm81JISlCUwu1banYUO5YfGdVQ3BD1bqvXG+X7Ct1EcWSuj/+n35dpTb9G68F6KBSClvThZSHok+u6rIUssj7zXpbLvvLQFyohDhAnNiedow66bZmFJA8IcXWf5zCQP2qzr1Y4P2nhM/9biE5QnLkuPo4qj+NXczvIJ05iyou7n3IrGMTN3F8pSvWNwUVvH+/XdTcWbB/dWrq/RD9SIJQV9YnnkVJufMWNcb3aXOqjDMX6tdpb4iL+klwVp6yj2Wfez8kxhjGyoOv13ftCIu4s//X1dUEF+h5xGzCVC3N2dhbr9W1+w/7+fkETirwzys/NX7PZrCQMucBzYnjA1dtPYfEVGmWmugKl1ZS1ZOajeKiJTkXJTFDFQDiR6LNrD5B4S+TGdgqdRUSJBTCuRIXlroLqylb1qPwZM8kmsXite3n6me83ctSbxRkcqbDfRDYeLKaScUWifvkKnVCfyxD76XLVR5mhp+yMpVGZow+hoQnuFtifzep312aIJJTa/dq2dysYDIZp8KQYFL9gP3wZTAOqCS4B0U5HTnr3RVWvgqIU2Bov+RtdCf2eWXhZRz93I1veJQRXWwSlpRTFi2wJV8+xHeIt/X/yNLPuWd/7iHJGHnLJ1Cdcxlvep7bJzeAxhpQNnuhORauYBbfGyyX0JVhfTaI8qo4MZXv7M8M0ltiOMfMq4h0Rx9hKapQpDZbtbssmHYuIe+duMCuTVkXBN0FFujj6XT4+oab+e+BNrxNwqC1iFN8ncKZE2daMb0QZeiYrS7+1bVtORBMCoNVX27Q6QIsriK1cAwZBqZBUFneQuouUGY5sEmQBc6IK7x/LYVwr44vGmvErZnYSWcoI+VhdXV2V7FqlqUt5yB1lvEXyKEQrVLher4ui4Osl6BoRfVHm1D5u69hkrmyC5iM2UBwPaURfQ/omDBWEW0dvh37LltzkfzdNUw4y0WqChEWCouBXRHRe0ai2acs1lyF5GAqFkslf3kctaWb+v1tG8iDzSako3AeukSYTA5s+SSaTSQdlcElTCmU2mxXe6l4pW8Luq6urEgvK3IFsqdnbq/7qPw8bIu8j7r+TpQbx9YzHZoS0VHfbtsUV0zW6c7zuS7BsJ2NJfJ6yQCXGMzpcJniUgMvQ2G0dTpsojYiRwdEaudXIrEXtudqk03e3sG7BCUkjIrU8EmwJu+6j0FIQVKYmAetQG/i+1QxCalAd8kfcF9IaIiCftAdGO1RVry/1bUocr2wSqn0ejGUyGC0iU8UlF5wkOvRZgq2J8+mnn5YXU2UCz/ZprDk+DtvZdj6j32uGx/vMDFm6WkSpnuI9m81ib2+vbCTTPeQhx008YrlN03RQjeRWRzV4PIV80jXJ9pBSJmVypByRjN5pObYm+H3CrEnsMLXvObd6mTBkwUVCRrorEmC++5NvYpPV1O7NiLtlNsUzhF40mbTvRZZHbdBvFPpM4bD/FHwqZxeUmvKp/cbvrnCp9LlMSFeMKwaKnTRN00EfvlSr8qVMnTfalFUbd98MR4PA6+ItV7EoEzX7SETgbqx4yLiWjAPL1+ezs7M4OjrqnLGh31Q+c2wkazrzVO4M+0fj5cu5ukb0Sjd6LGWIdojeKcbhrsGY+wk9s0h0DbE4IxnRdpil7zydWy4CA6Z7e3sd1OG+Oie7a3yf2Bxwf20B++TIidZI/9nH2WwWp6enqWLkZ/KP9WWukIQ0ImK5XN6Dva5g1Gb54nRNVB4Vq3bEaoK5m8i+a3XGx1i/e8p2xgNHveQJ+0P04OVk46HJTllQzIKTlue38nCjbG8Ky5PsCpV4/1UfFQHH0OcMlUfGLyf9Lje971Rzp3cOjnICuZ/tkzmzEplQiKFeRla3l8fPvoHIme4CpwnApUxHA1yNYRsI3WltssEl1fIdIm4FXS9vGlIGfYgtUzK+qhRxl61IhEEB138qAF9OpSKVhWXaubtGruiydjtfMmSWPZPxg211I+T3qc26j6srEdHZxbpYLAoKk9XncYFCuTJi5LPu1T4WyVBE3ENjfXKf8apGTdPE3t5eRETv+3Zq9GDFkcHmofv0nfc6cqCPlq0kZGvqWRu4dDaZTEpehTL0VKdcGR5gzPeLqEyd6DSfz+Po6KgExrjsp/v8DEwNChWW2jwU1KRi0oQcy2vyiPxt27YsT9MVId8l/Ixn6H4JO62nxobuDM8X4YRVW13piEfkl3Ji2Bfyj0aG/NJn5wlXTdQOtcWNBPfqMBDsMiw5kvxw34n4JD5ypYoKWbI0n887756tGSAibV/udl7U0LsOeq65in30TqecZ5NjLPXFK2p1ZPVRAIggKOQ65i6ie07I5eVlZ1IoEs7B4X8tc3G5ksos4u50cEdMPmlq95D0XZOZlCkPKgnyiQjIg8J654me90npyk48Inxn2ZoUjuboLrgB4ISVInOXguX5kuRYK8tn9vb2yulbNCRergwL231xcVFiE3ztg9AYlUuGVMU/rhDN5/M4OzvroHcPcLr8ZDLE9g8R85I2pfcW48ioBhX1G3MAapPIYVkNsme/64+QO6Ibs4i4mwxaxeDZEhF327N5TZOEE6pt706pzg6h8TZyMji/MkWoP0/WIrnSUH95TcIpC6g2esyJVtEnt094TQhNLioGTjoqdfaTY8E+iJ/6XWPApCuWIf5kK1dexu7ubnkJE3/3eANRlsZZwXWe3SIF4waQiEMvA5NCl6JVrEdt8aB1zYhmBljElSFSzVBtQu+8quInevfd6zAy+51MzxQT3QK/h0IrRKGcDPmMWv1QMpeucamQwqFrDGwJeTD4pTLUPl8uzJREDU2xX1RMjIX0uYDaserlM69Fk0dnpbKNivUQibn1JPqiQpGfLyXcNE3HZaGyrfVZPNSzXInSfXI7PQ5BJOh8p/I6OTmJt2/flkklF4bIQPEIXdPk5+qI+K226lm6Q3KPiXapnNkvxpecXGa8f06MT2W0KVIjvZPiELPYyUwh1J51xnHS9ZELkb5zwkrY3Yfn8wzo+lIXB1iZkszm4ySgtWXbfJlLgksrrX4P9ZOkCUpFwj55QDLiTpkwwUuBOCkKuS1sl8pyVOg7RXWvEqC4LOtvuaO7JN7TYjJzk/1UmzRubXt7bJ4ruL4JwbFlbEHXpCS0jK5n9N1XkKQoqWDEw+l0WlbYTk9Pi2FRcJl7pKiUKGOZgdjEUL+LcuijdwqOqlGZrx9RXwnwchjccajbVz/J75fA6zfVr4i2IPDNzd1bvDVYaocsmmCxzpxQ/RLera3bMylVLhNnqJyGXC32zdHJEPqS8GuSK3eEwkdrNpvNOlmjUji+vC2lyGVHJbnRbYu4QwJ6TnCeKISKw3MOiH7ULo2lB4UV8NYSImVI7ldmxDxuQzlSIJMKQG3locK+A5aJfZr8enF3xG0QkmiPilbBdClcHpREA+O87ZMfp1+H8ninzFGHnLXv2W81ynzEtOFWhy+xOprxlQ1d04SQ9SSslnVRHRIMn4ge6Mv6nkFGVw5OmcVwGMzrNX7zT5ZNZTAvgYFJJiOR+HpCIjuH30ILzH+hElUbpZRkoaWQWTaDk5x0tO5c7mXmZY3ofvh4EU0y10Ira1kwVXyUkvNxEF/39vbK2TBUCCqT6eY8DpBlesCan8mjmmzo2hD1ZY5uhDio6Qj51GARP3sHa+WKPOqfTRASEUJ2nw+yBolKhYhAv3kbfDWAdVDAaE15TzZ4mypWVwC0QrJyQhqa/OyTymawWHEc9oOT25fq6BKI3BJy0kvR6BrvpevmAUnVVVME7u5IifSt7mXyKlThY0nkI/eCK3Bqu8ZdE9zdC/2uU/P1FkBOfiI9vQBKxsvliO2T7KssHhFQm5sR3ZeH+9iOpY0UByvziU3tyes+Ifomhk+kmhV2t8Shve4T0xeLRREqaXO9wZtwWOUyeh4RZecjg6WLxaLkfMhV29rait3d3XI4r9o1nU5LOjT7OWagMoQhV0P5DQycctlZPOG5Gjp05+rqqrhgnHRCWVI6RF905bjixLFXnxj3oouk+ApXqhwteszDSQpProArP/FN5Mlqbtj8filPR1su00SRtXlBg8KDe9z4iv+MdfQtlTqv+uYXeeKuc4aQx9CDg6NZ1JcNUMeyQao955A3o5pvl7kBCgByg5uIqyK6j4MrQdfk4oDyJU0SegbH3OpoZy37WBuoGt8Ily8vL+P4+LjEGaQQmckocoul71qt4LIfeeuxDt6ToUDGkRhvEUlBUPFTCXladfan31THxcXFvaB0FlCsIcVaXSRZfb3qk/0iytB+KKItVwLMLia/tHrDsdjb2ysuE90j9qE2t2ry4+7LGHelRg8KjmaVEr7xPv3fRKMNdYguQd89CoQyU08p5WozB5AuDK2olIn7liyj5oo4rPff+/ouBZa5C/6MQ9MM8XHcPPim4BwnwmQyKSentW1b0Ap3CJMHmjhqr08W8cODi2zvGOPi1zgZacFJikEMkSY826+VIa2s8EXdEVEQlPI6mqaJw8PD8qxk4OzsLCaTSdkfpHYqeMo8D42BH2+QxU+GEDiNG4/P9Gc2oY1dFcL5TSoe0zDXiB5Q9EmVfSZJEfBgHQoBYTVTzWVVVJ/S1RlPiLg7AYr3a6CzpcHMpdqER5ogvkpTUw7u7nECLxaLTko9f9MzWnFSfX7uKlcv9JzGTHCfy4uE11R0/L0Gz/uUbAa3HT15oG8IuXLPkhQl200+S8Hs7+/H17/+9Xj+/HmJaVCeIqKzi1oGScFpXROC1MrfkAxRLpyvvM/PMRHPHoI+NlpVadu2QKq+iGtmEXm9zw/jd19O9PKyZ2tle5DUtbEYSDfFX1CkicP9BjUB1CRguewX6+6jTHFmvNLv2UoSn2UZjrD0DCe3uxcew4q4E0jGQRhXkYXmqo1nDXMSbrJ3gu5iLUMy459PMI4llYUm8Pb23WszOH4uN1KauodZyn7IDnkiRaN6JHO+yzr7zLGtIV/J9GeffRYvXrwo17PxFPXtlt14VSU7wzK7Tw3z65u6LG5BvY6h593q9rWRfi7zGiKiLLUtFoty5JtPvhpffLA34UGN+lBLDdIThvMeKlOhBF3XNcLuLPtT92kScqXAt41Tqao+xgJ8zLJx97b7Eip5McQz5xH70rZ3mahSCkRObKvkwRWIZEft4+oHk8n0DJFGhh4zJFlDT1zlu7m5iWfPnnWUBV31TejXcsp536QeC4mG7qtZ+TFops9yc3mN1yKi+KicNERFhOx6lt9pacbQpvBRdVJomWMS0Q2WciWp1lbCc1pVJXZpuZW81DOeAaokKKKavgnSZ2jcFfElYSrrGl/7lBOVAAO7/D2bvETKGRrKFJxW+HQfV7RYJ1GZo6UayiDaIJ8Y65tMJuXl02NplOLIfKI+LfUQDZaVmbkotbIpKDU0QYHxwKrDwCwIRX/Thduf7+tnrX/Zfa6ExpTvk7FWlywrYxGE345eONldyFkHj8tz2K4J6W10FzKblN4HV4qu/GqU3UNlR2VJi+1tEmKQa+EImW2k0VDQmfumVB9fWK44EhVDZpg89patTnl/pfBF2ilMo9BHD0IcvsT3vqhmAWr3+PUhxaNrhHAR3RfmZPX7xHGBHdunoT5kv2fKj33lvbLofMmyl5NZrkzJ0KVxhBAR6dJv1laWT0H29kuJZagnMwZD8JwKNKOai0Ees0zCe/0p3hXRTTDU75mhkbLVeOmanmW8iMHrTDn6+Pq9QwaeypDpAmNWn3oVh6Am96Iwsv7/azQUbHTmizRQng6dTU6/nt3z6yC3OD4ptRGPAjC2TEdgsjqz2SwODw9Hwf5a2RH3XbMMEbFv+rxJPziRKKdZzCRrZzYRM1SZuTHOhwyVO2LJFhdUR3YsQM31JHmg009R528uTx4cHpLnXsVxc3N3pJmURy2tNWOC7mMD/zJpyBVwIYjoDrALRSZIY1ZGNqWhyeK81CY7WYsMCWRCE3E/3kNBms/nnWVX3u+fx7a3zyq60nCqyZy3g6euZ5TJJIO+NYTjzzKhMHN9s74PyaSQhpL7fKykPGo84Dj3KeiM+lwbp94o3Xq9LseY1Sryz75EmUGoXwdlk6I26P4ctWyWwJZZpQzOksZay4eSW7Ozs7NyKE3N1WH/+Nmtu/IeJpNJ7O7uPlgxZkjN+cJrfa4F7x1DmZvgn7P2eoyHRkJtIMk99H0sXp/LS1+7Ly4uShnams+6m6YpS7zafsD61S7W2ye7Klv9H2MMexXHJlBccJlnVDhs2kQAs8723efXap13y9sn0EPtqlnIX7fScNKSXy1vQ39+bgpXNZyurq7i5cuXo0++fsiE9wnJ7++K4oZck0xZyZoz27XPIKmexWKRHmyd3b8JTSaT+MY3vhF7e3sdRa+lVSoJLgNrb5TaJ2Ss78pu9dVA/T5GyY0Kjm4Ct3gUnToZEeXsh3cRiLHP9iW1eHn6q0X1h4SAvz90Tfyh5MhO1zI4H3F/5aKP5PYMoQDVmZEvG0bcXwnJyvh1u7WZK8f2ZufDtO1dVieXZ9u27Wx09Pa7S8bnhmhnZyeOj487CIQypn0sOqtUdShTmjLMdHPGSnxFRmW8k+LIOjmkNKjdxj43hmqToY9qDOBgZlqXdfKZ7B7P1BzD9F/HxMiUiK7rGpO7xtDQ7sys3sxC+3O1/tf68OsklyuNoee46LdsBc432rlrJLTHQ4pVbl+fLy4uek94VzsZBPVEO5HK8QxguihCYGPm2sZ5HH3UN+jvOlEeIkg+gJliEMSs+cS1SSgiutF1vvKx5u9mG5Z+HfBcguTLhmPK8UnubRyC8bonE/yMMgX0l0FZn/x0s4huGgInYVZWZmAyt0DUp2izclXO6elpp5xsyzxRheQumxubGPpexTFmWYZUsyLvgzbxfWt7SHjNT/KqDRDrl0ALynKTFwfKz4bwuof87015NrbcMZQpQpbhk5tuXkZ9bar91uduvW/qG/dsqVX30FpnClaka9rnwuf1uU9OM6In4H983lMn/B4G0scYFFKv4vh1aP13FYghrdgX2yAikOYdm/fAwfLJQiFw5FKz0LWl3k1IMJU+sFOmFGukBCTlQYi8bE8Vp/D5tT7qUx594zHkDj2EaijZtxR4/a5AMmKmrEjuxVhLn6FcTyPQ87rGZXTV0xf/YxlDLu2DYxwPHTRafE+4GiNsffe4paoNiCbuGIWRlU3k4QNas2AUsPehPCXUyj9Q/RHdAFrNatYoi0+pX337Xtge8WYMynpfNNbN3LTM2rjxmlxepuBnMsjysqBz5o5kfXTkQEVA+ayly7tMPkQWexUHGyqhkabMSGclKm22j3jGQ0T+jky2QZSlE9eExX8Xo9wPrFmbjPomg9qVKZmsfZsIM/1TTwwS8mAKNFFRBrWz65kfnu0HiYjOCkNtAmTf34cCySZUNrHfVWn5+LEOBjm1Sezq6irOzs7uBZUpq67Qa+3zCe39yhRGVgbLz+51+RyLFkeF2KW9PC3bibn1Y4m5+7W6+XnsRB/TjmxiD5Wb/V5rF4XMj8Zj/WPaurOzE5988kl5C7zq1Kas8/Pzey+UGiLWnwkqoW1Ed8Oa6lAOQ8RdqrdcwbFCONSWIWqaJpbLZfzGb/zG6GfGlhvR3ctEa056+/Zt512zpBo6GEPkRTY+VCKs4yG0ids8epObw9OMdIIz4d1QmX3uhF9T/Zkm9YnL53yJin3w3Yt0pdhfwsysDq+HZfF7tm7e11/RarWK4+PjcuiLiC4XXZisjRSyLPCduSDsW+YfX1xcFH5t2qdavQ9J47+4uIgXL168kwtYa5d/jri/WhRxl+o+tg218mv5Nn2y9ZdNg4ojg341qimVPmg/tn62YUjDZho+g8u0HE1zm1GnieCp57S2tfZzidX77FvJ/b/uzRRUxK2CODk5iaa5TeaZz+fRtu29N7BlyqqPp36t9rz8+FreSjaWNXSYEX3xh7hzQl01gzWmzJrr2zTNvfNTeV+GyLJynPrGqw9VO883RWjvg0bncYzxGYfchTEoZFOqJZtlf3wm8+145mhWnj7rf3auBNviVqRvcHU8nYSfz3vZOl5PJ0v5JrS+esaiABJ56CdZ+d6YrK4x7uBDYHxWjn+mu+UTL2trrbyIu41wEdE5jMcNVVa+j4svPPD32hjpmdqiRW3c+8aHxnOTJdl3OgHM4Svh77towLFlKO7i7kdNaXBQ3Uf1QcwUjXaM8pWGjohqCiK7Rt75WZt9fde9eu5deJ0hOpJPOCoL5/mYdtQsv5SSX39IP7LvQ/c7sT9UCFIeilnpwOGdnZ2SvelvcxP5psIh5TIGPW1izGtj+xCDPrjJrQanOFllBelrvwvVyuhDERHdg2v9/tq6NN9Zoef1rPx2vXxpe3s7Pv30044/T+VFCDmGD2x/LZ15E3SX/ZEPGXlds9ms83Jtv0ffM4Xl7p0LrWRm02SjseQWe0z/x5TFcjyHRYdYc6Nbbex5fUjGa4bGZYR9kyz+Oo56cBo8yIeZZ0NwU/9/nT5XBh89IFuDoT4ouv/Jkyfx+vXrjnDrv1YwIiI++uij8oq+vb29Uv719XUcHR11NDctch8CoSUTeZCRbe0jL5PoasgI0NrXkoQyJMazWZumKcvCfJ+Iu2xZWQ+lTMmSp25VN7WuvrfDDZFenTG0IXCT+fC+eNNXv7tNm1Kv4ug76Svzsx4CeZyG3BQKgMO0zDXJyuIEuri4iJ2dnXj69GkcHh6W9XnB0IODg7i8vIynT5/Gd7/73Tg8PIxPPvkkdqazWH3623Ex/zjmr38Ws5/+03j96qtynL2EaxPrOiQwQ9A1U5AZn1iOC43aT/SV9SFrB9FElutTQwB9Qj1GnrL+uXGoycxQvzLZiYh7sZ3sQGNHoqy/b9k2k+u+tvT1o2/sRQ9BgKNTzr3RtYDT+0IaNetYs8K0qp7LT4XiQtk0TZycnMRisYgvvvgi3rx5E4vFovzpqPovvviiPLu9M42ffPu/GmeLT6OdbEfz7VXs/JU/io/+3v88nn35q067/HTpsX2vUSYI/F/jYTaRdI/89IhI801qcrCJoSAC4D6h94lMVVZf4lvf5HN0wmckX56fQr76GGeB84i7k8752gW2sS9jW//17mDWmSFFyn5fvpTT0LiODo5mA10TnKZpiqXW8uamlJXtwu+DWkMbjIbXGPLq1av4/PPP4w//8A/ja1/7Whw8fhr/ovmN+Nn5TnwcR/FvPTmNt29exatXr+Ivms/jbPl5tJNb9rVb07ja/yIW3/zd2PnqZTm1WvW/r8nB/vK7+pglXdFlyvjk5dNq6j+zdTPly3t5zYlH8419po/60Bfv8ThUDXWMseiuHNwNzMY742sfItW9vl2AaCU7HzdzxTY16GPHYVQeBydjLe/daWdnJ/7KX/kr8Sd/8ifvNHG8fv/vPmeGOmTldEKZfFLBTVndy8vL+Oijj+Lf+v2/Hv+TP38U5+vbel7E4/j+mza+PXkdv3qyjLPJ8n5Dt3biavfTe4Gp7HMNRvq1mouV8TNTjI442rZ7TqZ+d4RBRVFrp777Sd3ej6zvPi5jM10zyhSXB2l3dnZid3e3c/ByrZ1epvOvhkrGtlX31t7hGnH3egmXF/6eleno31f9nPdDbeyjQcXBnIexDGrbNt68edPxA8fA2iELkN3vB8xS+Nu2LclSWhnhDlBq+08//TT++I//OP7oj/4o/sNfbMX5uvtymlU08cP1xxGTiMnNdawnWxEN4gOrq1i9/Elnq733m26VfuOkyTIna4qkD8Y6cQL4eQycBFluQWYo+tqkiZUly/nY1PoxlrJJkZWj4HXNhcnKVB/YJ11n/zimDKr3Kai+uVRb4q6lHfCzB569zixYTBqSI9LGMY6xk79t2zg8PEzL6rs2RNyR6hDUaXd3N5bLZUwmk/IW8Ihb/3BreyeWv/nX45u/+zfitz/bja+1r+MP/8bfiH/87Cb+zvd/ldTcxn5zGX/96k/j5Bffjz/7+I/ifPnZrbtycx3x6idx/uN/VI1lcCCVhRhxfwOUT1Yf4LE8c8HhblXVzd2Tuq572EYiOrVJbRT/pZSHzkJxq+1nY27aT69D7eNnrrz1levKwRMF+az4oaV5Ho1Jl877xFiJKxgiaP0p5pQpdfa1pow4V/hi9HfxAiKi/6XT+/v79xpQ03i1a30N5EAO3eef27YtG6qoacWc5XIZ+/v75YXHtDjzxTLO/vP/vVg/+Wasm+2YbjexP53ExaqNk6taELON39t5Fr/x9k/jpz/9abw5fBuvZp/H5eLjuHz+4zj8F//PmPyrtmXnINAS1WAuBZTnXtZ4oe9jlDongw6V2dvbKxNXhxLP5/MOGtNp59PpNNr21ldXhu329nZMp9OIuHsLu8ZE9enVhnw3rIT45uamunL3EMWRobxNJgj5z/NI6DpIMUhRbm9vd14OrT5SMZKfdKlpIPz9L1IU4k8tyM45xHnAFTEhbymi2lELrljPz8+rvBq1rb6myZ3eZbBr5JDa0YYEMOKWuQcHB3FwcFAGfnd3N05PT+Ngbzf+8JOT+K398/iXzTfiP3z0Rdw0t/dcrtq4XN2W8VsfzeLf/c3d+N/+kzdxet1GxG19O+0qpj/8+/G9Z7+Kk5OTuLi4iNPjn9z+Pz3tvMG+xhO3GjWl4WU4LK4p0to9+o3tk0DzdY3Kv6DyuLq6Kofi0gpL+UihRERBdcvlsiBD7djd2dmJ2WzWSVDKXGC6nkNK0/unz5nxql2rybSUwmQyifPz87K5cDabRdPcvsXt6uoqJpNJHBwclFc66l69IEurJ5eXlx0+tm1bFLBeb0BELMWi/BDynmM9xBP9TmNWy2zdhEYpjozcV6pFiGsQqibw2T26zyfMZDKJjz/+uGxp/uyzz+Lb3/52sYK7u7txcXERJ0dv4z/47j+Jf/PgLOZbbZyv/2n8N+J/Fv+t6/9RrJE8++9+axr//f/C07i5uYn/4teexP/mP/0yvvfsLPYuXsRvXvx/4kfPv4yzs7NYrVZxcnISx8fHBab7blq22xWehCLbmEb47lauxpc+coTTNE0Rct4zm82KxZRCmU6nsVwuy6SRtdLx+0ISFxcXnT0scnkmk7uXCzlfVFa2rF/r11hk5d8ziN9XpreHqIDPyJqvVqvO6z/0/MHBQTnuQPfyPsmAtjKI7+fn5zGbzdK2eNtrfKIL40FS8iXr8xja6PUIXhmh1mw2qx5hlymAsRCbZfjgrlar2Nvbi29961txcnIS3/3ud+Ob3/xmeYlN27Zx/OZl/Dvtz+P3Zqex9a8e391axe+1P4x/Z/JP4++tfz8iIubbTXy6ehH/4B/887i8vIwvv/wyzl++jL1nz+KHP/xh/Hw2i/l8HhG3E/o73/lOvHz5Mp49e9ZxQXiIkbso7HdmbZ0YM8gi5DX+ZAIlhaTJvlgsOjkbk8nty5cEtdVm3afn1+t1TKfT2NvbK/1VXx4/flyQl1atlAvjk62vz0PkKCFDFKRsKVZEV9frEBpbLBb3zjpRqrmUiSz6crksn3d2duL8/LygDylmlb9YLO69sFrIQ3LkSINzZtM4RY1HmyqNiHfY5OZWUu92GKJM4H1ScMKIHN5ubW3F7u5uPHr0KH7/938/ri9P47/29EfxG+334ldbvxE/3/p2/ObFP4nfmv1ZzOP+ezrncRn/Zvuj+Hvt78XOpI1v70/i5pffi//d3/278dVXX8XFxUVMJrcnO11eXnbe5t22bTx//jz29/fj888/j5cvX95Tmo4yfHUi40f2e8aLIXLhousjJcAJzIg9N7DJTaGl1YSi+6SVKro/fMb97b6J3ucK8x4q5Yj+TEyPAbAcJWNRifJ3oQkpP38REg+u5uSfTCZlG4MQBRFK0zQlHuRp+75SKB4TjQ0py01oUyMeseHrEWrI46GkchyuZv/1WVBvd3c3/upf/avxt//2345H+7vxX/nn/4OY/qtgzufx/fgD1PPy5iAeT05ip7kTjItVE3/+L38Q7dX/NbYvX8eTz6bxH/3LfxG/+MUvOghBE1fb2DkJX79+3REGoZxMSTAoNmbQ6QJSYIagtiyWK2O+f0MKgb6uJj3dzra9C86xb/LtlRej+hng87wSR1tZf2t8IGWuXc11o3zVns14xXv5G1c3xFPJBCe4gslSPK7giDDYDsVGPHVdz0mx1/rOa0Py5XNsUxqVx0Efmz7wGNI5pDWISmHK4PhisSiMn06n8eTJk3j06FH8wR/8QfzNv/k341vf+lZ8/U/+pzFtz4MsaCPiF5NvxN9p/svxsnkc/831/ym+uPllzCbruLhp4p+92on/+J/8LK6ufhxPP/88/tMfH3ZQAyeOlnQZx6AVIBrJltnUl6zfpb0V3qhMP1y55pbwLei0tGoXY1FMNb+6urq3U7hpmsITBlY1GW5ubmJ3dzfm83mcnp4WhST/vG3v3nK2s7PTCQz66kMmwGMVDCdm9ptbb+d7FpcSDyaTSVkZEvJQW9U38k0rH9vb2yVuRBQn3hD56SwWvae5aZqiqHkSursrHkSPuO92DCG37PMYGszj8MKb5vakrCHFQaHntcwiOKLhM9PpNJ4+fRoHBwfx1/7aX4u/9bf+VhwfH8dv//Zvx6frZ/H4H/0Hsf+Lf5A3YmsWV/vfjfnlZfwvfvk3o/nh343f3D2NP3s1if/bj27i7Pw8Pvnkk3jz5k2cnp52/F3CUp3BoVfrLRaLmM1mcXp6GhG31rhm1Wq8odvD66Tsnog8AEsF7JZdaCDizuKtVqsSkIu4FeSLi4uYz+dFoUhxEpko4CljQOWje6Sgtre34+zsrFhKt/5ZH/jdf8/4yJhM9ruerY1JzX3k5NSOX6ELrgxxUyOVIpehlZUsXmtpVC7e9fV1QdIKWvub5GqGd4wXQHfxfdHoBDA1TAJWayihqt5r6VTz2R3CRUQcHR3dMj3a+O/+ja/Ff27+T+PV6ir2/+R/H/u//H9ERMS6mUTTrsNLfDH7TlxdXcWXX34Z/+xP/3n88peruLyMuLm5jsvLy/JOEi2VMdAna3F9fX27nHtwUCzCt771rfiN3/iN+If/8B9G27ZFcUip6rlNXDqH9MxCFF/7nh3j+siaKeCpCaFYjnxvWs29vb3Okm3b3i0frte3xw6cnp4WpMH2N01TlC7HlkpZ/fRkq4xqOQw11EIXL+NhX11UOopD0H1Ve3ifnqOb5mhNiXXuAnHeSElxM6C319FHX1/et9KIiP4EsMePH8fBwUFcX1/HyclJFVrXGk1h4KBLY5NqQtM0TcxnO/Ef/7f346/uHUdzc1kUxHoyi8N/478ez7/778dv/Uf/ndi6Pi7PXcQ8/sdHfzt+9Bc/j+9///txcnJSlgU1OBJYKTJpfymO6XQal5eXRbE8evQoTk5OiqV+9epVpy9cqsxetuP9zaAn3Qvn99DEcl4S9UkopegiohPEpGJQ/bSudFOIzJbLZVmWZgBb5cq6EsGoPlccGm/2g5/7VlyG3GCW3Qf1dZ2TXUpScqzlac/Ajbh72ZdiSIwdUYGpPB4BqSVqKloGlPk562tGmdcwlh6cAPatb30rtre348svvxz0xzNyCBtRT16i5eD3iIj/0jdu4t+YvYrJDVZemq34xe/9D+Ptt/+9ODk5iX/4b/+vY/n//l/G7ut/Hn96uIj/wy++EV++/H91lmY5IAoUaoIrA1AQ/Pz8PK6urmI2mxXLrMN6zs7OymTSJOG7TYY0vCsLChLvEWoT0tmE5+4CahzkttQsfJaPQ/emaZpOH8/Pz4sypvLRWHq6tMNmjrO3hfIw1v0bSzWUkrlSuo9jIF56hi/vzRSK5FCkADPbQtTnbWZdY/rtsva+qFdxHB0ddTarDfmKmwycP9tHf+3jdWw1BjPbdTz//j+K/+TPm3j+/Hm8fv06dnY+jx//+Cy+973vxf7+ZUyn0/jqq6/KACjHQIqCkFBoQdfVH02WyWRSgn8SAAWJJURyWfr2AnAiEBWQB0QBUnBqUx+/a5aVvzdNU/zt+XxeEIDKV34CBff6+roEAJUmPplMisui5DDmL2jSMA2dAdrJZHLvdDlvN/+Pka0+qzvmvuwerpwwDV19kYLg4cXsI48TlBz5/haWqX5KDofGdGx/hvq5KfUqjlevXnW01SYHgTyEBO/8PIt/9iLibBWxd/c6kbiO7fg7//hn8X/8J98rORe/9Vu/FYeHh3FzcxOXl5flzWYKalIQlc1Hiy/L6fcq4p1ZPyIlPV8LHNeUBn9XmWqjfOsx1sItt7s3EloiBPnvQmFcklXMgkhNWbmaNEIjcn+4F0LlC/HRbeWEyng0JhFsiBe++jdmktAtIx9lDHiUpLfTg5l0E8UX8SMi7hmEtm2LUld6viN9ytu/Tho8c1QR6xqEe58kBrtl/b//xTr+8bOIP/gsYrETcXkziX/2VRP/q//kJ3GzvhvsV69exZdffhlbW1sl14DlMHgn4dYAsW+egJMlGYknmnA1/zMbZFca/hyXdKXcRO6CZEosq0cuFZeVWT7zC7j8ywQ0KlihEF3TRi+1nytST548iZ2dnfjVr35173f2R/zM4hKbThb11yewfuN/KtYM+WjliMaFcS3yiaswzHcRWnn8+HFcXFzEarXqBODpOl5fX3fe1ldzq/r6Tup7bpNySRvtVfHB27TCzMqSqpY6JvHv/5/b+OPvNPFvf3s3/uzVJP4vf3YaN0gc056J2WzW8eHdcnFpkLCRk0ttlYCrXZx0dFEkHLRMfbC5T/mSx4xNdPiRQHk9m/IPdUjw1QcJqdwNKkDno74rcCwF4sqFylkTJkNrrph8ggz1q0ZDMtqnzPU80YN4w0OLONH1HI0eXV0aHCkNyRT53bZtJ09ERkllc0m8tvwsvpN37+LK1GhwW703clOUkfmXteXYMcSEMvcdZ7NZLBaLODo6KoFFLh/rvoj7b3HXACmBioNHi0CYKYsia62JVIs98HPfJM8CY3rGXaFskmRlk+eOOLSUqlUWCTNjFaqXzwpaR0RxYbiKwM1f6hNRWoY6qCzfh+LQd0ccLJuIU7xlMJMGT8rBVwYli0QK6of4OJlMikyqXAW+3a3yOecrdbW8Du8/21JTIjX+ZqkUokHE4Zr2fdJDlIfv6tT/9XrdiWtoYJhso0GXe3J1ddUJfDFLNSLKQG9tbcX5+XknXXs+n5dJdnl5GfP5vEB3PwXsfZIHXt3n7VNInLAM9DVNU86GZZRfk41ncXjMQMiDgU/FUQTFOQmkhLIzIRgb6HPHapT9liltf4auq3gppSE+OY+lKIlSGSPyPlM50uCJF/47FY+uuYuX9ZXy4MdEvk8atT6jDWVjiR17n5OnxjD+5sFEKQ+ebiWmZtl52sVIy0eXRKsLnCSyKBysWjv7oKOeqQVCa4LPRCGfKPyj5fd9Jtox66d+KUWa93EVQUczervpQqpO8UkKzONmD/W3vc4xZfjkzNwU9VUrZbrGQ4j8kB9dFz9odBWk51I+FQzRme6hHHs+iNNkMukEb7na5+SysSkN7lURVO/LhOzz2XWNzOWg1fy1Wluysv03b6dQAwONQgtM7qHVIUIRcQWCJIiviZG1e2hwqGiH7qXFIsLx8jLeUDHQfeMKU8RdAtjp6WkHdeiZ2WxW4hvcJcrALpU232ovXmofjOod02+2PYuZ6L8jhL64QFavDJDQKQ2I2k/3gW0TiqCMy9W7uLiI5fL2sGstfetl4qpXspQlf2WLFCIpDj/sODuRjnx6iLLuVRxcShJllo3kSiRTFqRaPKCvMxQM3idhl+ZmGvTZ2VmxkhF3qb8KpvLMCVpw9V8TxLMuZT1VJgNTm5Am2cXFxSj/vg+1ZEqDwqfcEOUSCCnJUvpklDtHoRWf9LvK5GSRYhGJj0IbhNS+61P1eD+lBPp4wnsYVJRByGSGk1TPUymTjzc3N8VVVRmUcT3Hfgk9yN31fmXB1swY1tCZxpcJeV4HUbn3aVPlMWo5dkyhPlnISFkkT3jhc24h+Ds76No9IwnNYrEoZ2kQNkqQFJ9QOxV05dFtEdGxKoKvUhxclVFwdAzCGMvHIfJJ0leGUJT6JYupmAehuSa+FBqDplruVpmsj4pTwVH2TZNuPp+XLQBCHX0BPx/zmgxwgmlMXW440TMkQgXAPSNEotyMRiSgVREu6et3yhPbQYOmFS4PgmcpEeSVyulD746q34V6FYdbzzFCTYvjWnqosdS2TAbzsvuep4VkVFhKQedG6hpzD3Z2dsr6uyYTT8AiNGfuhiYIU9tJY3moVZoxvqd+0+lSTdOU/UQUWpEUJxWd+MV4B2M+4sl0Oi07gX0Dl/67i6L4ByeoeC1fX1QLurON7qb08SSie8Bwhr7Ul0wB+YY2GkFXqm4kyHe6f1RezGAWzxlD0bOueLwfdC1VtxRIDd1rLGgYs3uGaHQeR81ndsYTcmUN6YNa/D6ZTOLx48fx4sWLDrzMOkUGSkltb2/H+fl57O/vlwxMrZ8zwCXSvhQN7OXlZcdtoWVfr2/PUCAUpzXwfmWf+d0nCC3jUPyH1ow8iOgeSqsyb25u4uzsLKbTaefs0La9PYB4sVgUJaAYhIRRbdLyYUTcW50hypT7wiVMWm0qmRr05rh7//oUK1fG9Cx5STn0PB6ONWNjcoP5vOd2yGgRvTmq8P7VZEb3cCWF/RB/yUe2zeXE5+W70IOPDiR5Y2oaLXNTMuWhZ1++fHmvjMy10T20mvyNMK5tb3MPdnd3O9bS28kUaikPKSRCPtWrPrPumgLR95pSFfUFDSnoOkgou4eThpNXKEM846lgngSn/7LiQoFCJIoVrdfr4r/v7OzEYrGI1WoVZ2dnhY+KA1E5usLUmGiy0b3KeOl99vvkaqnNvM8Vl3ggZdE0TTE8QlAySjJCfNmX3OLZbFaCoKpLBxo5WtPua/FWhskVhxP5mC3rDlGGlsbSRorDhbxmKdWIvonj5aWNqxwk20dUArIAjEVE3ArS/v5+2eMiQZeAUmgp4BQy1sXgaZ/W9747/O5TEDVyRcpJkPnEk8mkLJ9y7Bj4FWlnsJdDJSIlqv1Bmli6rmvkmfeHrpPzxg2Arm0yOVg+g501FOu8VBt02heD8GwPl+KFcMVXjQc3TTJewjgglVbE/TgK3UnOvbE8qfV1k+cfhDi8oTXl8VAi1OZ31u+fXaiowdv2Lm9Dgyalsbu7W4RJcRCuhzfNXXafB1el8aV0pKRqPKv1o49nffDSy/T7JBCeqyI+SIjFHyEGoQBNEj2jMdnb2ytBQ00SBr71jFZp3J3TUiPzGTJ+0NLSvawZkxpEj+guE6stNWTIlRYl+/H3m5ubwhuWMZlMSkyMvNFvk8nkHuIi8pBiEprxlSwfX/JrKOhZu/6XgjjYAGopCmbt/jFlqgxasYxqHXS/UWdEcomzaZpy7N/V1VU8efKkxDM0uLTWzN3QH9OGVS/Tzt3n3IQXTh6sJA1NILaPbVO5Wewh4k4hEn2JF5ky0r3qM5d4yT9Hc9mKhis976/+MiPF726Vh3JFKM80gmo7lS37Qb4q+M5NcVTWjgYdUUgB00C50lVb+Z3opGZknD/vSqMVhyZV5pMyeuwdGqI+i+nX+uAUBU6vI9QOxIi7t4xFdNOqnz9/XnxK+eRK1GmaJvb29sq2+ouLi7KcuLu7Wyz01dVV7OzsxMHBQamfguF9qRGVjbsdzpcaD7MyVa7GT+jAx4f+N8vWEqFWnNg3BsMZeKbFpELW9QxpuGuQKcoasu1zOzzOlQVJ2V+iVu6pibiLZzHLk+3VNbaVE5sBU9YvPmV88HZlbX6oUnjoc6MVR9u2sVwu4/T0NIU3D2n8GIvcZyWcyFT5mFol4GAIamoy6F4JBOEwN2qpHUQn9H+JTNjusXzJFObQZMmSebLyIu4CaEJYenesVlCapimvLby5uemc+v7kyZNyRODx8XHhi+IgFxcXxTXZ3d3tZEq27e3LhzSJ1ut1iQ/Q16cbkbWffMh4mrmwfNbPDh1DNCI6uEiurpRxdr4GlQtdQh5oJCUslMIUAncn+/rrAd+/DNrIVeHOT06KPlieDVJtQvnEcaUxhjFbW1tlAxKXzpgl2jRNJ/rvbWS+A2E1fXMJoB9Sk/GBE2ZssJeKLnNTVDYtmLeBz6h/PFmKSXlSUFKqhOXr9TqOj4+LcGslJ+IuB0Mojgcec5lT9WVjqhwY1alJmSmOsUYk46fcTB3KpOvOU11XrIIuXMTdu2tYNtEcXY2maToZuZIjIVOOr4Kn4pW3j8ZEVFMamSwO8S57pkYbIQ7tN1AlXinvdZjtcFTXahBzDETP2iitLuE9Pz/vDBhRhpSM6uFSmEfII+5WeWRxJBgSek+xZj+y1YSI+4cED/mm5N0Y982R0s7OTnkpNHfaLpfLwjs9q/fGKsh8fn5eEsJUXsTd1nHfmiBFw9cKSLEohsUVD7k43n7vk/Ohdr//puV0R6C6l7JIRcxT7HWd16gYJTckJgtGRBwcHMTR0VHZV8K211ZWOObuZo4xqGOVruYN83MyGqU4PIiVCTK/U1hrA+yNzajmutTIJxKP/Iu480957oSUB1dgIu7eXKb7CD8FMzXRGDR0V8H/htrOvrvgZH2slUnUwUOYNVk9u1XKVmMnwWSgj9m3usdXCRgHE3+V4+HL1gx0RtwdmUDZyfgp/vT1m3wUqQ2ceJmiohJp27Zk5lIeqDgiouzX0e5qoisF0zWH3rx5U+Jv/vY/GS0q1EyBDs2ZhyCzPuPlNKg4OFAZRPKgTua6PMT3eugzUgpaap3NZnF+ft6JWPP0KuUZyLePiOLGMElKz0dEOQGLlkF/fEEPqTYYmT9P5Ut46sglK9MVt1ssKQJCcCEm8YV+tepU4JMwncFPxTrEJ8WMdJ9QhtrIdtaUQ/b9XUgTVPwkf2qT0w0eUUZWtk941cVy/JBmKSSNT8TdyhYTF729/P4+iAChdhqfaNSb3Bx+92n7bNlraOBrcHusdfV2Sfgnk0k5xUyBKwX11E4mBMmyKNA3nU5jPp939qBoUilesl6vy9bzp0+f3tthOnZgdd+TJ0/KO2D82Szy7nzJFI7GT6hqtVrF7u5uURRKQrq8vIxHjx6V5+S6zWazso2emaoKgk4mk/jiiy/i008/jbdv38Zf/MVfFAWiFScpW+3HyVy6Wq5DTRGOIVeknMQ1eWK9jAkJXXobGGfwg444DiShkIjuW9vcpZfS6DMI70qct+rPUCyud71UAynShGOF3pnpdFqgKTvvS1Rubdwy1vz6IUsulJCV55ON5SnY6ccSRnSzRlkOrUw2yJsObtM05WBf1kOL47x05ZFZwojooIKI+ztXZSC4AU11K0ahGBFXEXSfoPpHH310zz2QRVVbslPM+mTBKUMhmVyQR/4Ml4i9XI6hdlcrKUsK7ezsrLwFkK6JFLH+2rYt9wmN6l4t7xNZnJ6eFqPhRnis4cyuZ79RQfq9Q6kUg5vc2HBuWiK5FvdUXH2udXB7ezs+/fTTePnyZQcG+47aIaWhzD2VqQHmHhNNBEJDCS53xaouKRL5qppUfEEwXxnAtqpcKswhfv/4xz++x3/21f1vV2g10nPz+bwEv5jYJZTmAU5Xkk1zFyxkAtTLly9juVx2jlRUTEgTQC4gV6VYlyYd94tkPMqChuRR9oyeG/MMec48FRGX4v2PGwD1HOM+6h9dag8SZ4qxRn2KlW60z1lXDkOK+F69bU8L9/f3RzUy4g7euHXOnvVMt62trXjy5Em8fv26dDbz7eQn1zpJH5HMi7jz4yXUjBnwflkETQomPPEkcx4PF3GX/ecHv2hC0IoPoRFOUraN/ON9fRZJZUiANJHJ44j7+22kXPS7lKNv+2Y6PgVfqIITz/NcfKXC4w7kGeWAW/tJWaYxyycvWEffpOFJXEoHpyxFdHcIc3u86tna2uq8fpQIlZNbdcudk+uTIbGsrU5yq31Vp/as//7gV0C6RsoqJrTxyeH3Uoj4+83NTXz11VeduhhIY3uGJhyFRyd305JOJrebvJjAIyuq7fRUFPP5vDCQFleTgzkftdPHec0VolNmcRw+y0JRoGpKQ/2jEMvVIBLyY/gZ2FS8QspGgk43rZajIVK55AGt9ljXTgqq1t9MRjPj4HJbm1SUJyXOEamy3Igou4Xpqqjd5FHGYyLCmjHwPvYheS5g9CkfpzFoZ/Tb6rPCWQEFI0McmYC44Aw13gee16mhue17Op2WA3sktMoAvLq6Knkey+WyTB4mhylgqozLiCirNRFRAoBN05QNc7Smbi2Zueh9IN+kyNhn58FYIgyW66Ut34xTOApR3o6ybIkGmVEr9LVcLuPt27f3jiFYLBYlYYwKQ9+JWBjI5SShkdJ3yokrAd4nhZltkHOjx7I1DiKtyikV3VGt+KFzSpVpK5THU9OISJyvGgvPCqabl1Efyh+jMFjOEI1OAHMNTUjuk4MN8O8sq6/BmYAQNXjnNED6rzbqcB7dw3IoqFpFYdskFILGfM/qcrnsLKvRmkbcV6QUztogErpLwdSURA3VDfFa6fU8vVzKRKsteo45MJoMGm8qQPXt5OSkHIikMXr06FF8/PHH8dOf/jTNlOTYMnnKlSuRA/tKJc1x0ETkPZml5i5q56VcjMvLy5L4Jh5o64XSxVW37tdqnM4jUV9UjpCw5EtohEYvMy595PMnQ2A1pNZXVkYb746lxe+DpjWi5eBE4kTwMmsKyEmDoIFkJqKUCgNbFC5ZWg0utbrqYxkRd3EDug7r9V1ykD/vqKu25OWKbhNrQcqgOeMWsvJujcknTTj549kR/0JvfJmVJtXTp087PHC0SRc2kwGPAbAMkiO8GkLLZEnLrRlC1mcP+Mu1YDCdClVnbER0X1LFlR4Gi9v2LnYmOdPY8Jka2qjxRddrAXqXtbFodiPF4da6bzLXNJtbBg42sxR1/5iO0B+VD88lsYju+ZJ8e5Ysr7S9Jo3awrwM+a+yLJo07gb44TXqL3mTDTJjCKrX4foYyqC6PnP/joRcZ07o9Q5SIru7u50JQl7ycJq2be+9f0XXv/zyy86E4t/QalOfb+4ywSBm7Z7seTcQ5JWMgHhAWVQeBl2OiDvjwvKYaRtxF7Tkbt2m6Sbg6VrmQtXIl8n1jBuq2lKrK8s+GvVelb5CtEPy5ORk8DmHlX5/dhT9JqSOE/KSiVqC5Nvg5H/y4GIOKlcbZFnkr9IS6V5C8b5+1NAGVy1qfRxDFBgqaJH7+6qXZ1jyerZMy92gyt9RIFV8UHKcIwZmp/YpD+Zb1JCouz2OVvy6u6MqS7LJVZv1+u4YP93Lw6nJT7WXddC4+LZ+IlMpV7pDmdLoG/++Q7K93333jaHBVZWPPvooVqtVvHnzJq2sbds4Pz8vHWXDMheEy6Cu+WpWZaylbZqmpJFHRMmO1JkZl5eXsVwuOy8Tur6+jsViUU5uUhD08vKyZDsqmq5Jtb+/XzbLMZilVx56/GdMHwmBHXE59U00n1y0oBFdd84PuuWKhSYdrZiE390YumtyeyaTSSyXy1iv7w49Zr/6AuNse01xkG9qG4kuFuuLuEOmLIsBXS5LZ8iaSYaKVUTcnVKWIT6VzbGgkiKii+h/sbSP8xD1zaOxCMNpEHFkGpuN4ABlVHNnvDxXIn7vGOUh5KDB16ndaufNzU2cnJzE/v5+UXSr1SpOT09LRqR8T9WrmIDS0Nu2jbOzs3IvjxpQILY2EfoGR+2hRXLaRJFmSiPibiOZDiiSpbu8vIzJ5HZ/j/io9HTuFhYflX4tPujMCt3bNE0cHBwUJMczKTSJh/x1zwB28iV7N1qcZFTMmcGinBNtUTEJMfBcD0dSXFol6tAfX0ZFoyP+ahy4b2qs/I+9L6NNn+tVHOv1Op4/f57+JoZxt+EY68h7uYrA+9+FmMXJICh3sbob4e5Fll/AYCAFxsupTfqM+vilOjYpT+3xP13nithqtYr5fN5ZPqZFFHLQZCNc9/ETL6R8VabQH1dTNDZjJ0IfMS9C7fExcwNFNOVyx3HV5/Pz8+KOLZfLMuG5qqIzSYVAm6bpvNRLxkVli7e+FK6xkbzWjNCvi9x966NReRzuIzfN3c5RnktRqzSDz24lhqzxGJJGl5ALjqu9KsdfrKSBk8DzcB9uuNJyra9MMKLPtgy1m66cu3TvYjl8UpC4jVv90vH+EuQsF0L80WT1w4m165hb+I+OjuLy8rKzUuD9JmUrUQw+8/qQ3GSIq4/H3jZdm8/nZeKrn1SsdNdoPL1d3lfFxrT9Qe5SRDcJb1PXxO/zcWR7fF47r/qoN+X88ePHnUrcR5XF4jkWfR2hNaBV6G3gBhNIyoL1akAcagty+pKj6qSFJvSUcPBNXoyAe4DX21JrNy2jW8kaP2rCqb4yNqC2UnHod1lSrQhxv5AmiVwLZs3qGfGYQki0Iv54VqrLRrafQspd7gHvIUpytMEYBuvwpWjVk/FVcSu5WjzukLEvT+aSgiH/eV3EzFK6OVrNiYjqK0Uz+ahN+kwxZGXyXslCjTZajvXotYJe2nrslfukfx8WtY8knBpwJfZw8PQb04claIxXKDBIYeW9Elrua5ELQDdHk2TI5XCo7EtnDqEzt4g89QQifhc/JJTX19f3jlAkXFa9jhQFt7ndnIqCf5zkY4kuiFBiTW44IfyMWN7DScy+8h72WSjLjafcL80JyRZ3kBNFkO9EU/pNsTkqP99FvCllisQRbq38IYO+UeYoP8tiR8S9iUV/04kaTUzPouZ+v7ehRgqKXl1dxcHBQcmJoABoyevq6qrAaKWhX19fd7S8Tj6Xoom4PfptPp/H6elpCQoqF4LKh6il1i+3elJsSoV3xCDyCH1E9+BfHm3gsRv1WVb8/Pw8Li4uSjo+kY8moRK/9F+uScTty70//vjjWK/X8fbt2847cLniwDKzzYq1OBknOSeD+kVecLWub2KMIfVRk51LrnQj1D6eyeromu6s7qVxoKxwaV997UOaNaJC8+cceYxRJqSN9qqoYELXWodqDfD7XSv2oRH/zYNfHrB0n5k+KpcOuWNRzyofQUlRFAi+wVxlZ9aUE8d5QLfIeaP26Frm87oiYRlte5vMxrwTkcoi2uIZHL4syVUC8ZTfdQbHRx99FKenp3F2dtYJjvKUcyoGHzsaHfKPk7RmjMgL/8+yanzLgqX6fHp6WgzJwcFBuf/4+Dgi7t521zRNx7jM5/OiCOjS+InoEVFkTAZKCihrq8+xDIU5MtRzfZ6Au9NDCHnjzFEVmgm+D7o3KiuPsHaMpuPkzqLo9O/ZDk1GaXrucpSQS1BlEdlGzzuhK8IYCQfIFZrzh9/JB20I4/NymRicy3jivKVCZPv9iDpaG/FBiEyWV/za3t6Ox48flw1+x8fHnRPRvBxHXtkEzfqR8c37yDLcCLl7wPZk1tWVmsZF7Ra6ZLu0CuIIi4qZPJErzfNcJHN6vmmakqXMs2N8ntTGPfuu/vfxexMafeYovzNxiIPlGlIDpbepDQnAJpT5bTyFvG27x8/zHAptnZeikXCwD8rqYxo6DzCWtSBa4aYwUeYr1gas5ppQyRA1ZSQ+1J5tmqZYSSnL6XTaiQsQeqsthL1N03RctMvLy/jyyy8joquImK5N5VFDBX1j3WdB+/hJvnDyuNxm47RerzvHK0oOsr5plZHGg2NFRMe9TWyrylF7GYsZUhjeX5VBw5HV91AalXKu/0ypzhqbfb65uemkePM3H8xMOGqfvY2C3GK+GKadjVwhiOgGHzlInEgK/nmgkrEDuW2cWH6/Cz4FkH3oU6zkUYaA+ojWVnXw3BEvS/VQ6FU/FWXNhWF9QiK05oxtbWI9a4LufMju4yTKnqGCpLvWNE2Jc7BvCorLoOzu7t57y514xNcgcBUqUyJ8Sz0TxLwvMmxZrEg0nU7LGbY6J3aIl2Npo1dAutKIGJd/oQmWWWS/nxaXdbig+aYmDbqi7/P5PPb29sqAXVxclASl+XxeBllxjEePHpVt4XohtQ7pPTs7K4PE8yWEMmjl3aKO5ZcEu4baMqg/xmpwzNR+1UWoTOXBFQIhiuvr65jNZvH06dNYLpfx+vXrODk5KRODr9p0njDozAlDHmVKo69v/lutnL5n+BxlyY1MTcHpv7bOE9lyx627NxxjrpDRjcrcOpIHT9kXGofj4+P49NNP4/j4OF69etVBWIxjuKy+l1WVtm07S0M168hO0ifzyUDyoFTfRCPk9nbQIqxWq3LEW8TdGRS6Z39/vyQ8KXawv79f4DsPpdXLi6Q4rq6uCiJROrXav1gsOgKjQexTls6vLI7in/v41Udte/dSLaXda1VltVp13n+7tbUV+/v78fjx4zg5OYnT09NYLpfxu7/7u7G7uxs/+MEP4oc//GGs17e7aPf398t+psViEdPpNI6Pj+Ps7Kzz6gTvb9Z+orgxhqn2fcwzlGkiTwUy5doyjiE54cqiXFdeU/laZVJOCBU5jQWVeR8CVT1OLm9t23YOE2L/a0p2qF7RRu9V0efd3d04PT3tzSPgNeY2ZOXTWtR8WIfVOsrOoTF3LNJ90KAo2BRxl6vAXA1adxdewn23cFSQtKibEIWn9nsfr4fKlgJktiInDP34R48exWeffVYUydHRURweHsbJyUk8fvy4vPlNzymHRcqU71ahJRcE72un7o3IzxF9X+Syk5FOKG+aprz/VsvSaqeQmRDYer3unFTPU8tVr5CZUgE0Ty4uLmI6nZbYytAkphtZu/f09HQjt5b/azR4yjn/q3EnJyep4GYuRhYX8WcYAPJGM9jk/mkG0ag8FJ9QgJYRbZVLOC5Fk51yLr/S0RffUythcuta40+NH+xXRjXe9z0jyna3KggqaH59fR1v3ryJt2/fxnK5jLOzs4Kk/vRP/zSeP38eb9++LYrh9PQ0zs/PC/rSJIjoupR0dzNkSSXWN5k3pRpvVAdljG3Qfp7JZFJe6kU3RkqTK0cqV9elWK6vr+P8/LyzYVD39s0b74NTZmizPmd19MnKkOIYlXLukNldhqwjbEBfp6mFHaI54oi4/64Xd4PcYmtC+KEpzmTVwcHXJGNOhOdAqCxPH97UVenjIXmR3dP3G6+pTYLhanfmIukZt2ae9Mf7uXJAReQ8V726/yE8yvrbZ5xq5G3nuM7n8/joo4/ixYsXnYOVPNGLW/DZbyIubnMQeXKctyOT0Vq/yEvnjY9vLXTgvOC7op0GEYdXos+7u7vlnSW8dxPK4h+uNLRXgSnHuj9DKq7MuDWaFi+r160Pt4L7ngi/l/1h+8fwgPfX4hi+CuRlUHCzsWA7mQ/CsiVgTdN9DQXb5tbZESHL40oVN6uNUagPkadaOWN4wv/iw/X1dTx//vyekqT7S6UgdCG+iI8R3VPOXUb53fOM+pRhDWVsSovFopO8Nqas0ZmjnKxte3f+QnZv3zX/3VGGGk1h/frXvx7Pnz8viVG6hwjBy9XAKd7Qt2ylZyg8mcD5dS+PFnZTwc+EmorIfX/ew3udd04+iXk94s4KyjXj0YmMg7C+bHWL/BJPuKOUSCezltlY1RRm1mevOyuzrz4aDie5Ifrs3zP3OsuyzmQki1l4P/raz2tj5Y+v9xyrfEYvx3oniQgyLRrRjVTzGf3m92aNvrm5iZ///Of31sEjoqPx9byWfqU9+SY2r5sQ2pFI1t+s/zyO0M96fKgFYKKUK4whVNFHFOI+odWyteA4N2CRMuGuoa5asDn77O3N2upKK1McrkhZbmasWG5NcdUQaNaHrC1+LRuDTemhciY6PDzsfB8juxudOcrzAmodJzmj/LdM2PxeCS/3x9QmjoirOG79s8Ek1SLZzM/Innc3iFQbiFr7qTSoRFxB+6StIQ3yIiKqqz6C2kpo2tnZiVevXt0LcFPxOvlkdNfSjYj3R8o/Qw7kG5W9KyAvr6+NNfKYh7uIfTkQTg9B466Aaihk03LfF22Uck4/ewz1QcOIO1jLZVWWL2GvRdl9MkfEvZhLJshZ1qBbr2wS+jWtnnA1wetiX4eopsRqiM6fy56P6CYZqS3OG33f3t6Ovb292N3djTdv3pStAn2IgOX5ik2GOmoTmve565XJ3FA5NUShQDlpyACyLkeDboyyOnmtpmgeihz+spQFafDowIhuqnVEP3R0GhrY2uSSUpEbUps4tTozyEmrzfwC98nHDqC7Jt6HTaiGyNiebOUjg8lUjhwXt+Ju4SeT28OZHj16FG/fvo03b950fPOaYvKDfbX0zXT9sTxlf2qykfW5Vn5NPmroLLuXSrFp7h86XCMPoOqZ7DmOseT9oYrkL4NGvTs2m4i15Z2aVRpigk9YCnbtNKcsh3+sBfL4RkZjlQCRjMcCai7YGCLaomKS8nOeczJkynGMtZOwHx8fd15hkJ0Poe+ePs6To3iaeqYM+4jKqNbe2vXa2G1iGLK6NjEMyiniaXGZnJOa5u71n7VEuTExCN7rc/V90EaIw4XXLULme/L3msDqAJnT09NOPfxcg+t9k4H1biIsGQQdun+9vttJSZdgTDmbKK7Myjq/MlfMfXL90bIJ4ekQocyy83nfvyReM+fBk74eOnEz5JMZpUwWNlE8zp+Iu5ge37tTmwMkrua5YanJLLOYMyKKycakxt/37c5s/ArImh8/xrLWGu8p6T4wtHw1YaxRn3b3uvra6M/zPi7F0b/Pnqkhsozkj3O5z/1r1a9yuHu1FivwNrmrJh5n7mifX5+NhyOfIfIgM08OG1uGb29ne9mXjChjSvTSgdzeV3/GyWUvyyXifV6GK2TnfcbXISP6vmhjxeGUCfImpE7WXmKk8rPMPk2qTZSHX3Pry2uZn5ktk+qzP5/VlbVhSGlx8ui/T1Y9n1l3TkC1k64Oy+KeHf3GjVhsew12Z/dlVFNMPjbZ/WMViaA/+zbUDtGQ9e8bt1rbslWkiLsDkJXo6GWJLxoX9wKG6n3fNCpztO96X0Yj7xeN1fz+vPtp8/k8mqYpsLpW55DQ9qGQIWL6e18f+2iMG6T7XAHomk+4vonnv7nVOj8/L+eG6p4sCMz69dm3D3iWqSs472fT3G4ku7i46NTZN4YZ7HdXUZ8z1yor08c0G2O2ua9tThlyoHLzOedyxeV5L/d90ZiyRiEOz9B0YSCDaxOSQr6pa8FndfKSdhbWSPUw5XdMnVwazpQBVyCGlqU3QUFDyqbPnWqapnMAcM3y8/6aq3F5eRkvX7685+awDB9DKq8+ylxMVzyPHj0qb3rvKy9DSxF5foVchEw2uYrhPGMZGSqt9dH3m/QZ4Ii7dHQ/Ja+mqPrKHFNvRhzjMWhu9AuZao3we5S5mVUsv5EbnLIO1AavbdvOwbp8JYOerZXJttaE3CE5hYbPZVZpqO7atU3QiU9itnVoN24G+ds2f6uZLzdmSsbbNYbGWPtXr151DNUY2fO2usuo/SP+RjUGGllOxmdRtpIngyZ0Rr4yNlIzNGO2RIiGlmlrPB6LoMcil0HFkS1bsmG1CG92XVH7oTprxAHwdfFah/mmNVcALpTKY+BZpVm6OoWTbcva679toigyyhTBmMHO7vFxciSSPZ+5BptSzWprAtVg+Fje+f4X7rdROQoic/+NJyJmRP6znv39/XJoTg1tZ5RN9D50niH9Pj7UlmIzNDM2QS9ixHKsM8gLZARfrkEGq9WYoV1/riUztOD39ZGEJFMcbFfE3fmovmmrJgx91IdE3mXiccVgTDuye1xZZkcCZKgv+z9UF5/XxKztfdF9kqeaEqvVz+c4YbI0d7ox4gMN5dAkI5pp2/beyktmbIeULp/1+/xajf9EDa4Ix9Y7Rj5HpZyzw5ngKO4gKFibZH2CULu3T5GM1bwiWros4LVer+8drFyzfiTPrPW26rlaXzahoU1QnDy1ycW2ZEqo9izJUZcsLttIQVQyVJ/C2FQp1dwXNwwZr7M2Og+GoLsUh049y9rheTI1xczn+P7d7Pc+BSDqW6zIntkU1Y0+c5Tay7WxLAgbrd/ccouGJo4PeG0SDEF2RxYZ0hjq+1B79aJrHac4Rtl4n8YqkXdxEbJ69b3P1Rqqt21vjxbMDIbgspCNH1jNtuhwaX9naTap9d2V8VjBz/rgipOvkHClJvTMVcXszYZSLEQ5fW0Uus/kiDyozQsZDi4G6B69+nQMDfGxV3F48k3NMvkkl8DUGuTWysnh3lgaYyVr7elry5CGX61WsVgsRrfzXydl+12c6C6MJXd/XB4Y1K7xfLlcxsnJySg3sE/GxlKGgBkXUOJhhjyyPjKG5+4Cy+1DG30HY/k8rJWhchx1UGk/VMGKRiGOmkvgiIBRZEbGqa2bpilLT331Ma4SUQ8+vgsjsoHZFAXomb5o97sM0kPcu6Gyhn4b6rdbTn32VanM2PTRixcv0rHn82P66ghhaFzcEGo81+t1OaCY7wSuISZmDus+7VPJgpQeJK6VO5bU15pC3QRhD9Gow4qzQXCIqP+ZS0LSoTd96+abdCBrb1Z35rJQIdbcizHavW3bTiLaQ9o+xl3IKHMn+vJp/JrXO6YPdEF47qaPf83YZErKg3o1I8V7+BvL059eo6jXLWZERMEAKZf8tQLD32goHE1QcTjCc5ffXeEhV2aMQqnd0+fe6MjCsTI8+pRzCorOKJxMJuUt8JmF8EQYH5QhqzFEnmwzxrWhVanVNzSAPvFqy8xa29fx+EM0tv1sxxBq4nN95WfKo69+vZvm6OjoXjlUzCzPk+t4D90c/Xc+C4IPTS62RYrBx4eoOJMJfeYSMdskFFJLIGuau0ONmUbOvuvdPjWk2+fSPMTI1FyfiLi3E/edFEdG2vRDBvgGIFbuDOHOSbf8fZSVr9cgaGLSt4uon8KuPwai+upRv/V73yTj91/nO0Ei6gFj//wuVqp238nJyb0XYNfGlFC9hjY4UV3p+JhlPKdl52dO2gwx13jkqI3jr3bwYGEqPBlSZjarbt9xrjLd9XsIPcS9YZ8i7rLE+zKtI0Yqjvl8Xg5mcYgfcTfYXpl+13H8tQOOGRupdcw7GHErFNqzQo0+ZFn7XC4OItuvt7VLEIfaKyXmdbwr9Vlbf8FxH6pyylBY7dlsUstd4ftGamV47g/L1XXJE+uqwWgqLLZP9WQyxzbxOSoGf4es7hF/tb8kMz5te7vaOJvN7vVXzwmxZLwdMgpjKeMvf/MUAudJjXoVhw7i9coEwbe3t+Ps7Kw6+UQ1eFlTCDXKnvdNbm1796Lf2qqABFPwzOv3wK4ySnV/bY09G9hN4eYY9FUjP1Ana5dbc06UjDhGbv29bCqPiO5RgVk/1U5mbnqCm8iTuKiE2L5MuWTy589zwvLPy2ua29eC0sWYTqclAYz9i7hFw2xX0zSdfJZMYb9PQ0PyvnjYYMz+K9HgXdfX1+U1eLIkmjh62bArDtfg6/Xdm7dpifTOFHYmoyFr6BPBLUBGLmhsgwvM9fV1521mfZN+qK1Zu8deH/pN7c0U8VCZY3jcdw8nyvX1dXz3u9+N5XJZ9olkyobuAN1dKh8aokwBZopQf36QjrdXhpHtoEtEnjI1gWnqaqsCsSpXsQ3PyqWr7+5Q3xwYa0zGIMcM7blSGaqvF3EcHByUI+REhI+r1arzukQ23pfU3J0QRMuCVu9CDFZxkrty64uD6B4Oqt7mRiRTg5NjUQNfLZEF196VNAa1MrlXQ79nVln31sifWa/X8fr163sv0XIFoHGggWnbtvNSo4iuUmFdPuFrbartp/LyXBkpjqGkKbktDHbTrVmv18X9UE6KuzpEWOpbH+LzcRhLNdTp7kmtnndyVdRxBn4EzfQy4sPDw+oAZNCRQjKmw7XOZXBSbfSlJbdsukbf05Wa0JAmlu5REk0Nrm8y4Slw2ftf+miITzX+ePtUp+JXmQUWH2r7lViWftcu14iusRFxHP0aP2s8xgSa++rI+OUGZXt7u/SV5c3n84I2/SgHvTyabq+Wf92ye5/F7z4jl/XLaYzMjDVmY8vrVRx8ubKY0DS3y4t830ZflqiezaCQT7xNJp27RLy+s7MT8/k8zs7OOitAW1tb5aAYvstzf3+/uGPqi2Dser0ugVGd68Dt2QxI1vbqZCQBY/4DTzQb6vsYcsTHHaFEGkJREuDJZFJeps2kJkFwKRrxVBu9+BYztpOoJ0vuYhtr+Rm15dkhcoPR95xiY25QNM5KP/BJ7kFwKmJeIw9IfjjTpjSGD5wDjvrato3pdFoOVq4dkkwazOOo+TyEoLWGa516rLbb1I9zVNM0Ten41tZW2SKvE6Mnk9uNVtqDwHX4nZ2d4p/LN97d3S3Co3drzufzsv9A8Z+muQ2YjbWMJN6/iVUY4k/2XUKuQ5U9psA9EuKBH0sgqysFqgnFzzc3NzGbzcpz4ql47QrSjYDGlNezFZIhHriM0P3I4mAaZ6EK3e+IcDqddgypI5q2bXtzPDLXiS87ozJ6X9SH9Jvm9kS9nZ2de5v1ajTq9QgqnJ8dLWSWNoPyTj7x+6hWPv1EvklcVlPulZCD/ra3t2OxWBTh0OBp05rKk7BI6N0Hpp/rimCo/6QsKv8Q6nMlhMDm83m5vr29HY8ePSoJXZpAXD6UMOtIQS2Dt23bOblKfFuv18XVXSwWvX1zd6U2zi6DWT9rfKu5RBkpdte2bdm4qPiW+CCFqGtN05T+zmazzpKrlKoUzsXFRVEs5KN4S8X0vkh8zRI1IyKOj487AekhGtzkliWssHBCVIdlmlA17Tnkczr54DNZiMpAAybI5e+35YG8bLusqWC8BnuxWMTbt2/vHfvPzNmIO8g/9jQnlbWpqzYU26gRFSGh6/n5eZyfn8fe3l4sl8vY39+PX/ziF52lyuVy2UmmYlDTlaaUhTasEbV4rg+temZEMlRS40WfMmEdWUBe93gZQmN8RorAkYVeRHV9fV1QiwwNT/KXnJL0nIxdhkqGqHavG/chgzYG+W70Csihyug/Zb7lJgLf5wv3tZWDz6U4WgCvU1aE+RuyADx4yBPV5PqoLiGYs7OzjZWH6uzjy6ZuTOZfe+yCAn91dRWXl5dxdHRU3LGI2zE8Pz8vAu9B3GysLi4uiuWcz+dFCe/s7MTR0VGnbX3jzP77y50yRdInZ2PQCF0L9U3yISPhcQIZGUfimZuiMrP8kfl83qnvXYl8yNrhvCJPh4zYYHBUE4DLrh5Yibh/nL6Oetf9NYhEciHhOrwHx/o+U8AF/yK67xxhW11YhBx0QIssSJZ2HBFF6QiyO/qqUQ2y81lai4e4L16m2sy3i0lhMjhKtEl0J6HnUqlQhAdexVvJ0fn5eZyennYQT40ffX1VG7Prfc9mqEVGhUqS8iA+yEDIjfGldKJekuSNqIXKh0hLhkhBylrfh/hT6/emhqePRr0eweGgD7BPEh6AwqBPBkcJI1kvy8/aod+8nMvLy1itVjGdTjsBy+l0Wizf3t5eHB0dxevXrzs+qVZGtNohiymBkUWQQmMey2w2i/Pz83t7LZyGJkctes/xGEvOZ9/lqfHhqpHaz+VZfWacR8FhJT8xJ0Rxke3t7ZjNZkX5sj+booKI6Gxt52Tog+A1C8r7eQq6uykR91/arWueVe1GiC673+cuvJTRYrGIvb29omDH8qdPKfQpab9O1N1Ho4KjfRteMpj36NGjuLm5ibdv35bGed4EXRlODBHjCY4Isvqp8eVm6CwFJeR89NFHReDp4wtqegSdqdPMco2Ikgikc0UODg7SpDf1a6wiEZ+GToMfKovXVK5WgWazWcxms4Ko1ut1CRJrDJTkxIAzXbfZbFb4o5Ul3nt9fR0nJydFeYtYR4ZChxSjxlcCzvs1kflyr1oZ/joJypnkgrJK5SujweV4ydx6vS4BT8XZHMUp8MyMUwXknzx5Uo5gdMNRS+WvIQryKgs50MDx/xhkMqha3Hq69qKw697Xr193ItMcrCdPnpTBZWJVrW66CPLP2VFaHLlU0+m0o+wk9F999VWcnJyUFHL6pY4UtDpAhcdEH5VLJJWhL7Z1iDTIfhTjJlSDzGwHcwyYo+AugCdDaeIw/qHfKNS6dnNzcy/zmHyizLDNbFOtj6qL1r3GN48pzOfzMrnZLsqqUNXZ2VlBUmqjlpe1D0U8FRJlpiiXt3VNikdL/FtbW7FcLuM73/lOXFxclNPSNU5CtXTdx6IRutUaO5fRDL0NKY9R71UhQqhBPz7Ttm15gbTDbA5sLU5BIZrNZhERBe7W7qf2l7VgwFNLYI8fPy4uhTNRCUB8XpNJbZelaNu2KKiH+o7Zc7QmQ7B+iHyMlNtCklup/AU9o34RiRFeUzG74tWfcjl0v8ogmqLR0O+MJQ31n8Jfc1n4vL8PluUQGbtMMUgeEZ04BGVO7i35xLR1uimq4/Hjx/H555/H8+fP4+XLl8Vt8QndR7rPDSp/3xRV9NGobfWcHLWBcY3lvpI09fHxcYdxTrREygUY6qwGzlc5lMzi2pZ7D1QPV2C4hCYFQ0hKV8azJmsDtwm5+8LJ8S7UNHc5Aixb55romqMBukyMBai/RJbsMxWHI7qh4OYmvCPfvb/uGmvsZM1Zt7vQkidfbRMPpYTk6nG5mpm1+q9yKU+s4/T0NJbLZcxms9jd3Y2maeLLL79Mzwp1GcnIrw+h+03kaxBxuHLIKuBkoZbUNX72CZbVqexMJd+wLdmEpKI5PT2NxWIRBwcHcXh4WN5Dur29XbLjmLdBixpxF8+Yz+clNV31a7C1UsO0dIfuD1EaEiTyLfNbH0qCxPP5PKbTafzyl7+MprlbqpYrpmSniOgENT3lnG3Wf2bpyof3JdRakhx3oOq/G6mM+oJ5zi+iiszvZ52Mc8lNEB89vqJ7qWjdRVJ93LuzXq/j+fPnsbW1FQcHB9E0TVxcXMTp6WlnZXFs/2q/1+IfDy131NGBPtEz7ddnbQkDmY3oll7PK6gk5utZWQW1KeL+C4GV7nxwcNB5ebGEWJOeFlBWRCnn8uE1cCpXMFvp6W3bxuPHj+Pq6ipOT087k2mM8sgUsENOXc9gJ4XPx0a84ZioT0+fPo0f//jHBU0RNlPoNZk1vrKOWk0hOpE1U51CYyzblUdfHzP+1Pz6McqF9+s8DD7jxs6Vg+6fzWaxvb1d0KzKE5q9uLgocsJdvuo/UZ+WxK+uruLnP/95pz2+X6SGCPpQeIYCM6TiLswYPo5CHBlczhSDr0LoN8+dkALwMvSblkMptBnEcoGUlTs9Pe3kIUTcvoXdD56VZVTbVQ8nga4rSMb2Km9DSooKlO4YXaI+ypbxIvq3het3Pk8SX3SvhFRC64lBzE2RAuVvOg3OLbdWChaLRRm72WxWlhUzwR+jILP+kLSEqdTuMUFlHf6UKW6Wz3iW+s/ytcy/tbUVu7u7JeNWsTIpFRkVIbbJ5Pbl2s+fPy/lysiJf1zd2dSNEEl+uYyd3aO+8/+QrA4qDgotVw+yezOkQaJyGZPYpe8O293KcHLKyh0eHhYGcBmM/ryYygnA9GgJCQVIE07tePbsWTkdzJWnT4oaPCbSoBvFvvYNJK0ZrXpmXQ4PD2MymXSCeEJZ3KvAJWtXDpxIEdFZplU7NZGVPMW4QpYz0UcZj/ibjpCsnejmZWVEPnPS6jePk3DpXntW9vf3S7CZCJmBUSJctofuFnlcQ6Fj6eDgIN6+fdu7RC0l5ykQfdS0PaP39OnT0uj9/f0SQ8g09XQ6vXcMvcMlMklCq99IDMxRUGoKRtDQtSaXxtxfbtu7sxUoLDzJiZF/5nD45CQUzRSD2kS+ZZaVioO/OR9oJXivr+8rduHtkSXypT26kkyr9qXtbP9FtnqipcbPPvsszs7OYnt7Ow4PDztKZEiBUHn5xOZql4+LArZUMo6w+N3dEik4V9iuRIgOlOzm40Wky7r5hgCVK16rHCqvh9CjR4/i5OQk3SrvRtf7enZ2Vi139Lb68/Pzzm8OaWh1eE8NQRDO6163uvxzl4kT1euQNpfFE3QWKWmLA0OlpwFW+7hEp3I1oejisO20IDUUkCkPTlifuHpeQUjnEXlO5eftV2xJ9ZHvnBQeP2L8ommaEu2X4uZkIQJ58eJFRESJBVxdXfUGNJ0y5CblROtMfjLFm0aDZfF5lzneo3LVNxocrrBlr7fMVqc0xlSEHCOXlT6lQcWV0du3b3tRlvjj9wwpqtHvjvXB9sGiUIsyaM6GeRyEE5CMo+AKVuk+WU1NEG5EEkRu27uDZwTJFf2X68IzGiRIXNpleznx1BdOiJrwebCSA0Y+yqrT/SFf6D64RdbvXN0QLZfL2NraKjktTdOUdPHJZFJ8f50jq7IEYYnG+IYybgPnErh4y7MsHAVQnjJhzdCtytdY8Lr46H59JoeeU8SJLpTJCU/FKTdXsSCl40uGFP+ge6Z7pXi03C8eRkTJ+XAZqk1kor+MhnjwUCQz+hWQIk1I5kLwPndNMjeBg+3KxbWs+4AaQNbhFl07MzXx2VYpDw0uEU/EHUrh2R20/KqfezmUPMVYA4l88IHM7t/a2oqPP/44jo+P4/z8PJ4+fRoREa9evUrdJNbhUJ5W7OjoqAiprmnJWWiA8JpKR767FLOvxEhwpaylZKUE3aqTF+5OZJ+dhpRMn/tD3vM58ZA5FvqdKJQBdu7JmUwmZbVN/efpaipXCknuSNPc5hZlBs9Rt1OmIDPeuKFyxTTEW6dReJEN29nZic8++6wIgZjgk9+VSMSddnQ0IchHhrMc3i/tz3wQTRBaOaaHOyQkHD8/P+9YYFl6WRPVrX0enDCXl5dxenrayXWoWUgfdA80++AzfVooIVM0rkQz6E4eKWjJ+zXxxS9t9ONSue47OTkpW+ypbIXMVJ/K9GVvtdkFuUaZ+0CeZQiPMjRUthONiVZCIrrxI/JNSvH4+DiOj4+L4qCRIkKjIaXCVAq7xkCGqG+CZzx6KILYlHqDo5999lnZuNPXuAwlUGgp8B4R139OJNbnwSyfbJrshJERXXjM4wv5X+V4QDKiu/WZrkEWCGW9NYVA1MK4yL0Bae5Wh7hBqubHikfOX9XvwkfLl7k6DNSpPsForhBoL48mhZ6j+0kXi7+3bdtxaVxGMjlzeXLXp29SuvywPL+H45/dy/iK+BbRPYJiPp+XTYT6Iz/YNi5/r9frsgNb/K65YjWquSZZXzhvKbP6r20jGfW6Khxc/acGVkPcCvJaNtE12EQtEXfMYxne8RqU9cCSoHXEnRJomqYMpO6RIPi9XG7jci3Ri+5jfyhMTlSGLhDON+ZP9AW/sjrID+efymN72D/dx2PyGDTWOaxCGHJ9skN82ScKriaY88WVB9uu8ZKl1z0MyLP+jMZMOkczbLv67cFNIQvdv729HZ9//nm8ePGig1C9XKJozS3W6RPZ+9g0t/uBhO76+ufjUuPLWMQy6jwOViyhy7R8Bj99wjt8JnxVuZkldsrKa9tbn11+p7Ic3XoSaspnjYjOQcVKe1dbNLkYAGya7slinBwZKiP/+vrFyePIpu85TrhM2JjNyZiErtFd07b5i4uLewFP7tIk4qNCoQurQCMnBA8EzlCg90385xKwjz+/Ow0plRpPvV7KAfvFNPLJZFL4xv4JqXAFijLI+ziOLgfsUx+CyvqT3ZvJ6pACGZ05SkgVcX+pTkSLS+3JvIFMuB3mZp3MLCitvb5L0Ln5igFP9kUC7BZak0Fp6lzW1AoKlaivyjgPa9RnWWpBQ3++VlcmsOoblyo9GU//KdRCXzwNzZd5VacmgXgo5e2rclQWnvGZ8cyRSg3yZ7xgmX0Th22iImCwl7LP9itwfH19Hc+ePSuKWgpCKyriicrjWHP/k2Q1M8payWGuRW38h/hKHqhPjx8/Tu8TjdpWn5HDKUcSmUtBhFFDKITP/N3Ly+qXm6GzM0snt7fLe1N0ClhElPMW+CpLlXt1dXVvlcFh5mq1KqnmtLA1VyWi/4Ql71efFfYx8Hsy1KcgME92lyLwOI9WVTg5PT5FgRc/FJzWSg2DuuSfj30NpRLO+2/6TwU1Bp31ES28TpLLlBrLX69vtyRIBmmMqIRdcWtrRcRdWMBzjtQeHoTMNjjiz/qTKY8af3RdhwnVaNQmN1Zegzt0XQ4ODuLm5qbs75BG1iBT4H1QeA+veQ6EC7WIL9ShVhf6kFWQvy7hV32qmxFzj3dQaAX53Zqy7brGCHs2ATJeS8iyAG5tvPpIPKcSl9AqrhERnTRppumLh7S6yl9R3Efjq1Uo8sYD331QewitqZwnT55ERMTLly97+bApnJdbyn7RzdNSNF0V1qG3vHE1j6sn7o74yg0VLVcJVS/33Ixx71WXUhX6MkOHytsYcWQ+F92ByWRSTrEmjFNZFBZNDF+J0H3+O+uqwTL6pOo83/fKstRe/fn6feaDyh0hcuAkpFL04BotgwcoaV3Zr+z0LB+fsda1aZpOAp2u8Uh+CTrP7eDz6jPL0KTSWOn3LGDqkLi2apD1KZv4bXt7aBTdmIyXY5SG35cZNhGVAY2L10tlQJeQ8hFxtz3A++31uJLIDE6tP7qPiYCuINr2dtvF3t5eL58GE8AyJeG+l2tMdVb30cq4Rqfl4jIXGUhtTeGV5lXdsuYqR9ZaKykK+mkQmT1JLa+6qbTo23LCCFZm8RsOsiy1Kw2H6O5Ds6za+PjnTFjELwWMOW7MW9DztKCO3HwFibERV7aUHbYn48FYl4vltG1bLO8QH8aQt0UxCS6pZvt8aJwoRwwK85pvVdB8YTxK76Zxw6o5wDNjXHaz/uue8/PzKmIXT/vQSMQG71XJGsVBzxSJw39OGjWSWjpTMFzVUL0eOCLJd5RmlQBre/d6vS7KQ8cRTqfT2Nvbi8nkNqlLb+Ta398vvrP26uhEdB23J79eRxyqX5nCrVmKTND5LH+vBaVJzhPx4OnTp7Fe374jRe/J5UY4KRS6LBzP5XJZeMwMSb1SkhZ1sViU8dPeIPbLlSbRHxWLywTlTYbDA9IPURjOPwUymWIfcZfEqAOK5MZ4EJkKlf3hkqsHRMkb/TadTu8pRvKAL74iZUhLY6qUdl5ztP9OrooKXC6Xnaw2VuCQzBuiKLNPBFcW9IMzK+ruCpGPWz+hAGl7vg+T5YjxbB+FVHEOTY6IuzMYBMUlPEo51uRxX762QsK+k4eZkh4DS6lcRLJgJycncXFx0TmoV5mvUn5qw/b2dnkxE5O2uGuT/reCyWrf6elpCf7RGnsOBPuSoTHvk1tnX2l5V5Kx0aHBsuySKR4ORUWqyav3ojRN04llKGFutVqV4zCn02lBcFKuu7u7ha96ux4PQuZ8uLy8jEePHpUd62P54IYlm29DNCrGkWktfs8sAs+ycLiafY/IN8URBvPZGhzlRiy1Z2vr7pBeugwSBr6EWe6Mt0dKiWcWKBCr3z1uwf7XSJOlthLlfc6gP8vRZz4/m81iOp2WE8e1kjKbzYrAssytrdut8HzHacTdYUiaTPpbLpdxdnYWr1696mTQkqdsu5RlLX7A/vg4c1yHeLspTSaTODg4iNVqFefn50UxMSmQbXDXxFElr/N33kPUxJiY+n55eRlPnjyJnZ2deP78eUf++G6bscHR90WjXJXsDI6I3EfnRHBYzf9ZgIgC4fVE3A9C0h8UaQWA1psp6Q45VRbjJy7oKlcWRffu7Ox00rFd0B0l1AY3U4aOOgSf+2CkC7bariMRxSspOZ2URpQWcbsa8NOf/rTEKpqmid3d3Tg+Pu6gBB3sc3x8XBLGOJacCHQtpbx9fPm9ZkA4Lu97sih2cXR0FBFRVt08e5axOZGMDxUnURnHRoFn8lzpABF38jiZTOLk5CTOzs5id3c3nj59Gq9everE0zJ0+pdBg4hDAuvp5xF38QeugIgyt4XP+PVMufjEqfmxZCCXrNxaCXZzEqlNFAzBSr33QsRAqCaCuxneNn7PJgRdPW+zW+nlchlXV1edmAGpT4AkvDyVXTxRHYxR8F79ye3gNS3b8mxXV/QuBzUE6wqU92jCPlRZjIHybdsWpbFcLuPx48dxcnISb9++7cTaNPFlDOTaZUZNPON5L9zApmsyeJmhkZKXAld8iWPk/RjLk77vfTQq5dxXM2oQUffxOVptZyzr0f/M8pIYE6GPK4GWb72zsxOPHj0qvqCCoIpFEGVI2LXScn5+XuC97tEhLXzdIdO2ZW09ZjN2EClsNTo6OuoooYx/okwIpDgZF9CavvxstUHBTcUvjo+PYzqdxmKx6Gzo4spS27blWH+dUD8Uh6gpW/bBEYo/O5aGFJdcht3d3Xj06FHnTBONK8ea52m4ESJyVVr/8fFxzOfzjvsupUO0RvTKNmsM5vN57O3txdnZWRwdHaX7VLK5k/FCwIBKbAyNegXkkOXPdhLW/rO8bBL0KQ2iGj3LoJGy9nzZUz4gA6n6TZuEaHWV0CQlIYWi+zSpmqaJvb292NvbK+c6jrFsfeTPuhKqCcMYwWGwku7GRx99FI8fP44f/vCHsVqtYrlcxte//vVYrVYdv1oTSTEg7enZ29uL9Xpdgq98vwhXP9SfTZQp/29iEbOyas8zZjGZTGJ/fz8iIr766qtSt2Rcq2kR0ckW9v1LWnlar2+TyPb29spmQGUb0707Pz8vSqnPeEiRKxhLw0zejuGV5sze3l40TVNe2TqGRmWOOtzMXBB/xgXE782yQyO6OxCZ7UmfjsxypaWgHxUQV1mIoGRlpXW5t0D3qXzCS9FkcvsWrvl8Xg4B7otjjCH6yH3l0B0Ygu96Rq8dpOLThP/ss8864yplcHh4WIRUS7gHBwedlSwdNKT3gUTcHVasQKrakRkSlzOfEO+Lxije9Xodr1+/LspVOQ+KYahfchv0jFLr5eZKboUedOaqDJCUqW+SFCLOFKzHQPrc9xpxjFWP3rPcp7CcBoOjhN5sMDtB/5xr07TsmS/L4Bk7Leg3m8067+ccYoI+M0Cqe7RblgE/tkP3eHlqv8pkn25ubuKXv/xlRHRf0vNQn9Pv7VO8bPeY8qQcPZsz4nZfwk9+8pPSr9PT0/jpT38ae3t7xWqyPMV+2vY2UejFixexXC7vvXhJbfa3prGszG0dcl+GYPgYfohcbtu2Lfua1DZmFPM/28qx8I2g+u+7sSm/jJuwn2rTQ+TKeZUpDSEiHvY9hnoP8vnkk086EzLL1VCwMUMMtRUTZ3ZmMeUmSJPX4isqU8zgCgsHkLEJKjtmh7IdXo8vB0dEZ4VCE8kj3rpGJZYOROKW+SpJ9syQZabLoDLpOnAnpg6fqe3cVP8ddbEetcmXWymsdA2HAp5URNzMtqlLWLt3MrndsyQXgLJJVJcpuaF6fKK6DGR8pJLhxr4hpFRTsiJfspUy5Fv3vJ43b95U+znqlHMy0TWhyNemXSv35SpkcQ0GiCg8bAMnKjPvmPkpyHh2dhaTyaT4c23bPYVKgq4y6So1TROPHj2K2WwWR0dHJR1X5a1Wqzg6OupMuEwR6D8DX7R2mUWojYn8YQl7370qy4/B02QUEvG8i4i4N/k9xT4id69UnytgR55ZkNz7oP+MZ41VHC6rzictJ7ss0vBJ8UdEx9gwbuMbGKmkWT8nqWRcAWbOlT60Wevj0D2cZyrX94opBsbd5RmNOqyYOxw5iSWQXjnRiRrYpzS4bKd6GCl2xUImuFVer9dlGz2ZRh+Se1g0+FoNkJAsl8vOy3U+//zzePz4cbx+/Tp+9KMfxfX1dezt7cVv//Zvx9nZWXz/+9/voJ9MGepzBgvd0tV+VxmPHz+O2WwWX3755b3xypCSytRY8f0fbdt2/O/FYlGU7tHRUYltSMCbprvKpJUCZVUSjmspkisJY5CSU40fY2gMpOe9kkMiVo5dRDdfSDyVMufZLfxMAydDyt84eWWE3oUy2eNcpVHxxYMhno0KjpJxrrUium9l89wOPU8fsOZ7qUx2wttCEiNYlgbb944ouKWyxRy3yOv17WsMv/jiizg6OioQ9he/+EWZcKLj4+N4+/ZtKY+D4O3V71SgVIyuLHzQ+TebzeLx48exXC5TxeH16v/5+Xnc3NyUtOaI25fuCGExMPf48eNomtvgoFYFjo+PywlrbNtkMukglra9jX1oX0/WntqYZrSJ7933vE+ImvsXcRvc1Rkbei1j297txN3a2ipBZq20KI4m2eWxAkpl52sqtR9Gmao6dc6NbI1Proj7+k8knxlwupz+DqWMBpdjGR/gK/0eP34cL1++7Gz0OTg4iOPj4w68pQIgavGcBXWOk5oMGmIKNapnfk4mk7LSwj75Waf6/fLyMn7605926n/79m0cHx8XSzqZ3G5B//M///OCVNgmV4y6RqivsjMlWuu3FNz19XX8+Mc/7ggCFS+/E3UIejsS0aE1bXsbHDw8PCyoQ4ogyxe4vr7u5BLQ/dHWbCovfaarkyFL9ncsDSkixtzGlEV++SY0Xee9fJ0oXVA+R2Tu7gtdc/6e8cUpUx4uUy6PEVH2YSnFfgwfIwYUB31iWhNtztFE0+9HR0ed3AkxxYOkfM6Zo0k8tBzpbguhuCMKRsWzQZEQsAwKOd0HKr+sD94fLy87DGgM8T4Fjc/Ozu65ieQR2yu+6E/PED1QcF6+fBmvXr0q17gy4q7rzc1N2ffCCaMdydkYDgUZN6Uxwl6biOSdvsvYuNJQ3Et81HENfM55S9mjLNJt0daFiO7uVI0bjzyokctShiz8Hrrlh4eHpQ/v5KqI/KDW6XTaOSeAMEdM8QbWYiSZ5RlrFSLuJiYVjt6HwZO6NcBakhVxMLVVWuvzgpraJLe1dXv4iVZ6FotFmTBDaeBSKo6+SC7A5AVRi3ZjcpKyLgqpBJn5KRoDjpe32ZWiyuXzRBj0n3Wvn1Ei8gzFTOD7ZCArc1PqUx60xpRboT0iOyoByZp4oDfZ0+jyWMGIO4Ws9846b7e3t+OTTz6J4+PjODs767jKff3i5yy+qO+KB3qWdyafpNFHB6oTSuaRNaGQ6V65LjUNzxUBIhavj884+fKV7ru8vCyWT376zc1NSYHe29sraEknUWuHp7aXf+tb34rJZBI///nPixZWotebN286W6CfPHkSh4eH914AJaL1IOJx8tUCKhnGcZqmKfseCG/FAyl2ZrpGRGfrvF64pLgGzxdRNuju7m7c3NwUF4WJdboeceenq38ae23d5zLn0GSvQe0aankXIq+JtHRdMTIpavGA+3dkjDQnIqJkhjKNnEFSXsvmj/f9+vo6Dg8Pi2utszR8bmXP9l2jXDB4PVYhjz460CcgIb8PYhYA5DOLxSLm83m8ePEi9Wc1eJn/nmlUXwqWK8UBIZxkCjrRioTh8PCwZE7q7/DwsJz8rLadnJzEZDIpwSR3n7K+6T5fkuUGQUcpWXl0HTgGVFQaM/FFB9Aul8tS3qNHj+Ib3/hGfO973+usuGxtbcXXvva1ePbsWclw1PkSdAevrq5K/3VOydbWVtknpPhHhjKGEOa7KodamZIBvRbCZbhpbreza0lSL0lq27aco6tAcsRtgPni4iIWi0VMp9OCNKSImebPM0y5FK7MUj0rGVmvb7NOiS5rfMnmhiOYjBeOHrM57TRKcUhj8twA13r0i9Rp7qrlJDg6Oiq7EKn5RdmqBDvL8pyZnjquCbpYLDpQUZ/pZsm6/OxnP4v5fF4mpxQHlWbEncAwG1X8UrnZhFEZviTHfni+hCse/vfxytwb1SfryYmvN48p14WRdZUhdMYcD9WlPkqpMD/B3cJM2WUKtqZ035XEf8YunE/qk9qu80t4jxCc+r1er8u1+XzeeYUmZYYyyT+/jzyjiznEDy9TZdTQf6Y0xyiO3szRr33ta9E0d76/FIcsOrcKu+IQHCOTWBXvHepkdj/vpWvj99Oa09dkOUQ4ZJ67TGybJzexDwwIu8tABaPJxb5SoegettH5WAYyEQg+T2XPrfFCaMyajIgS8BTxNY9SukRkKsvTs+W+sR/+UiW2N+vTGNoEnfShQV5TO6U0mqZ7ngvlSoqIO6oVb2ua5t7b7CWXGmfl1cht9FW6rL+1dtcQO+MWVFSUcyn7tm17X5EwCnHw1CFFeH23o/tosmz7+/vxxRdfxM9+9rNOZNiXbFlnpiQ4mb19EfmZID6xNZk1sEziUft1SKxSr1UuzymVAtXz/sIh1SGBcgTAPmU8zxCY2u/kiiHjo/OZKG29XndelEwXzt9Y78u6uuY8z9BgTQHX2us82kQxDBGVmJct/nMJP+IO+flp5OovN7epHN92QYTJPvNdNDVjFDG8oZHl8r/IjYmo5ib30ehXQGYooA8RMC1XW60JkfW8KwRnGhHJkPCs1+tS1/b2duzu7hYFoJWQg4ODUs/x8XG0bRt7e3tF025vb5fj4w4PD+8Fxagw1T8ucZKorBwx0AL4IMk1pBBn5MKbkRSKVpkUYNNY8PxUKlShTPrkvkrVNE1ZbZKiUNBUy4vaip+1PVMeTu9TYWRl15Q3N0XKcCoGISRJo6Lx5xYIjrWUBk/Zp8WfTqedDYXZkqivXjq6UNvZD8qku0JupHj/O62qODM3sRwS+IuLi/jZz37WYWBWBuEt8zB0X0bu5rAMlklNqwN93Ooq9iElw3o5IfScYCi3jmeCKAFyN8hho37zeh2lOJFnDj0drSmGwbfWKW6jVRAFR5UjokN+2rbtpJ5LuTE1unYotAuw+BJxt9TfpzQ2cVnehbwerY5QxhTTYvCdB/pwCVyywXwOKWKOD5dhOZZONFqODGoyQt47P2l4IroJn1wpymi04hD5IGeDnnWqVoZPbnWkz+cVMzKrJauqSDjzMHiGgSCiC/9qtYqXL1/eW69nLIdIgZYlI01UIR5ZdLptNajMjFZHLOSjB1azcZEg86BmWU5dWywWBVFIySin5ebm7oAjWdmI6Bx7oMnTtm3JRBSPHGW6Ysmodn0MUul7rjZeTXMbRJdykLGgLOm3nZ2dzilp4rH3j6fE0U2m2yJ3kajO++bXHqJsdc3zcfxZBsdrNHo5NrPurgBcIKhFvUxa4qFlJj5L35nX9FmTSMfc6Z0U2cpFRNyzKOoHlYyjIKIHjy04yX0iT9zKOtJw5UEUMYZP+t3RgTb4kWQ5udokAVb7s9iG+BMRHcWr+xgE1PPMO+lbORvTt6FrQ/dkxm6xWJRlZ46/35chYipjXuOYSYlyBy2DyeSzXrbkRoVjm/Wj1lc+5+13AzNGEfeuqnzxxRdpQ9hANkCvltNS69bW1r1J8hBhEe3t7ZWNWrVyuMTKI+x4glPEnR9LIa8pCQ1wxP03ppMcFXCwMrduiKTAXHG4ovW4kT4rwWs+n5fNeNqs5oLDwDHhNieFAuNZnx0Bss9cXVN85KH0LvLDMnxyzGazmM1mnZUEtZ3Lys5rpt8L5pPHuk8rkTr/Vvd7wJ6uhY+9y1EWh9BYceyoDFSGu0KuUJqmicPDwyoPR7sqNU3P66enp+XwW8US2PCswRHjlsdUn8dInCSU3D6v61tbW7FcLksMQwOik8Z8Szitoq4r41VClUF2TuzailEfsf/Z5iqVnVl//6+j7nQ/YzVte3dQ7mQy6fja3l6tNmk3ra/mCNXoOQUX+X7SIeU5RgYyYiJXH3nsgHLUtt1XSTpaE/Lyw5tkVDTxdDyDJqFkRvCfrpvQp19ztEKjwcm9tXV7XqgO3PrRj34UNzc3nUBrhlSdB2q7DP0onvf92Lf0QyKk1+lMmpAMmNWQh/vpbrH1X/szSF6m7lXaua5rAs1ms3L4jiYBz8dQ4g+ho7IA1+t1WYUQf/RCHLZH5FHwIcomDq/VPou4/KcdzUz31iQnvyk4LkRNc/e2OgX6dF3KiKnVuk7e0OrV2v2uNEZpqE2O3tzd8glP94r9Z4yCL/JumqagFj3vy/Ws1y093R3eF3FftmazWfz+7/9+/PEf/3H85Cc/KSkP5+fn914TQvTkbqIQzyaouNdV+eyzz+41nkTL5PEKMoP36XWSGigKNcv0/zWaz+exs7NTNt15u/z5yWQS3/3udyMi4kc/+lFavpJ4CNfVNyqTiOhFQOKLViZ4xGINwek5v5blgzhs5fEHQlyyokOojgpYAqakJP3OpUZZeXfxVA8Fl9mkTCYci8BqfHoXymSa1+iGEKkQZTgyFSIRb7gTmi6C7qWxklGSO9OHzFW/3m28s7MT3/zmN+MHP/hBvH79OlWQ4pva4W6vG5O2bR/uqtSWDF2AxeSsAXrWo8zeIUbkWZ8TJ4EmJVEOmZLRer2OH//4x/e0K/vFU6x1cItPjJ2dndjf3483b97cSyv39mYuQN/E5bVM+bpvzHvERyEsUqaQqPQpbLu7u2WyS7h4nisVVsZvohD9F8wXvzKFNoYeqnD0bB9xclExiOiqePCXxlPIxCe75gInLhGfXB0PjHrfV6tVvHnzppRzeHgYn3zySRweHqZKJ5tX2ZwZ6yKOSjn3Cr0x/Ow+FRvHZU0yIStfE0EMZ7DHy6+1id/7rG12LbuHZSjoyDNIhpSBfx8jxPqfPUvFIRIacIXDZ1wRUZhdmDKllfWRE4H3M77FJeCxAjrEm01pqN4+pUTjwcnpyV7ihxteVx4R3VUrfRaaqR3V4OOq748fP7639Z5zpW+OZgbgwYcVZ4Ja+z37roaJIUNJQbqf5Xnsg0pkE+FzBZi5BWxDjeH6fHV1VXz4Wjk1/tSEs09ga+1kf8RTTxUX+VK515sJpLfDlWSmODwox7rHIsM+eqjS2KTsbMwj8rifxwwktxkSzQwn+SU0UUOxbB/bMpnc7X7um6surz5+ffJMGvUGlhq0duvF70QI/F6bBEN1Nc1tsPV3fud3Stp47fnad5bt9UgpReRr3V4GB27MBMgmZC0Rp68/JF/75+B7/MGRT/aZ1/qu83f59Z64xPsJ6TOFtgnVEOoYeqiiqj3nbgr/Nikz43df/zK5y1C4t4moooY+hvosGlyOdb92yFLz/2QyKecZumWskcNk+oOXl5fxk5/8ZHDJaGiS1Or3398VTmdtUh3b29vx8ccfl5PKGY/I2lmz0jx5ypFSxG3Gog7KPT4+7uXdkJKhJXNjwd/6+Dt2pa6P+mSpJl+bjuUYA5RB/k3K9GubKkOXU85PT/QTMY6YlUfXpY96fxVs6oNOtQaIuCSn32h9xmhqDgpfKj1Ws4+tg5ZRy65s7/sglceToPSuUr9H7eL/oT7y2tbW7QHSfMP5UNuyct1StW1bNnxRwdeWO7Wxa0yaeR+5DHkZRHDeh/dJGXrqq+PJkyexu7vby/93db9qKIPBbVIW0xhyyTrPDzWo5v8OkRqlJSkm1PAe/eYd8boIz5qmiYODg3Ii19i21H7j74Le3FKtpc0MGtZoSHhXq1W8fv06Xr58WTJuSdpfU8sO9PHI7tP+iBcvXpRErDFKkG6m2uL5BeqXf8+MhBTkJlCe/fRrQ/S+FYXIZXWT/nz++ee91j5iuG99roRQJXme3eeGwGks7zba5CYN1lepuwaZkFI79ikmd1t4rXbIyBBU7WOofl+v1+XN3cwezKC695nXHFLzXk3Oi4uL+Oqrr9KMxuVyGW3bliPsvC4q01ob9KzGQXtQtNzn/ciEV9mN5BlTx2swXuX5svBYA1TrV/Zbdq/qGhr7sW2JyI/F5O+ZO392dhbf+MY3SmC/r3wSV1toLDT5+7JgdS1TbDXFonJHKee2565vfOMb5bNH5NnQDPIwuOj5GZlfzGf5O69nzGGn9ZsTB3vs5ipayv39/Tg/P+/sffF6s2ez1F5vI3nk/axRLb7g/ZpMJmXjVsSd26gUe6WfZ8pQRsL75O12HsjNy3jl7R9DY/gxRPv7+yUfR3kYY96U5vU6Yq6119ExJ/om/XHF4Wn+GoOaMXfFIb5zbvpzX3zxRVxdXcWrV6/i9evX1baNOuXcJ/MQZKeQsZOeWZppw0yxUIhpcbL6WAaVhVylTV2u9fr23aKeVVfru+rP2sjfSDV+DqEnfq5ZXL2JjXVfXl7G7u5uLBaLEvtw5VGrb6idbdveO4aQtAnva3VvSlQS3Gfz66ifVps84Sa2IaLscx9SrW00zDrs2E+W55zIjJ+ePzs7GzyLI2JkHgcVRrZMOSQkmbb0OmodycqsoY/awLDdNa3Pa4SU3u93EboMsfkE5LUhIRv6XSstTJPWM3plIYXOs1DHCHl2z6bK+ddJbduWU8KHlLf3meMwdtLz/syA1GTP64vo5sTU4hJe1+effx6/8zu/E3//7//9eyttjnizvr59+zYODw8H+9qLuxjUJOP6Jj/vc8XjgSX9VnM9yBTviEfwa0LB32puiltbvdF+DLryumrXMx5tYs2G7s+Up+8Xoq/sAdAxdWT1uRJ8HwjhIaR26J09m46b5DN7ZpPxYvpCRFdOM1nv+ywXlmPLzOCsD1999VX84Ac/6Ozbyu5lfZkcDPaz78eaLx2Rn2rujRtqzNhrHFD6c2KWrmUQrGnuXiY9ZEX129HRUWoV3iUHQYLp0NMtRo20fb+WhpzVF3F3ajVTpSmQNQH0633t2xSlDNGQK5iR2sz3245FPX1GUP8HLfC/4ic3/4myV1ywfTXlwX0scrP5VkUpkrZty5Z4vffYN1963VRk2W9D/R3cVp8JkGtQVpjRplYse16/ZRuPaDGcGX0McnIL7W3rg7oRUTbc1SadWwFasqZpypF02Z4eKgyHvjUixHV3Uye5+/1N05R3yngOjt87VPcQ9Y33pkQLzXTvDLH2oVP/7sg3u58yoyMXa+Rl0SBJEXDeSXlIPtxY65yZ3d3dODo6qr5WwedHnxvl6CujUaecZ0qiD/K/q6LIKBtU/a+5MyTf4+I7FPm/r601IfKXD9Xa78+2bfdVEVm0fxPrX6uXiOPg4CC++OKL+OlPf9qxLhpjBjdry368v89lrI2JI1hva8T4FTCvh4KfKfxaikBmacdYXzdO3qdajlI2pz7++OM4Pj6O09PTcm29XpcjDrydioNoBURtYDCf5ftc0b0u+++kOLxCVjA0oNng1aw5n6mV7Yqhb71ZqGRIq+q7u11Ze8gDV0J6pm9jkpefCeMmCsGpT7h12E7EbVD09PQ0Li8v4/Hjx+WoOyWI6ZWNLJN9lH+d7Q6VRXS3KGtbpuyzrfib8EbPZeNAvmeb8LLv/O98UJm8lslZdr1vHjx//vyeDLJtfe0U7/03xhX39vbKORtZeoBvRq1Rr+LQ+r8EhcqDjaoloui/d8jv5feMaRHd80uz+zdBIGqTTwJHE/ycbShbr9fFPamlU7tV7qM+xVnrhz9bu49tvL6+jp///OexWCzi5uYmjo+PO8uFmcLQpPZJSbjsCC5rX62dhNKZ4GZjXeNrdm1IeY1BFlk5Q0awZiBqz2XKaMj14eesfVw902s8lVXMk+tVd5Yl7DQY48heLMPJ5IJGLUZlQ2a5gvCOu+BQYGuJUhkkzcqnNeSBMn4fr2uvhermO3GzCZf1KWtzjYbu4QnkjFPUhNqT9yQw2YlkmSIWSXm6HGQKopYE5e3js769PKO2bcseGb25nURZrSkPR51jlUZGfW0des6pJoe1+8eS3By9JEuHWCsBUP3n2aq1TXKiXsXhbxPzwff3h/pAqDHud9UyKoUA1FEXALeEzuixcNb7MYSYVqtVTKfT2N/fj8PDw04MYDqdxsXFRUehvYvLQartP9nf34/lchnHx8fx+vXrKix2HtaQH2mM0uLJ9VLCKpcrOG4sxpRPq5e5HE1zd65tjed9SJOy+S4Kw9s0tn5/zo1fLc1gk7awPPJdL8du27Yctdk0t5sQF4tFnJycpMH9tJ72fUn5B/pAH+g/M/R+9op/oA/0gf4zRR8Uxwf6QB9oY/qgOD7QB/pAG9MHxfGBPtAH2pg+KI4P9IE+0Mb0QXF8oA/0gTam/y/4OR0ZMwR3UQAAAABJRU5ErkJggg==",
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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "tracked_labels[0].plot(scale=0.25)"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "tracked_labels[100].plot(scale=0.25)"
-      ],
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -549,30 +348,57 @@
         "id": "nDMnJFmFCszY",
         "outputId": "90b984e6-b6bb-468b-eb66-2b0537758c44"
       },
-      "execution_count": 7,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
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VQDV1/Ir403S/yVJ7C+V9dl0mVHcCPjJZzCxNGN2/oysK+kO9GovhYCbtR5sYh/JTrXAbHqkSZ/NiNOkYIw1qs/SqCEQVkMYQ1E1BDjebjb311luVJDnQGO/Dz+FwmK7l3P7ZbBYaC+0vf0cIxv+YVY1xHd1qd2wbIc9VrPcj5REplhyplVMLYVbNYFyv13Z+fn4DMqs1LMsyTXz1H7Gq8/k8ZehRJkumtHu5XCZr5NEEdREjgVSQI4W5jyLV5+reof30CSuJUtTMyE5nl8fgczvm83kKvBEHAk2hfMhspC7aBs8gHQ9ddcj1qenvHJrQSUi/R6ORjUajSrtoP4lsTHwgvMoRcuVXq9RN3mw29uTJE1ssFjYajSrBVe9WIC/evYtkHYXmeRrxQJdxcx4B9Z6cnISyk8qqvRsUrH/nJvc+FrJtfbk6I/TCYPq8Dpb5PFTTQaJMDQh6pYZ1ZWJAXON9b20YYC+kHkJG/WtyX9ooDMgLEO3F3fJ8jNriN/2hMJmcuvRMeT5arzt4/bhqhD/qj7fCOaiuvPGTin0vd+7csbfffvvG+7hbuEK0V2NeV1dXFblCSRLj0k17T58+tWfPniW3GuTqs5oVuWj/I7nVH+1vTkHkDLMi6qIo7Pz8/AbPlfaOcUSDvO/7Zjd9MLNqcCwa7KiMnC+H1T8+Pk5l4xqMRiPr9/vJT53P59br9ezw8DCVg7AcHR2lCQDi6Havz+1gsoFSJpNJaps/EEXdnfl8nqByLlluH/fD87GJ8NvZ5s375FdEq0UgL/jCRjj4zeQ5ODhIE4FxYLWJfR6Uz2ZCJp1CdXiW62tdn/cxTNvt1p49e2bPnj2rvK+IQPenIFfsw4GffnUE5anIc71e27Nnz1K6APEwykQ+lstlyhzWfoJUSZo02ylY2gHfdJ7m5ogqFkUxOfdIaS/F0e/3bTKZ2NnZ2T6vZYmBiyZJ1DH/jCeW+jTQxj6NoiiSkPqgHQhCU6OpYzqdpjpRPAg4e1MYeJ1wlA18101hx8fHKV/j1atXYZp0TkHXWdK2ygMeaBIdlkaXLjVHxe+q9MoOfmtwVd0zM0urUhpYVSHWieLLjgKGOdL3GJfcO94gecNFXAOeqevAniblicZAFC0omjs8PLSLi4uUUu6RLm1mDNQYKb/U0OBW8VyuvxFC9/SZKw5iBZ8XeXSRg+d6XZWE7vxTC4CFUI3KRMAP1QEZj8dJKVAXz85ms1QPSoc2qV/v4TrtWq1W9vLlS+v1enbv3j07Ozur5HV4yNk0yPpMGxdRf1RJ8C7tVUtGu3Ed1AKrS4hQohC8H881n8Xo+xe5Z/65NoqV/jS5d9o+P7FAmuom6LI+PDGzFC9iAvf7fbt37559/PHHSS6IL4HA1EUAzbIC6CevPw0uCmirwfVLw3XKVnkRuTie9s7j+Cwphyj2aYeHXd6i6QoKA7fZbJIwAJdV4H26Oj9s0PLxirIsU/Cv0+nY0dFRmiC0kXaDVI6Ojuzp06e2XC5vTH4VYvVvNUnIw1iIFaK6XAXK1iMHlstl4hXKkzpwL9jMBW+A4noN+E5QsSiK5O+zWgCvh8NhxVpH1t5bVoXoUZ883+pkK1ISSjoRtT49C5brarS4vlqt7OOPP670B+UCkvN1q2JXXjDmOdQET73MUFZ0GJZXDr7uOtpLcfgOv44i0c7lhLypEwrT/CBECTogBCaIKhe0PtcQav2JNLq2QYXc50Jo0PT4+NjOzs5sNptVAqJMPkVRvq9YN8pU10rdKc/n6BrvehcBFMGOUL9srHtJfFQfC89ytCpLlLd3RXQ5nfp1DCN5q3M72iCvXBmQLhuDEsx2O6h1VU03X2KUWLZWJdvpXGcps/cHg4VCjs75aOq7ykqkGDxyU/LPc63NkvheiqNtxl1b0oH2jGmj9cyqpzsjwPiZ6vcRpCJr01sz3Bmz3alevK8avSiK9JwGADudTgqO8iEnb9FICz87O0vLetp+ja1EiMLDSP7GJ1Yh85sA4U+/37eyLFM2KPXSPt3UxfIpKAbE5d+fTCbp8B9OXNO4AqeqYfXoswZGlU+Ru8YpXNrPnLsSUeTq+HsesdAO/VvHCFqv1ymozH01APAYPp+dnSWDxfXJZJJ4xClsyEEU7MzNHaWmOEWOvCKP6FYp5/sMGOQ7zjV/z9fny/BEbIP7OgGxfAgr2l7X3MlDAGpiTRBmBpsBxWpSd5TwxEqNnwxHR0d2dnaWAq7aZk6ZZlu19sn33wu2luMhr/JXEQCT1qw6GVA4LBOWZTUbknLga7fbtfv379vz589tOp1WLDWK4u7du7Zer1NKuCphXW2CyNBVBEh7ParYVxYVLfr4hyp6VSI+E5Tx1lUlSBGVyqYe4KMoTTdj0j69H7lmvg3+BDyewTVExiNeRGjkM0ccNKiJiDTnyLsRuXoiodDJoxMossqqELCOGnTyihBortaas0oRCFUUvi8EEC8vLxO810HgRDLlUbfbtYcPH1bOJ/HKIjeIHPkXWVN1f0AKugnK8zQSFo/kuK+rUkVxfRJZdJ4r5V5dXVWUALzXv9WK4hLrknYd3M6Rf1aVBv/XLfmqO+bzInBRIF1+1ViUGjBFeNvt1sbjsR0dHdn5+Xklb4M8H28YttttQsDz+dwGg4FNJhN79epVknGUIajwwYMHif+gxMePH9+IsXheNc3Poo7xBwcH4c0mH7HJCvj8/roYhye1mvoMvxF21fBFUaRcAgYYODgej1NgEMvPHgCeXa/XlQGj7Z1OJ72/WCzSsuvx8XFFacEPlI8uz6pl8QpN+xxd932vGwsElj5i0ekrvjk+t54ohsuiAo3yGI1G9v7779uzZ8/s7OwspdyzfXyz2W3480u6aslpZ85NzbkaftXK9z0yKB5Ncl+fIcalxoQ2qjFSdMWqiKIZjY1gcJApyvVb3X1KP+TzNDQG4xWHmdndu3etKAqbTqf24MEDm0wmNplM7Pnz50l5wCdVptR5dXWVFazPfJObdsys6pYgJLoctw+pcPnrEVT3qIQfPbuDVRGFlwpLKUvdF4Wzvj5VKr5NKiBq0bxF9/30QVJ9xqx6iJEn7x4qVPYrNPoO/dd2rNfrFD/SMp48eVI591Wf0wCuTjZVZsrLnEyoAqRcP5E96UTySIwyzSwl83E+rU5+0BrKnkAmbV4ul5XgpvJUJ6Ty05+boat1ESLS3x4lK898pi4Kf71e28XFhT1+/NjG47GNRqOUgKbl+AB/HX0uu2O1M74B3I9WB5QxdeX7cr11pnzK9YoM35QcDHxFbY9ZNdsTQQI1cI0gItZUrYoXUgRFEYTvj1ce+r7+H/m+dUjQKxwQgSoJ/QyAt85mVnHX9DwOTZumDMol6OxP9eLcDvqgVtQrU1WyRXEdjGW85vO5XV1dZWUl4qvv9+XlZYL9FxcXttlskguo1l/PE4UPusRqZpVlft3notnCGrfhwGc9BBo+alxJeVeHQLXPZLdOJpO0fwi0ry6xNxBeriJqPDowp3kiIdZG1PlIfp+E17JtSJ+LEIgG73xGKAPa6XQS+mCLs1n1I8s6SKvVKm3+wTrpWRysWHiFELkUEXLwfYmE3ZPW1YTkFHXgLzMBUGj+Wre7O2MEHvAu/dWYB6noPI/V1kCiQmTak+ON5xFK/ujoyLbbbVrSjqyk8kNjOP5Z4j9mlsr1QXNFWb4MlXnkDd74yazL3/q8Gi51ZSPZzpHvF4pDV7AGg0H6DEK0GteENKC9v+RW11A/aTTI6OFQZI3buC9eK3qtSx0EKvXgV+4rbETAVVn4ACqk1pQB1/bn0ISfKMqLqF/6XBt+KHyNxkjLpY/0XetR3tBuchD8ePn8Dd7V08qUx758lDVWlne8sqQduDnsth0MBvby5cv0rZI6hMt7yEWERMpy963ebrdrR0dHNhwO7enTp2Z2Hd9RWcE4qQvMNd07otZfeVeWu1PQNJgKCvaGtw5N5sa6LK/TDy4uLtK4F8XO7a4ruyk4ute3YyP/X+9FwRb+zwn2vuR9xqhc7mnWKJYQASFgx7kHy+XSZrNZ5cRphJ5rfH7w8vLS1uu1vfvuuzaZTNLA6wEvXil4HoC6gLX8qGLV/+vQhvJe7/m/UVZMIE1eQqBBFFq32c5SktiEkkDYgeDsJlUrr8FUVQwPHz609957rxJ85L0cVOb+6elp2p1aR8pzb1lzssMYj8djOzg4qCBjVWbqammZmkquRiKKIehSN8pYV2CivkQUKUyza1dOP5Ohf0fPt52fe585qtYJ4faC4plsVv3kX9ToaLWkyeKqT+w7jaCq4qB8YBvuhbozwEx1bcx2H7/RSPz9+/fTsfegD4JrwMSyLFPdOnEUmSgfVAChJu2vz6ni4ccHeqlrtVqljVooAviOMKNk1I8nroPi0M2F3uoNBoMUN2C1BXrx4kWamDyvcuP5oehFt7vzbsQ7Xb1CQXl58+/AsydPnthwOKx81c+7s5oThHJ99OhRWol7/PhxBYV5FKXJhioX3n3Ttnnlo+OqBufs7KyyAsShVTnEBW/0eo4ag6MIvA6CFqwowysLHRSsm6bvNgmJr8uX6VGHFwCdnETYNU+DMrQe7yaor695BUVRVATC74rUvAUVbq0r8u19jotayZwg+bIhPzbKKwKW3vppO5ioGjCFdPtBpPCZDMQ3vFtTltdH8M1ms8pqgu9HJOD875dT/bs+90LlpQ7y8zwGodPZJQqSHYxCpW54d3V1Zc+fP7ejoyN7/vx5ygzFvdJNcTwPaoNv8N/Li++/ykqv17PRaJTiUYoodWMip8srDzwSrOMN1BgcjZYKIZ3EHlrqIJntziygjEjQdZJEbYkoEhjaQ6Rco/zkKDCA5C6Qo6DLZOx0VHjPpEOjT6fTpDjY1ajowitYXTUwq0JV3UOj/K3rq/KzCZ0hFFhK+szYKBrQgCnPsSEQPmoCFO3EonFtNpslVKPPRj52bpx9YJWAti7DepmBrz6YyY9e18miLoWZ3YgH6OoPP0x4yjs/P7fLy8t0GDSywzPaHjYF+hPr9OjKCJ0qch0Oh3ZwcGCLxcJevXoVxpk0HqhzWVcA1fVqolrFoYG0nGBGFkIFJ3q3TqtFk8NbnRy01efV1QDSMmHMdgKhZ3SgNK6urhI6Mrs+IYpUcFWmQEGI+sjQVKWaE3Amoe5l8P32KATybonyRvk4Go1sMplUPqykwsI1zujwE0vf8QhNrZaZVdAcbVBk462+RyrwQBGe9ldXd0B3kSsW8cMbM+6ri6VoEZe10+mkPTg6NlH+zGg0quyqVjdBea191Umr+4G8kVY+qevPV+Y1YUznHuMTzRMyV3W8c26SUuuv1UeCn5vgZjvN7S2v71BdvVE9de/7axrYHA6HlU9QFsXNk8w3m006HYy6NpuNnZ+fJ2vMNbU8KrjETbDMkPra0aT0qePaH3036nOdu2J2fXCMHiSjVka3B2g6vcYA1JXwKwbEcxQew1ueBxmw+U+zLFGa3j3z28BpK4IN/9VdghfqovCM8odnlH+KZnjOf9MWXunY81t3/2quD0qB69pmvwcIfipyVYWnxxDCU5+er2PllSm81T7xDR9vgJrclVrFobkVUUGR9vYIw8Meb2l8fT6F2EN1j2iiNvI3Abxer2cnJydJsGEy7oomxHhh5t6dO3cqS7UIA8/qqoG3LhEK0Hb6lGP/jF/FUmHXa57P0KtXr8ysGvOABwcHB6ndCnF1khDYVHcDFw8B1bwYJoCfgF4wVYHrhPVt1fK55+UiEnompp4EnkOpZtVvophZcs3UCkcoQ+vXgKkqOM/X3NhGcgAvQIRcK4prF3k2m1UQsvKgiSI+at05utW3Y33FXtDbluEbbdZuA1xUr7+nUW+zqiAi1ASMqJfniHTzvPqzGmDlt77jhdwLgreCKDivEJW8e6DKMjcJuO+VNO/7Y+sIoGl/VVARUt3tqfzGIupE0JUMs92Kk1fA0Zh6BaPQPuqn/oaihDg1aIoW1ejpGHS73bQPqSiu9zzRT1DicDhMvCEbFJSLTPA+uSscegTf1PVQGfFypNv1y3IXI1HU1YToI9r3nc9lr0pEOhBRdFsHP4dIcuXmmEUQFCVRlrsEG3xYFMdkMkkKgGxQUoFpN4Pf68ZOAGMAACAASURBVO0+LcnpWWZW+VD1o0ePzMzs6dOnCVbqBCuKIh0EE/mV+/zt++6tkn+WbE7/JTaCebxL/MXD+qIoUjIcCI24kG7mQuFNJpOkHD0SzSFZrY+x0t29bY0axiPKItUJ6l0es92p+KAt3A7leVmWSVFovSBdvYbCof38rc8ijzo2Xpl55eJ5Fv2f48++Cgba+7sqnpoalutQXXm5Zbmmdz2C0XgCy376HNuN+eEd/YYIg0dyGGXi2uiXrxCy9fr6aEB1QTTvYDwe25e+9KXKSoumq/u+aKR/HxTofX2f0EVfdWVA4z1MFPrM8qRPEtNlSeqF9JQsnXi+j/Baf+vknM1mlQnuy4h4o/1QHigftL1+RYy/UYgoEDUALMNDehoYRkaRqeYyaaIXp5qj5KJAp/bZu+URtZWVfQx1KrvuBbbV1z3T1hWJFAG/GTCFYFGkXN/TgY/iKDpRiFT776qY7TQ8A6hRdc0AVHTChInOjtSDgzQT0w8Q7dBJ55er1aJojkg0BmqFtD9qUf3zlKvt4Bqkwe1IsQGzUUAaNPUTkfcYay0/Z2B41suEVxi4WmwSg0/qGunzXkHoSoi2Q/kBj30+j++3joG2m75ojEzHHWVCPSjtyE3TueJ50kYJ+Dml7jU0m82yk/svzVVRquugMlKv6eRowxjgImXhcmAxy7K0w8PDZGWZOIeHh0lJ6AEoevwdLosKBmhiMBhUTl2KlKCPkvuJHQkv13Q1QgOKXtno6gaZj9oG4DW+tS5Na9BYFRduyHa7TaneWEu1kmpNWalgDAg4Uqa6B95V0D572VGDwW91CeET/2tcSE8/i3Ir1Lj4OIPywyMVrus4eMOk/VRFwvuqIBgHVSBexr2stEH00bU2uRtKrTJHPy/yzIgmTIRadJJ4YVPSNX/9Oj1lqJAgaOv1unJGB+ViVfzaO9YAAVmtVmmlRcv3OQlm1e+M5NCV9lX5pMlPTPYIyhOsU16qBVbkoAKN0oEnXqB5X4VaecmE0wnm64/67KnpnjckqoxoQ507o+jUo2DcDAxLp7NLhivLsnImK8FxZAEkRrmqyNXd0QA17Vfjop/oaMODtgrEzytFlm3otRBH20puW7Yy0ENxnqlTHP7cDY1Iq1XpdDpJsahPqnELNrEp1OS3rp2zJKkKRpWUTiqvNHz/9X11r7xPqhNF380JT1mWKTWZ5Ty1+hrzgA+Ui4Il1wDeeOtJWbq0reOlFi4yDvo3z/r4iK6aqTxo/30w09cHX3XZVZGKWTWgrxmi3F8ul0mhaBzo6OgoBdVVHpDHq6urJHPIn2bm0g41dtpmlQ9FerpNIEeRS9gWzZvtoTj2URJ+Ikda3h+Lxnv6jFowf89bx6i9GqcgIu+/DYKgEJwqiiJliKorcXV1VTk3wczSTkPtY1mWyU3Ra7R5MBikT0n6/qsweB74Pnq/PbdUGZHmZeinDVGYjI9XiKroOL9En4Xf9As3RVcTNJ4VUU4mtB1+74zyxscNPLLxfMRtUYWne3YYM4+sdImZZ0mGU/dVZdjMKm0HpfgYHwaJ9jPevKcreZ5n+qGsNqQ83Me7aFQcdQ1QWBg1Nveuz9VQTRopnZzygCJhU8RitlvTxwoxuFybz+dp0oAIdPAU+fh6VZERiOU+VsDMKqsWqul9u7WP7IFhhyrP+VWffQgIrO/qpND2+X6jaFE2ahGZLB6em+0OfeYalvbdd99NH6bylMvpySnTCKEqn3Vy+jpUyftEK1wPfV/RKH0dDod2586dtJEMJacKlXroP22kDap4VXnrrlxyN6I5QJt83Eyf+yw8hddajs0JbF3DGBhVOLzDgOSUh1o9b5WjgCqQjeU0hJzzOPQsCg4c1sHjWDomMMqE5VrK3Gyudz9q1iRtQPDUp42QVWQNlR+eJ55/vu/RPf0fHqIs1cfXZUKN3yiE16++KfrQPSQa39ADi6lns9nYy5cvbwQolbwrokhQr9MnJpuijogvfgx0VcHz3bud9MEHtK+uruzi4iKhWt7XeI8qAP7nzFPa7RPxaANIRMv1Rk3daN/3OkO+rzJ5bcWhE7/tO2axQETQM3rGQ3jfcRQJygCBNat+MFrX5VXzKyzUVQfK1QHUAdaYig5ozpozcXUJT4nYC2d+REjFT4S6/1UBoVTH43ElnhOhKkUSuHT6HH1nRycWEWXLCpWmnvMe+RnROPv07pyS9TzxSMOj1ZxiZSx5ptPpVM4IRdliODAKrDgR39LMUUUVfqzVfdF6lUeKglVpKI+QPy03MihemVAGp/3vQ5/JciwMj/xszwgVmrrVBK7lrHOESjQyjDB7t4G6FZrqQGgilioQ6vRWSeMAqmx8uV64Pe8iWLler9N3WHg3KsvXEY2BvqOWDH6Y7Y63U3hMu/RvPUNUFShKV9vKkq/GhnTiR6hT2x3xzPerDRLT+iJkF5VNm7mvH4kys0qsSr/EhqIFkZpZCqyPRqMkH5pkx8qMnqpGndpuHWf4G62m1fWRsTo6OjIzy35vp45ee1WlCTLrgEXv67NcU3fFv++j8Z5BqjzI3eh0dgexYDF5Fv+VQWZTFIFU6l2vr8/IHI/Hdn5+fsOPVL9fz8CkHtqRs4a5ie/7HVkTRT7+elR2We6OTMSSqQLTlSN4o9AYYfdCi7LGgPhPPHqhV6Wj1/0Y6pfIvMyo26r8ysmHKn/lm88wZcxBxhoI9u0ty+qpcrpvCV7AG12pogx1LzieUleMtB9qcDWG4hWD8sLzABTDQc9RqkATvZbi0EHaV2MxYLqrlOtavmeICloOdcB0hAH4aFYNkvLxIP7XTE/tkwZYfSDQt8dHtSP05PsR9ZXnmGCRH5pTIN731oAb9xE8PfbQIxP/Q3ksQfuVCz3cRxWSuhyR26ATlrKjDFlFgl4momfr+Hp4eGjT6bTSRu8yMZ4oQ+RqPp+n2IRHBPoJUSVVyrqah8IZDocpqU6v+3ml/+eQufYhInV59lmNU/pcM0dzDdf7TZ3OwdSmMvXHowPyFyCQgloFTfbSYJjfks8AUD7WWbV4TtD9RI+UZ7QCpNbK88grDbPdEiDPluXO39b0aW2HPu+vq6JRF00Vq/r2PqDnIbVXtGpIyFblf8ZlNBql+IlXxJptqTzSMrbbrR0fH6fDmAiUa4yL9iEHukrElwD1DE/dcazjDAphp63fRV0URdppyz3dWKgKM0JmOtbaT79k7OUlUqpt6bU3uekuvls1IIBZOaVRZ1n4m8HYbDbphGeUh5kl9IHPWZZl5ROOal3JukQo6CcKRKEkqyrU7wNW3nJrP/Vv37fIrVGIz/vKC/IJ/K5WlAX8wR1T6K4uh7osaumxmpqnoCtOTD6CpboC49FPNJbKO/06mj6H2+njEDr5cnwvy9IuLi7s9PT0xrKpKiEOdaLfugoHb1k5Atl6t9Rst2vauzkoCB1/fT+a2N7dRbFpHz3dVjnU0WsrDiLI+0x2CKFTdKDLYjnyQhQJnPqpQH2NEwArPQSnTBWgXq+XPpun7fRr7n7wvELwViY3oHVoyyMcXyeCjJWjPPa3aBo1e124r3kWEOV4nms6tdZBgFWXZVEgGg9S5KFokPtc93kv8B4FdXx8XFHo0Rhou7mGAtasVh/f8XkzeqwCitJ/EkMREx+tWi6XNp1OU+yLcWAM6LOukDCOkcKMFEokK7nVlc+Cbu2qKITyKbFQkw/GPb9ZqI3yiPzWCB4iDFxn8uuHl7FcCgfLcpcdScSbMyd8nzqd649PIywsR3IvCoDW8SQSfv9ehDaw8ixFe9iNQNNXtYDq0vn6mLwoBT6CxATzeSuUt16vk88f7cnQoCDlar/UrfKTgPgMcQG1xLlgH+3zyALiniZ10U49VFifIR6hbg7Ls/BrNpslefT8Jpjuv6CnO7OVF/TVUyRPvPN5II5bfa3erDqwXqtHFjMHTz2pj1jbcFdHtA7uI9dcgzQugZIBgiMAam2ZJOora5l1fdeVAoWqdRTBT0Vpyge976G5Po9lox+6iU0Dk0xoXUY1szRJKFOtPPzmN4qD5xR18LcGxzWhTiG4bhrUSaeTW79Fq0cE5Ig2qtJTo0P5uopEfxS5QqpAVEF52Tk8PEx7VLivS7zscwGpMSZ+THXcdcz3URRNzy0Wi+wDeyGOSPsp5RRFpDRyjfYWOpoY/nllmGp0VR78+MlutgseeaHW/9XP9i6WCpi23yMDrzD2hZHqVqiAmllKwCLhTVeVIuVB/4g/qAukwTS/scyjCu86qeIsy/KGK6s8VXSpY+2Vb6QIPJ+Z2HU81QCwTuhoLJEPrmt8QxWlKhYtSzNvzXanl+tKDu1QpId7o2hHx1/lSuMgnFynMpJDXpqTo7SPPO6lOFRh5CY2FFm+tqQWKmqDh2qRMkNoi+L6nEgEGzTBUYF66C4/i8WisizLzkfQiNn13gt8bQSWWAhfBmdgWRloOlvU94nfCo35n3p0gqIIzKpLbnquBklGy+UynY/Bs7pRDeHWVSQVVr/ixCRRdwhSBEd8xX8GQBWrj1V4OaCfGk+JVhV4109i5XuE3FCeHm15heYVpTdaPGO2y+NQd0Tbqm6fBrBzpApc2+Lnp3dxlA/azn2N2K1jHH7d21OT0vD3/GRqUkg5iK7lex/eWzYUEIEv0q91JQYLwAQiEKZaXYUzanv0vc42boqWVxRFxfKihHhW++snQWRNUaqTySQ7ef3GrMhiK7ynflVSXGMiKixXl0iRhyIp2kL92j7GyU8Mz2eUul/21vs5ZY6iI8ZB5qfGbXQLPG1DUehKmCIJbYOeVQu/Dw4OUtawBoOhpvmhSieH2vcx5p5upTjqkIZ/jt9tNFpbdKIuQd0z5GqoVdXBVJcFP1khKm1SC6y5G9xH6CNXJIps54TU91stuXcZ/HuRtdFneYbrejYmQs7k1/+1bOCw5sAoXGe/hqIevw/GB2z9smPd2HtUqX3VsYx43lYGzar5DyAA/QyGui1mu8Ob+v1+UgKnp6fpXWTv6uoqoV3O6OCHMrnOGPjjDXxuCjyMxt0rVAxgk+FtM7f3dlXUSnuqG5x9FA1/e3fFW4robyWEVvcUIATaB57DgplZJarNMqVf1tWTu/1avrdkSvvCQu2/Qk0tzysGfce/b3Z98rjfP4JLxDOk2avSVN5qFi3vqG8eWVfli1dqoKao3xEvvbLJubfk9LRBeZSncSBNuec5+KXG4fDw0N599117/PhxZalfV/jI91EFCuoqy92xDHpqnXetPC/gX4RI9Llo9TNS2q3mah0z/aoKHWPNP0e5AW7juuj/3jr58qJ3c2Uz0Orn5tCCd0PMdhu7/NfgcvDYbBdn8GhAoXcbdOX/Vj5GE7COL1qGKkEET1c/dHKoNff1aCIYvzWuoqslPo6iSg+e+YSvOlIX5zZ7Lui7D3wy8TebTUqmI+dCx0/5jAFSlKArMbgbirboL4hGN7rph8GpJ/pbx8IvFvAsMv3WW2/Z06dP03Xvyup4XF1dfXarKhogrHtOGarX97G2DCi5FNPpNJXbRjtSX9QeFVb1mxXeaSCQcsbjcTrEWAXArKoolHKuyetSHWz39xGKaPlRLanGINQ/B3Zvt7uDfMyqY+0VKkIZbRvXskE6GvNoUoC+7RpTqFsxaEKw2i4UKKeYKd/UAED+aEpduVL3RtP8NZmM+hWt0M5IEUQ8UeSh4wACfPz4cUVZqKLxPKmjz2WvStNkbluGKoeTk5O0a7XOZakrPxJ2fV9jH3qSFYLEuRhal6IiFSqvwalXrXcbHjT1JUcISzRhKRsLqbzIwX+Pavggk+YuaB8pH6RBir6eVcJPblLUGRq9F5XTlo/Kz6gdOvF83ZGBUKSsy6y6BwUFh3I6PDxMn6nUvnh5VVSm9/wYe4TLWGg/NFjc6XTs6OjILi4uWhu3Vooj8onqKoi0ZZs6/HPdTmG/9pXCfu4LpX1alvaP5nfs1en5jQmvdeYQhv8/KkPvKZO9gJpVz4CMyqsTeN/OHCmCaANTVdBzVsmX7/MPNFjp66VclIDPCOV5VVjKM91S7tvoed3UdrObRyzk+BoZF/+M9jfHf98mJiQrVPrD+xp8V3TF2bPL5bKy6oZ7om5LNP/4W5GPVyDekCmh8KGrq6v0HkahjlrFOHzD94E1bRWHVzadwuwf/qcd++bbHRv3zRabwv715aH9h//r3K5mi9qJ6jW0f1aRhDK5zfKyCodXHp8X5VwPXzfWjCSwCJn5d+iX38auRwHqJFchjZY3tc0edTGR6lbEIqMTjbPvt1k12G1Wv1dD2+fjXArlfR+Ufz4xTRVMNGYaN+JwI1ZafPuLokinv/Gu9knHgnp8OdonzzPPB20r/JtOp1nBrsXMusFJIc4+1BZt8NEjOv+rXy7sm28Xdjgw6xZmk15pf/3uwv6rv/c3bDQaVZb5KEPhd86iaJtUEBS2+3Yz2BpI9EqpLcS7LUXt0evdbjd9zzb6qSsTIVSYPBwO7fDwsPJc0wT2ZfNMEzrTd9o850n3u1CfHthUV0aknLyxixBMhFDgncYU9G/eZXPcdDqtoD7eBdVpgh/unrbBkyqVoqiezRopYI+sFf018b1WcSi0ojJd0qmztG2hO/f6/b7dvXvX7t69a5PJxL7xdtfGvWr5vXJpf/vn3rEPPvigknTj26HBtkjBQIocdP+GLkvqM1yPVlLqJtDrki8jQlCTySRF7qPntY11qFGtK3t4fBkRL9u2X42QV25tlHFuJUqfXS6XN84yVYrcIK8cm9AGKE1lLGpP2zlAnWqsNc+DunNLzpGs+lhIUzsiZZKjWvhQlvVfksoNLJXnfMaoHFYqDg8P7Ytf/KJ9tHhhs825HTrVtt5s7Vd+5VdsvV7bxx9/nGA5P3portlu01CTENFe/12QuklWF+Rs0+/Pisry+kT2OqVhZjeETyexf2e5XNqdO3fs4ODgRkC4Len45xSBf6YpcNxWEesEi5RjRLrqoQpKLTI88AqmSb7a8m273Va+10MOiY4tckqcwn+fBQWk41ynLPl7H7e7MbzfBEmVdFeh19D+SHslOjefz+3Fixf25MkT++PpI/uXL4e2KgZWWmFl0bXCzP7a8//d/u43Htpv/uZv2je+8Q07PDy0Tuc66/P4+NiOjo5sMpmkLfGRixFZtrYIyZeR49nn7booAX1zqEDdsbZtXCwW9vTp07RtvYmaEGWT8VFrR7tvS20ngLoGWHiQRJsykPPJZFLJpo2euw11Oh1799137fDwsOKe4vpQrqJgkKLuR6Es/tfzdSMl2KbvtcHRyWSSbupkyxXqk3ropL7XZkA7nY4dHx/bB1/5sv0Xv/ZX7d/9yqE9Lu/blxd/avd/+H/YpjO0f/7V/9r+9Oqu/c7v/I595zvfsfPz8/TRH3JNOChFk5GgnMVQIW874PRJ09H9u5834qAOj/K0/rqs39zzbfzdXJ0RWmsbd/DP5Oq4LUVIjL81e1Zlwcs39zjbxa9ERHWo+5NrF/dGo5EdHR3Z5eVlCpLyDHwcDAbpy4OKJr1LMxwOK8FcyvArMopQ6oKjtYrj8PCw9P5nE2mnbjuBENq7d+/aN7/5Tfv5n/95+5mf+Rn7ypfft7f+6X9jD3/0f9rG+vbxyTdt++pj++PTif1P/6xrnzx9nna26jH1dUpD037rfOs6heCFIhLyz0NxtHEF/QSoW9Goe8+Xn/Od62IG2uacn/6XhdRyioP/9egCfjSnw7tUTXEBP0l1Xnle6T1VNB4dlGVpBwcHNp/PK+eYIs9KPK/3ueb7Ci/qMkdbKY62lBMmf60N0cHBYGDvvvuu/cZv/Ib98i//slm5sZ/4Z/+tfeHptyy1rDS73PTsP/jHX7KP/uLTFNPIJdPo38RD9LxI33b+jqwvg6uC5L+Joc9q//xy2r48ajPRsJ63Tcf2yiD3d937tDWi6PpteHEbipSatgv3WpdC1ZrrmGvGayQjoJI6vrVxcaMlWNqUM1Y+U9THbHywn2dvvRzbxjL5hkYW77ZCAEM++eQT+63f+i178uSJbcvCVgdfsNLMCn4Ks8Pu2v7LXzT74IMPKsuS6vtpOzQSntsb4RWgWu3ouDxIDzn2rk/ODWqCsHXU9N4+Zaqi9YpT6/KKOPezb1spk2fbukq3IS8T2m6NI3i0pM+bVZdBc23zxyBEdfprniKZruORHn2h9bDz2SuxprFTqlUcbSzKvpQbZJ2UWi/r19///vftj/7oj+zFixc2fv4vgoLNfvbu3N555x17//337f79+zdOwIZgnJmlGEhdH9HU3g/0fdL2apQ7UiB16KUtdbvddMhQjtqcjAXh2kW7b/3/+mwbIYb27WNuMkao9rMmxlPHbR/kBEVLyH7PUGRgcspEFZqmDvi2e8TLmOXiMRFCz1HtcqzfAGP2+n4o77KhKaLIT14sFvYHf/AHdnl5affvPLD7wXufdL9ox8fH9urVK3v48KGNx+O0TPnq1asKRPNJQ7l+IaRATd28penZkSXWPngY/Lo8BI1tt7svfnkf2sxuRNebKFoORZj85jeEzceJiBG8rrvR9v1Iidddvw3l9uOofKgizaGLOmQZyUUd6ouQtJalytz/H6Wn+3bW8qPuploPZVCuUI6iazMpdOnLZ7hpBzqdTsrtuLq6st/93d+1f3TxVVsWYyvN0k9hZiNb22QyscViYaenp2lV5eLiIvXBbPcNUL2mffQ/o9HI7t69W/ETo+dUY2sflCLl0oZ0J6Uvl6xRXRL0PmuuDb6OXKDMC5iiNs2MjPij5Hl2G4ra72MObSF3HXnECOr07UdG+X5srhzaZVZFDVqW8q8NGmnDw0jGtS3+2TZltt7kxqDkgmxFUbTaHONJN0lBMKbf79sXvvAFOzk5sRcvXtjHH39sZmaHh4f2P//q37df2nzb3lp+ZM9XQ/vZ8rv27xR/aJejuX3zp6b2T0dr+60fXG8aGo1GKcHM19PEyE6nYycnJyE6Uiuk1iTqi68r5+7kqN/v27179+zi4iJF0bHsq9UqpS+b5Q+08VQ3qaKy/D6JsixtPB4n6Ksb5SLFVdemnBC3pclkYg8ePLAf/vCHrd9pIu13bvJBZ2dnKZs55+pFSrRJsdUhE1Vobd/17YhksJUyqmu4HuTjNxJFRJpr1MCI6hrd7Xbt7bfftqIo7Pnz50lDdzodu3fvnv36r/+6vf/++3Z1dWWz2cx+8uX/bX9v+G0ry2sEMtsU9ifP+/af/V/H1u0N7PT0tJKGrC4TA+FTdIvieg/Nu+++ax999NGNw4b9wIM4PCyM+qfxEqWIZ2W5OwiZ8vBf4XmUX+DLUwThJ4Pvh/KA+9o3rvFeXQwo1y/fR3W19kUjnU4n5TT4Pr0OulF51GteMcJbf8xjHeWQqZfR6D3q0w16vrw2iiO6xzjOZrPsg7WIQ5mTg+DaiFwUuk7jRnUOh0O7f/++nZ6eJkuKdS3L6/T0P//zP7f1em1vv/22dTodWwwf2qYsrFuUVpjZQa+0n72/sr/1hbn9zuPrU8FJnaYOlAX946tbZjvhZQ+IR0bR5Iw2yUW8UEHTicd70cTbbDY2nU6TuzgajZJC0S+w1fnIddeani2KouIqwTOPpG5Lqpj2QWLaltlslvXPo+u+nkhOFWH5zXr+t8/lyMlLVKd/rm7S+3bmlEQO0eR4tA+1dlW80EeV+/+jvIimBm+31ycuPXnypJKUxb2iuN7q/d3vftd+8IMf2Ne+9jV7//337eHmUys6VSaNeqX99Ydm/+RH6zTwqayytHt/7W9Z/9FX7HhzZsXj79rjTz+xy8vLisW4e/duOgHKn+I9GAzSyejPnz8P++tdGb2u/i3brBF+nvN8Rdmx5FsXZI74Hgmdb6+iMZ7lR7cVRF9og9rA8OidXL/3ed+XFe1baaNgPd81EA0Pojhg3dxQQ1EURWXTqCqpqP/Ib5tFi1w7aIPe01yQtrT3CWDaOO0IMC03MG2JDkSam4HabDb28uVLe/fdd+33fu/37Hvf+56d3zuzX/rpjk16u/fmm8L+9Wm/MgF+/IcNf+3v2+KtD2ze7du617H3f+Hv2L938fv2g3/7b+yjjz6yTqdjp6endvfuXbu8vLTJZFJZ1nzrrbdsMpnY8+fPK64U7fSTMBpI5VGbbdP+WXjeRHVp3k3K3LsmqiyaVqX0WpMbpRDZT/Imisq+rfxFbVceoDxYlWNTJKsq3I8mIX30yiaSkZxxNquGBOqUbE5paJ9uizwaXRWtzJNm0tU9d1vKMW673drl5aWNRiNbrVb28ccf24tnQ/uPH3Tt6w+2NupdH/zzL18O7f95PLKi2H0cqSgK6733c9Z5+BUruz/+tue6tO+fmf3ST/9N+8//9q/Zq1evbLZY2T/4V2f2w9nARmcf2y++/GM7P32Z3ITnz5/bhx9+mBBKBBub+KeDl8t/yA1szgVS3tW9H7WVzYLb7bZy8lRUdjQxoqzEqL7IvWnjxubIt9MrEq/82lAO9WmcB7dxu93aeDy22Wx2Y5dq1P+mduTQWrQ6o32LVpU+L6oNjp6cnJS573DmBCOCUf692gZlLDIMUj/zwYMH9s4779if/dmf2WAwsIPJyH7p4dR++u7avnc+tG8/ndh8cf3VMqC9mdngG3/Hhn/j75oVN6PR797p20+ddOx3f7Sw5WZXf2+7sp/8F/+LrRYzm81mdnp6apeXlzafzytJZDqpQAQ5BahWVvu7T+CU93Jowpfnn9Mcje12m1bGuObr95NU3QACdRzO65enlR/RB6wiNJJDKFHfI+WmbW6rjHIuj7priuI0NqMyECkOX5YfO49W65QP7fPoIaecckpH83PMdvJy6+Bo3cd7c1rxs6Jc2crMly9f2v37963f79tsNrPNZmPfWo3sn/yIjUiLtHSpa/KbFx+ZbVZmvWEqt1Nc//zobGU/Oku9TPfXnb6VX/tVe/Dxt+3Zs2c2mUyuoGTlTAAAIABJREFU3/tx7gPLvToJc9DZC7haySaEUAc/cxMoQgw6mXRC8P1RXSXJwW7fFnUxo2X5ukme61Ob/vv73rL7SboPnyP0UpbljdiOj6HBw0gxwOs6hd9mbkVKpQ2yisZtX2rlqkQNyQnTZ61M6ga4KAqbTqdpol5dXdlisUiJUGxc84JS/sW/sPL5D6z31ge2LXo27BX25ePC/of/5K/Y955e2X/3rU/t08ubgv9R/z17+IVndrxa2/TOl816JzZ68UMrPvrDVL9vd9ME2bfPOTfIT0o/FtFE4ln/uQPveub60DSRc+1uOqzndWgfa017PHnr7d2TnHX3cyLnshRFkYLtXtkp0ouCpPzNYcd+09s+Y1JHjZ5BnTAfHh7euNlWEx4dHdlisaicZtSqQTUQNbKS9+/ft81mY2dnZxXLbVYdOE1K6vV61ul27einftHe/7m/aV8Yre2LnTP7lX//l+3evXv2v/2rS/sHf3rpe24gkM5maduic+3qbFbWefWRrX77f7SL8/PKh6oQpghe6t+0yy/3eVLBiqCxf586fX6EVyz6AWizm7k6GnXX97QtdVar7YRtQ553kUX3eSigJz1iwSuAqA6POvQ9lSuz/OZAyMd/OB9Dee3HUb+/Qh1mu0Ox/JER0Tyh3Jz7Eik1fl7rg0yeaT7BKUf9ft8++OAD+8M//MNboZA6yKXX9JOL3pIqdNS+AKkv/uz37cOPvmOnDx/aXxwd2dnpK/vqV79qv/DFn7B/2OnbfLvjW9829v7mR/Zv7B3b/jioamZmvaFt737Jxl/5BVv/2e/fOOLN7CY6q1OOuT7zTE4wb6wcyfOUWZbljUQmM7sRoc/tY/BCpinquDi5PuQUxeui1KhORTRFcR3APDg4sNPT01DR1JUbTTiPDPZBkryrp5v7Z/RzFVHZGkLwz0RI1+zmB7ujZ6LyctSoOPxGLqiu8LIs7eXLlzdWG25rZXLvFsX1d11JhtI2MwD6rE6a1Wplo9HITk5O7Pnz5/bpp5/a9773Pfvt3/5t++pXv2r/0de+Zt/tvG+vOid2OH9mX+9/YtvNyhbzqf3w6Gtm2p5u3zr3v2S93h/cUKwoMG8Z4KtP6fZQPqeo2wiLPquTKipT2+nhsz7rXSIf6efZSFnqezmXq44i1yN6xpe9Wq3s/Pw87Fcd5Z7TfnNPeRetkEUoLSo7l0DmNzL6+9F4Rf1hHkTB2yY5Uto7xtF28pdlaaenp2FZ/lpd2U2d8bEFs93+F/0COM92u92UNbpYLOyTTz6x+XyeNuidn5/bd77znevlted/YF966y179eqV/emPA4dXk5dW/NW/kpZyrytcWffi03BzGG3XyeQVQHQatUJGX9ZtSFPrEUKEHUHXDW7kKKgyU6H1sSPiJDlLmVNEud27n5WRoS9tAoA5tKrXVQEzCVlJ0nGO0CblIptewfgxx/ipwo4Ubp0HoO31LtDr0F6Zo9oQbfxtKdKOkRKpq8cPpJml8zDY30Ed7DXhU4TL5TIl8mw2m/R18NVqZd///vft5cuX9v3vf98mk4n95E/+pC0WC3v+b3/HigdfN7v3JbNu32yzsuLlR3b23W/bcrHIbvRTpcKEyU0wT3rmRTTBvMX39/GJ+SEOc3h4mPgxm81suVzaeDyuILblcpmULWWx56ff76cPKi8Wi5Rhy09RFDafzysTDOVNElXdyt0+1Mb48FwdvyBNbvRfaoNnKCVFsrhv1Ivbocve3FN06T8m5Vdkcggmhy58f/VznLdZRfHU+HkEGsf/uUbmNF4deViZg61RvTnGMRjeFeAaJ4LrSc/8rfBxuVzaw4cP7eOPP7b5fG7z+dzee+89m15cWO8f//c2/sov2PbOO7Z5/pHNf/DPbTGf22AwSBmEuf5GkJ97nvQZgmb+2dzYRPz0p57BI00h94cD6ZfTtTwmE8qWrQLr9bqyvZz3+/1++khS1GY1ALnJUsenOopcKV+Hl9+yLBNiZamd8cVqL5dL63SuD9YeDofpc46r1SolChKUnc1mFQUM4i3LsvLxKDNLdWy31YOVfM5P3WZAP3c1tT2X2boPtVIcTaRQdp93cvV5C6ETWin6YJS6KJRFu/hGhcJkBpM0bizg1dWVHR0dpVPEptOpvXz50u7cuWPPnz+35Xe/ndK+tQ1RGrYqMIWg0eDl/FSvZD3Pcvz1rhOTHfeNctkEqGhsMBjYZDKxzWZj/X4/tZnj91HGLIHzDLzgGze60qRjwk/Un1y/9nWZvYvENXUrII8GeA+Ds16vbTAYVFxOrqvMMaGPj49tNpsl5annkprtzjHpdDpp5zNHFAyHw0pbeF774o2P5xHv+0UC5Yv+v48H0cpVyflROhjD4fDG0mtbi3Fbl4f26GBoRJr/+YCvLmvxg0VQIcAyn56epomx2Wzs6dOn9uDBg/RhYN/PaL9JxC9v5eoQVS5yH7kkqiy8UlL3iIN/VHl1OruvwWHl9DkV/MFgkNwclgSxvPBvtbrO2J1MJhW0ktv+7/v9WVIumxkjw5jpuGigczwe38iE7ff76XMJZpZkiD1NZpYSE3u9XkJmOp7j8TitRsEfPuatctSUjR1Rzm3Vv9u6OxG1Po/DQ8pI8+eEP9d4yGtEVQS+Hn77nASsH1pc/WoGRz/Xp1ZQA3ooBCwvHwamzIODAyuKwq6urtKkU3+37nMMSmq1ci6gwlEtIwdR1TLxf86vVnTo80KwsCCpaLnWp9X7JVD12T3y0rZF0f0m8goSdyviH+2NEF6ns9ubowFqDSSqy6LKk9iO1qXGFXdAx5b/4Q1oFv6hLCIUpn1tcnH1nnfBfZkeEOgzt0459wXlkMfrko/Q5zRlBDW98iiKIgU9e71e5TyN9Xpt/X7fBoNByrxTRYF1JQ6Ce6PWZjqdpuCffvSpKK7P7mBA6ga3zv3yz/hr+jviERbLK2MNSBLwxGIy0fX7ufQB/vT7/SS8xDf0+EAVfB8g5JqO9+uQ8qFNWbpy4w0U58jmSNMRNC5ktuO1d306netDhcx2SESRHW1R9AHKUyUFqdw3KY3IzY3oNihDqTHG4X1BzcJroqK49qejk7O5Tz1Yusha6nM5Zulk1GVGs53Vw6/HIqgmZ1XFWxadULwLXIcHGkDUczu0fVHfvZaPfHEN6tIfJnuU4KZISi2d+t/+ZPKyvP5WLIE7LXuxWKSlWe7x3RoSq0ajkU2n06SQdAUF5Qxc17btq0AiF8cjF89THxfwVJdzAb/4UhpGh3JwYfUacoc8qYtYltexJLNdEhey1Ov1UnAZxYOx82itjrxCaGvg91UijXkckS/U5mxRFUD+z3XCuzsR8mijabEIfDdW6/VBVMrjPAX1X7UOnWSa8jscDm2xWCR+ILg+UBv13bsQTf2r66+/ppNL++Ldt/V6fWPpdT6f22g0qihVBFlRoSIw/5Fufnc6nbR0q5ZaDVDTpPZ982On8hlNKs+Htq6z1onCQAGiyOEbShTFSh0cYQiyQ1ZwfeATAXneVwQMoonyPbQ/jIm6W74fdajqNtT4eQRtqJklAcspApjNMmfuo8U5qOSDgZFgNDEBuKdLhAygWdUqEPkHFTFI4/HYiqJ6QhPLlO+99559+ctftm9961s30Av99isJbUn5on5tk7XxCVqe1PLhkrEbttO5PqVbLSP3cb8U3YxGoyTQ/X7fptNpZalRlawuwepE96e70fc68m6g51XOtYvKaVMf9/03Y1VBeuSGgVIFrsvgqjhV4eq80dPdvPGO+qb8jOizVhpmDYqDL8AvFgu7uLioMC434PyNYHlB0ue8pvTlRHV4JugAqXD6MiJFxMAQJCM2Qhmsp8/n8yQUnU7HHj9+bM+ePbPZbJYEBauNO/Q6a+XeddGfnLB7F0/5wcTVoGev17P5fJ6UpQq2ni+i1tXMUpxIxw5XzR/8zLMsNaoiVAPRZPX1b69sPM+i99ug1agsxlRzfTA4nGjOD/1CeWpMyczStgiVR5StHjw9Go0q6EXHMddO38cI3TbxaV+qVUU/8RM/Yffv3w9dhzbkISxlqDCqwEcQzD9fBz0JbM5ms2TxNW6hy4wIOck5JO3w7Gw2s8vLy+SKIESbzfWhwSgNAmxmlpCLHlITCXpkOf11FBtLgV4pNvG8bsLBBxVKs5snlUM+61OXL8ki9QpGDQb1wj8tuy6JSdufyxHKWVklL791fNS6+Bslqitm6tr6cUNW9Jr2VecDbi8Bd5S6b7O6SL7vTYpX432fFdUijvPzc3v16lWYHt30fxvKKSS1Qk0WSYlB00N8URJsYcZl0TMiKZ9vzuo5B6RR+/6xoqDLv+Px2LbbbUIoEeXgtlpgVaaR71rnJmqZkcICQfV6vfRNFDOrKD8UHxNjtVol4QZhFEWR9veQ+KX5C6pUQW+4ikwAv8vTGw6dpEpt5QGEtc87+iyogNgG7deyabcuyasi5Vl4jyzRfz/29L3X6934SLUi633nWl0/b0O1iuPFixepgX7t//MgLBtWzE+AfRQNA8e7ujyo1kKDdgy4P51JE3A8fPQ+rn5igfoUBfjJ7d0z/mci6uch9DnKU1LrpFZP+QZf4DUKFuFXtwbYrUuOZN/ijqBkODxJUYX69trGKJioY6lWP6cEm8jzqK2fr66dlgHq8F8y1CVobSvyqyiDFRTaont1KHOxWCTeR6tmPPtZuRy3pcbgKMLn3Y3PQ4HA4JzL4p+N/kYodRLwDLkIZrsB14GE1FfXe1ouE4QyPI+a+tlEuqQcTb7IJVFl5HNjdKJre3E/9Hk1Elo/ZYBCNLcDhcn7mkB279496/f79umnn6a+KZSPkEakFPeRuWg8fD3eICEv3nD4lTevEOGLuhpkznq0dXR0lI6z1E2VikrgrbYrx5dc36N+1z23ryLa+/MIWmHbyjRQqFrYkz8kN4KZ+ref2NzXie+PZzPbuTTqVypq8G3QJTKEiDV87RMoQZWfp0gJen56d8ZPAC/4/v0oeK1IBvTFUiD5CZqspBaTa7iA5B2gQJg4Kvxmllwc2qjt8hNR++H/3leo6yaX8lVJ+6qZsKAM5Zv2VeXPZ9CCvDQ1AKXheU1duglO3/W8i4hxVtf280InjQlgGhmmIdzzz0bv79voHHTPPRsxiAFlMuhmNIXxymRN/1VSawyp/6orM7m2+Wt1fPHCxDXe1+CkCpg+G6EzVRy+XBCDIgF9xrt0Ovn0y3eUTxtQuk+fPq0sgWOVVWFrXZ4fr0NNSEXHDkNjtlvS141pnU4nyZJP8tOx1eQueMcPK3fqhqniZAncu5lq/HJ98q6w/s5RW3TiqVFx+KW625B3c6K/o7rr3JO65xk4di36wcI9Yb2c5Viy+tRv5Svwl5eXtl6vbbFYpFR23JX1em2j0SgFR+HZ5xET0o9CwROvQPmtioFJSoBTA31YQrPdeZY+QKtGRCE8ez2oR9EXadfRUrHGTXJuV+56jqJ7ORTj+efRLYrSJxLyNzEd5og3TIoiSKLzZXm3UF1N3lVDo7G2iLSMHOKMSNvflmqfpEPdbtcODg4qnWmiz2LCeEa2qZN6NaKNotCBwKKotTHbrayo1dQUb8rSQCr+KOUiOHWCmmu/tj1335fXlCik5SLous+EMlj65ZqmidNmzV1AOWjuAs9qENbHBlDevl23tX76fFt50ed8XE37ylhjTHSPD1vgVVn7gCfyQI6Prrj4sUFBg8r88rhHaJ4wgNoe5DMirbvNsnilrrqbasFJAPOV8pzXlHovWoP2dUTXtcycNYkEzlv74XBoo9Eo+fJATk0CM7PK4SwMoO4RGY/HN+oHti4WixQD8ELRZkC8719HHlXofoaIN1qmBvZ0BYX/tQ1lWaY9KFo3SgaBROgRUnVver2eHRwcVDbD6WqOd318fkSu35Ggewut7+WQbm5skI9er5cO8NHn6a8fZ0V1tIlrLNVDLIkroaSiuJuOY84dRYmrl6AHekdzZl8FbbZHcLStNYiuN/mZOcpZ0NzAq7Bi/XE/CG7BNOA6n5HE/9YgGMzXBCAsjh5PyF6V3P6dNn0H/s7n84rS24fP1KVKSJW5xhf03AwmCJbSKyDcORValC6umv8oNwpVLTKKQs8yJb6ik0Iht2+Ph9Q5edNVKd8ffce7ld6F8GMKH0ejUVIOHuqrfCmawWDpGbiqtH2/c32jDr+yU5Zl+mh5k2tCnbdRGmYtlmOj5dGIIsWiDCDWQIfblKll5WB67vlO5/pgmvl8no6i9/4nWaa0FUHjNCv6rtaDMphwGj/w1qJt/6DbKle/CpUrx++qBSUxoRWaEwNCoSny6nR255n4QK5OJiaPR1PEiNh56wOv2gcvR02TShWXJmVxn/95VhWiLwelqZm79Eu3FlAmO2K1bpSIyrC6PfCde8ieBsE9n9XF0jZrOTlkkrsGz9sqklbncWinmwpX7RpZu+h9P8moczAYJGhHG5ossFrIq6urymCVZZnO4uCansCE5eMsDo1woxS2223y6xEMhEt3jWq7ci6eJ80kjKC6Evf0QObLy8vUbj8BeVbjDLQNQWXceA+feTAY2HQ6rdz3RkVhtcJ5ngXd0M/Ly90Hr/QD19GY8rdHlxF/mMTRMq+2kXZFikgD6ciu77MeYKy81vwUv6yrrq22DSRiVv0MgndRPAqNXDW9HqF1SGXM87WN8qhVHAodcwPlO+GVQ51r4Tul93q9nt25cydZpbrOeE3LJqT5fG4HBwcJMuteFfVDi+L65G9WRdbrtc3l8GGvDNWy6KlNeq4C5eaUhg6at6SRT5sjnSje2mj+jKKi2Wxmw+GwkhCHy8FWe55T6638pdzcqoPyWPNz1Orr5PRjTP+1D3W8UCtMuYoGld9ePvUZRWX0TwPhvIPMKV9VoWhmLGg3Wl2iPN93HVtVRioTfoNoJFMqc7d1SyK6VQIY5JWG1+QRBGyyogoJnz171ipQqBNF4acKie5gXCwW6QhA3W8A41k90fd5VgVC4S591n0eTe32ENwjLurwuSXKy81mU0FWuee0bxrMxCpyULHm7Wi/VHnipoBI2OUJhActjsfjyiZCzbqNJrqfzExUnov6FZEqAtpIn3Tlx48744esqFsKIuU3bh5L9vANt3g4HNpkMqnIIbuFMa5muwRDeIOh1Oxen1sDqeFDgWtMySuNHNK/De2lOLyge1RRB3ki2NTUcLR93Xv+f7UKaGQmB4HSwWBgJycn6SBZBB3LXRRFWoZUSOeXdDXQmGtTnaLMQU59r4lHHobXlUGb9eBcRSR+2Y6NbX4MgNaKwNTv1yVI/y3dyBJ65eQndM7ViHijqwmQukzejYzK8XXDt9VqVTnlXBUbKIS/NWjsjxsA9SrfSA8gdqZulI/VRO6Poh6952WiSZ7aKpPXQhxaWaQ8PEWCXDdpolTknNb0ZTJYmtik+Q7T6dSKokgxDLOqBufZbrebVjrUkvkDgrgWoYNc25v45HlUp4QoP3KHomh/Uew+UFWWZQoGs3S33W6TgjHbjUW327XDw8NkJSlTd7ryvj9vE8GmLs1nUP4oGoFQbEysJr56t0TjTzqJ/fu0V1daMDj8D3JQl4K+6CFJuirDfb/3S90qkMZsNqucvEYbc8apKOKvs7UxztE7bWhvxeE1GlCda7mGeu2XUwAKz+smoS9D/T91IfhsAwOO4E6nU1utVnZwcJDgIcLEb9X0Ogk14g3s1g/1KDpp235/Xfnly6qD6Uoq2AgnfSJY6TcD0l9dMaJ+LLe3rtqestztX/GxJOUff0eIUsdTyQeeVSl68spSV25yvPLKxqz6uQ0mvsY84Ctls6GN8vTzEdonDZCi1FDAmkQYxXdUAaoMeXnyvHkd18RTa8WhAR6PLjxE8hbSdy4Hr3O+mb+WIwZmuVymw3eJVYAQzCytgCwWi7SPYjAYpECqHhp7fHyczu5AAU0mE5tMJnZ1dVX5ctfJyUklqcnTvjBRFaHZDrL6yarPR2XyrI6fjyHAF32eelkiZMenjp8Gw32eC8KvsoNCjpCGKmizm4Lv90xRv8qk56NC+MhNiniuBlFT8xkDrqvSQFHqhNdyvMHVWAvXfZDfK0nf5lyfc337LGmvBLDJZJLOh9DrUASV9Z4y2t/nmajeSFlxzwuJWgq+SK/XsUBYBv1+rE8nxk/VbEtFLlouKza+fbl+5XgcXfMIJoc46vhvZsmvZmcvirIsy3SOLIcVb7e7A4mKorB79+7ZxcWFbbfbtG+HlHTcORQzmaKqbPVgZF0qVYWiFj1nKHSieCteN5FATNTVZkx0dQn0iiyo+8ryvqagE2RFoWq/abdHZqQQ8D6xpJzC8/PhL5P2clXoqNnNnAuPNHIaEOGInouUkK+niXQgdanQR/T1rARfn+b3q0WlXLVywFKvGFWAqR+hbUO0RS1QG2FRAVOLzTtkiCLEPtsTuG22UzSbzcYuLi4qqyec4q3ZqBHS8DIRuV1slmNcNPailHNrcjkJ/lnGUFFTznhhsChbXWf4pmUwvvqdFlUQPAevdO+K7kzmWeIqEdLQuIf+bqIm1N7WBTbbE3Fonr1vbA7y6T1vHXIJONzPIZAc6qBsjWJjGbAACLdaC9rB4b2DweBG3oIqjrLcnceB8iEi7rME1bpGg6JIRvsET+pckiY3TnlDP8jaxJrxzMHBQUURmlkK9LFMCFLTM1gpm++AQCgEYin0RQPRGj/geZ+z4Me6DlHRlkimmPxYdt7VAGlOBgmE6nhoZrHKHeOpxlRXpjqdjt25c8fOz8+TLGp/NYDq26R1eWPtKUKfbZQCSt+7aJ5aKY7cMhYNUqIzvtFtlEBEUV3RezpR8akReD+w+ikE0EWn06lYOj31Sc+/QPj1HApdAlRkoAHXprwOP7BYZ89LHxSLFKf+rUvGGvD0wWcf36CMKPGLiaTl0AZFY/AXlKI81wAs7+oHs/xYtxV8zweVEzUCOYXtlU5Zlsnd1bFUxWFmKdA8mUzMbIdKzCwpVep69epVOgHM79PRpfIIzfs+fZbUlr9mLRSHaji/PGZW3ajjtbKW0QQjqaOJ6hQQk5pkJr4ydnV1lTR7t9tNMHCxWKQP5YxGoxvQEqVCrINnsbr6OQWWCxGuSACjtuuynO+P52cOgej7XnH7+n2SnJlVEpt0cvHDs+remO2UBP5/tN1c0QS8UXmI3DG1vj5I+DqTRieoh/teadQhYf1dd52+qIIx2yVp6bOgL5+HoomLvr1R+5pQaB35QHcdtfqSmw8m1SmAKGClv9tSbsL5cny7UHAMxNHRUQWW44rwXvTBpcViYcvlMq0mADWJDZRlmfI/UChmZvfu3au4BjleRUqE/+/evWuXl5dJSek78Ff/zykR76JQB5N6PB4n64ayJc0fHqIYyQKFf2SqEgTtdDr2zjvv2KNHj+zs7Mx+8IMfJAVydXWVDknSL95RlhLoxuc6RFa3DVptcmVypEpDTxvXXCAlRQc518f3RZepvYtD2+GBV7T6XJs+N5HOW3Wt66j2PA6NQZjtjlPTCn1n2ADmI+N+icprRh/IiyZNG0Jrq7Xz5ahQ+vLZ0+Lb7dulfY4Gsa7uHBVFYQ8ePKgcDERb+QEqN7k+Wi980c87Eq9QXqEs1CrjqoFI+IFX1EH+Ad/hUcVFGRrf8LzQv73c1fFQUVHEb5U7X0dd+cjpYrGwq6urlJSFEWUpHlcYdMQ5rijosizTcyhi6tbdwYyLGo26VaOIcvfqZNS/l8uL8VSLODxcYS1fLYV3Q9Cm/K0Ny2nLfr9vjx49smfPnlXOB21aNvN1k7mnDLm6ukpBMQZHz4nQTWxqYXXpECvFF8l4ljKifS3e2nglGwlBWZb24Ycf3njPP+9Rjb4fkbaJPrDHhAkPT3RVQJWXQm5cGs1bePbsmU0mk3ROBTxXhKFfLPOIDKWIO6kBzAhlRitGTUo5Z5xyqI22+LHSpDj/o4iG99TlUncPJaLp+/CzqS9N93Ru6Epmjh91SD6sp66BR0dHlZt1sNtPMp0oqj3Nbm5n7na7dvfuXXv58mVFA0e+XcRUDy0hhel68pePWutSaVnutpkXRXFDyaB49GwR+mR287Bj5UvEs4gUovK/lqd15ay25w3CzrIp/fMoQKP6ep4oK0p+2zfKVtP54RETT5d61Tp72YkMjUcZii48/6KlbvXb9ZrGEiIUA8GDsixTOjjGhvpU5nR7PO0k8I7SUKUDb7SdKG/krwnd5sZcd+rWzRvI35/NZtnKGmMcdQWrJVMr4q0jwqSWTMvebDb2/PnzSj2a6wFpZl1E+p7ZTWXBII3H48oyLdfIjATpkOCkaegs4WJVWeZlwkWBL21fnSWFN5FLo//rMmYbK6sTjSVps3gjmOZ4mO2+gYIw01cNjKplzrkD6mKhTKJ3miaGWvsc7/R/VbSRIvK89e1QedIleI11aHkYHV2NQ1nruGv/FREoqm1DEe/UQJtZqJTblpmjVl+rjwrzndOMPx1A/VuXmbT8KGBVBx+9BVHLpcFKJrke16a+vmY2YmGxosvl0gaDQWWDE+WOx+O0qqLnlB4cHNw4fs8LaW4QPUIjzT1SIJFiqSNV8CzLsmyoS9CsItEG/HfOJWGcSAzTrdxmZpPJxM7OziouDEuUJIyZVXeVaiBbJ5D3tb0S8JNEjZfKgyrMaFlcXVI/LowDxJKrKl+UgNlO+aIoyLLmHYKs6o4pTxRh0F6VEW+gPUVzT99tKzNtnmt/HrrtcgKioE3dKkLUqMiyRpNCEUtOqCAgOG3UJUGz6u5GyuGawkMVSoRfk8o0DZ326uYndXWwKB66RnxS4fWJUDk+KulE8dchclDUkoKUptNpZSl2uVxWlqy13YquGI/Ly8vKSd5mZsfHx/bFL34xoT945l2IHApQhaEKx48TY+WD8FzPuXWMV8TLbrdri8XCzs/PU9o5+S5XV1dpoyRtYcXp4uIifZ+30+mkD5iTrczN3aIrAAAgAElEQVTnHXGDMVpmlpBwNI51SDXioR9/nXNNBqhJebTOHPWD5DW+PtOmLLQ8/6sw+TIjhBG1jUmuml/hIgKNUKuFBUqqK4LAahlmdmM1wk8qzxt14dQa5ixHjtf7kgoadfosRvig1hg+eF9cA8NYY6wjqwa0n8nw4MGDSlsgLVsFOuKdjpnyx/c1mhDRGHhZ0q0UkSJT+YCQD5AX48mkXy6XlTgSMqJGRvnlM0jVSKrSq9uyECkaj/hzRqWNMlHaa6+KX1Oum8z+mp9UkVujS4U8uw8sJ+ZA0pbfX8DE1zgGga7tdptOiObZsiwrB9GQil6WZRp0hfDEB3wOBrzwE9mTWjT9NkcEPetIy9dxQnHwt65i8PkIzeokV4VnNZCnh/WYWTpKkbJR4p988kllcqoialKMPqCX4ylKyrsbdXKjE1FRrdbF6pNuI0CJYFwUSaoy1Tq8cdI4iSoblrrVRY0mu5atpMvkUV8UZeV40oRqoFaHFfvBUGEkgKiHz9aVZZY/OzI6in4fouMKabXM6JQl0IPGQRhUhbnqk1IGlllXZXTlgLIiYY54QL11fW/iiVfsucmpisBsF3egX4oCVUFoWXrYM/k7BFLLskzLuxpspZ/ecOSUhwZ36/rp8zU83NdJ4Y2el0vd8r7d7o7xoz4O8lEXRw2U8o1rXqZU2UF6tKC2h7Y0jb0/ic7/7Y1IWyURUWMC2IMHD+zu3bs37qm25xMDvgE0UqPaoIA2EHwftEF9q9XKptNp8klZRlwul2k7OMcCbjYbm8/n1ul07PDwMAkMn1Qoy90+BZ7lTEnV7kwUtl37/vs2+kHUewR36/jTxpJSnrcuCDVKVBVMtOJCej33dFWF8lSxcL/f79udO3fs+PjYRqNRQiieLzr5vaD72FBEKGyCvnpdD6j2boBHP+pKqFtDW7yx9LyiH1F2KWV7w4Iy0SVuf7CzUk5u/PhGPPbXcu+1pUbE4ZeyIO9yNPnrUUO1vEjA9e82MN0HnJbLZfpSFgJ2eXl5Iw398vIyrRyoG4L/yvo9CoI0ajOruBTA9ZzCyA0Oz/tU64hn+nyuLPjlhW+73aZg3uHhYQU6LxaLtDKEImD5Ub9ODx9ZOYBn7JxGeRZFYcfHx5WDkeiD+vp1/dAJGPGvDl0onxQtaOamVww+YKsJg3pfz/XQd3IrQfAe5axtUUVelmUaB8pqa2Rfl/Ytv/HzCE+ePAnvaceUqZ4iBaCa3Q/i6xIIQwcVC6gBUIW4CJZaAS+QPlgYLQvWTfqIvAujAk4ddeVFCC+CpPym/aAkTiFXhaDP6uoU+QU68dVSw/vRaJRWVsiL8edrMO51iiPqnyefLuBjHFGwPVI0ek/lkRURlC1GiBUolAjZyroREgWqhghi/4sqU0VD2+22slu5LamCUTekbRn6btM7rRLAVJi5DiTT5aNcpX7govLatKOJGEh8Uj1Yxi8lch9SQeZ9FItPLdeVCZ9WrW1p2y/lSdt369CLX1bWeyAlFAUTQne96gTU+IRaTP2IEtdRzuzBYFkS31198KiPqiSp32eket41uYP6jMqdJv9pfbynCVm64qGGRJPacjyP2lyWZQrKs4VDeevL9OXkKJJBz+c2MtmqrrqHTk5OSi1Ml09JpFKfPKLcdc1pqOtEbeMD7er3yXBNA06q1TWXI7dMqagKXkRf8kLJ+LZrW3I80kH2qwk5nnirohCbfqjrguJQtFcU1ye9U+9isajkuSDQ9E/T7nWp0E9C3YcBX5kQUcZo5LqA/jBS/tQtfccnfvkdnvoeil5XmDx/QRzD4TC5bDyPkuTgJ59N6xGDKgMNvPp0clVG6grn3LScbNQ9m3P99G/Ga7FYZCfgXsuxCp3LskxZl0Av34i6if95+G3qwwIV9RunCDhWUZfiosw+v+RIHer7qkXU+Ar9iyLkOX5EA6krOVz3QTxtG+QtoI/yq/LjsGUdE12ShgceJdJfVhjUXYisvQ86NhE80RySHGl9jFs0YbQN3lD4/sEjNUb+R5WFJh/yvCoKdZ38vWgPVZQ5vA/RNu2Xoq46dNREeyeA8Tda1ezm8WsqQDlSoahbV861IbpvZimxZrlc2tHRkRXF7oRz3IrJZJKUCoLBQTTL5bKyQ1RRFf08Pj624XBoq9UqHfJLGZpMRJujdnrlqhCYYKy+72Gw8pzfCMpms0nftlVkgcKYz+fJrdhsrs8PZXVpNBpVrLmm3JN1yiQhnjQej+3+/fu2Xq/t4uIipeDTTt1jhKHxmZxmNz8DoWOq7ocKvZ5rAQ8iRRCRIjwNcqry1A9zqXvnXV/QnKIwbbOidXigikvHj3r8ZPey00Q+sOqVxusY7703uSGAykS9r+82dTDHkFxncv6ah3vU7ctDAEEJDLJCYcpjkmBBVCD81n+uIWTa3ijByAuS7w/t4RrwVWMNuYlEmziYR1GPQnsmNIrV+9k8q+dowEMd916vZ/fu3bP79+/bdDpNMY2yvA4KslTPQUCKpPwY+XHjOcZUjZGfVB7h5IxM9GyOyrK06XSaskCPj48T78hb0lUkXJei2J0oh/u32exO1seFA6X7XCKQSyQjfo7lEsSivuR4pX+3VSZ7uSo0WtfBc53ykC9XXhThrmu816DKDHUb/PtMIgKmuDQk4TCIHkWp1Y6QkY+RqODX9cNDb+XD1dVVhYcoA53s3oePrEpkSVH6Gt+hfF8GEwR0wfvkaLBqcnFxkZK/FBp7oVbo7NvteapUJz/ROzllpIZP+aRt8QhBcy9we7VMv1SL8SH4qZPb7wNS9IJbqGeWMFbe9dsHiXhF24baGP1WZ476yeoDUdoJ3xGgN/GQXB2+4W1gpv6NUlDB1WVAtVx68pLCQyyublDTTVt6pqimDSM0euy+74dXpBEs9xDSK0izmzuMI77l3jXb7RhWd4H/tX1mVolx+Eg/eRudzvU3Rj755JPUPp4nr8ULfh0qiPoVoUcvB9G7EQLOya0qHlUE6vKpW+1dJL/pkXd0pzD80lQB31afsdy2r9Fz2g9/37/fRlkotUo5199eEJv+BvL7hmtjvWKqu+7roRy0OAoEhi0Wi6T90ewa6VcYrPEBlBDPUq7Zbi+AWi8VLL+sGA2IH9AmZBbxJSc8dRMI0o8tKYLRIKjyRjddsdqkChCFyvMoYT1ERhW4oiE/vrl21ynLNs9pfRGqYRw1GFoURWV5nudIgMOgHBwcJMOhcoqL4pe6MVBeiWgekiIU3xd/+LPnGUrv3r17dnl5mU7Cy/FsH6Vh1tJVgelNPlf0ntlO2LxFbqpHy1coz8B5S4TLgbU7OjpKA4bP3+/3bTweJ6jIisKdO3fS1mfSyofDoQ2Hw8T09XqdDu4lJV2VlcLSHESsg5Pqauk9nWz+nTrLw3P6LEpUdwtrPIV3EGwNIg+HQzs5ObHDw0N7+fKlXVxcpMxS0MVqtaqcw8kEJV9BFQ915oxCXd8id6fJEucUiqJova7yq+Op7p/ZtSFBHnQ/lCpmjTNFZesyNUYoh77MqrtktW3aB9zIR48e2cXFhb148aJSfySLTfIEtf48gi6JtanUw+ZcgzSYqfXxtw6sX9r07/E/iABLwbIhg3FwcFBxUebzuR0dHVUy+tD2rMCAPjj9HOSC4jCzdIqYtqUteQHTPvm/2ypuT2V5nV5fFNfp4Fi1q6urtLp0fHychPj4+NhOTk7s4uLCptOpTSYT+/rXv24HBwf24Ycf2ocffmjb7dYODg7s8PDQzs7ObLvdphPNz87ObDqdVjbA+f7m0KOOcx3P6v5v844qVUWexHI6nU4lDRzlgAurJ6fRZk0sxLhgiNR4okh1jmjcoy6zNlIsfi5st9dJeLRZ+18nN21kqlZx5Ao4ODiw6XRam0eg1zS3IaojZzHUN9T7RKD9Coq6E2bV5SgGQldOFC4qFI8CntoGzQXwypE2NaVTR3xrEphIcexTB31TJaooT9234+Nje/ToUVIk5+fndnp6apeXl3ZycpKSxsx2sBqly/I3YwSvqMfvgvbt1MBvk/J4HVLZyaUEcEI5RsRs911is91RCN1u1y4vLysJXJSvByFRLykDLOOD1ubzefpYupe1CGVG8ulpOp1m++cph5Q9tQ6O6iSdTqfZZ701jNbr/TsIsUcfvjM+YUcZqb4rWXe6jMlZEwTzKItB149P605OnWQaHFWfFIUF2uAMhyaKtH9OQJqo6R2uEwj1/j6nUpldW89Xr17Z6elpOvaPvv3Jn/yJPXnyxM7OzlK+C7uRFW35w34U4kdGRGUncolfh3K8UWWmxgeDxR6dTqeTPo+Aq6UpCfDNL3ujqJGN2WxW+bwozyov4BGy2ETKs0iB5Ixyk1vXpDhapZx7VOAnctQRbUBdHR6q5RSHRwRan7bN5wdwDZ/exyC0jeoOgUiAqpqNqHsuIh854tU+lPNtvX+s95re9361KkGuaX/0b73ml6u1fq/Yc26onxheWe7Lsyb43QaW+2C52XXy3/379+3p06eVXdAoXkW0yAu/zSwpGu2vImKfAqDt0DgQ7cwZVH0v6mvdnInKo675fJ7VLI2uiq+E/8fjsc1ms0q+Q05gcwOngpzrBJF5gp7eB/aKwyszTQ9XpeL7FVkfPbFcVwx0RUDLi5a+msgLfS6OUWeF/QSPxsIrU91b4icNrpaf8PRf69D/VQFTnuePtjGCxXrts0AcfozqeAIvaOdqtbL/t70zeW4kSa6+A1wAkCCL1a2eni6NNGZzkc6666r/33SRyaQe9aqp6tq4gjvyO9Be8JePHpEJVqkvX7kZDUQiMxYPD/fnHh6Rb968eTKBuZTvStCD5HpWKMX5Q/7rebqNLWXoyiQzWGN4uFgsepnRrXpLO1s/ZpWqQwwK1u4d45O7pcuE9e///u/jzZs3JTFKvxMheJnMs5AP3mKGW0YXOP1PRemDo7pqELPFh0yoqdz0vyCyt5torSU0VBquKKUs9axewKXnBM29PkdbNSRBxcsgYEuBtPiVKaEWMs7qyeqTXPmJWiK6J5Itfnee6npmDBxl81lvm8uV35PNnTGKQ1slmNg5RBtljqox1JquRf1efnqDxiiW+/v7+Pnnn9Mt3xwsCSUDgBGPMYoMadBt8d+HEJTaoNO7tcdlE01fI01oTu5MQbq7NDTgFOKsf+KTNq0xoOyK18c0E1YXbE4qXfPvWXu9XkeYNcVR40trRaJ1D2VDBsTbX+NRDRWOQUFDtInCzej4+Lj3fYy7OMpVUSE8BLjruhRqkbK8Ay9fjPcJLZLwMv2WjM9WXahkWL4+iXK8vTWG1fqYJelk/28yoL5qQ8jv/SGRL9n1mv+sOieTScl12d/fj52dnXj37t0TS9QSrOw6x7UFwVUH4wxDfXLLXrPCm1CGlNgm9YX9a9WTKaBW3fxk+Y6Kn0Ofw/2L2DDl3H0zNqbVkVpna88xQElhc8qsArNCvXy2ozahMwvqRNdkvV73AmcsZ8iVc8p4UVvKbrVbdTHwScXh8FjXFEReLpexv78fHz58KMlcrTawPC5n+7hlED4jh/A0Ul7/GLnztrq7p+utMliXx9YcWWR18lom92NRY0Ys73MphiEaPDqQQSC3hGMa29KQmSXRd7kbsuhMhfb79bvKbMFU3uPfN9XmrNOt5KZUa6fnotTuzwQ7oi+QbsXdwk+nD9vqX7x4EScnJ3F8fPxk81vWZtY1mUzKqwOoxDN00OJDS/Fmk3kTBOSIovUc++45OkOT1AOo4k/2nOpTHS30m9HvpTBEG78Ckh0UOZz2Z8aQl083g+c5ePs0Mfyc0KF6GDeoDdLYweDEzt5p4XWPLVP94SRSfxmf8ZgN6/ZJNoQOJexKJ6di9klNXrLdfBeNMmzZF5YxxIOhiVa73lIIY8ah5hZsMkG17M3zXYbmyGTy+PrPWqLcmBgE76255p9CozNHXVFkFoHWrQbFsjp2dnZiNpuVxLIamsmCmTX42LKOmcXytvJzaJAcpnu5Q+UMtaHW3hr6cljvboNPRiqp+/uHg32yZWd/3hGo2spEsFqy3iZCXDMINbQxFFMYUi5EBlR4fO9OJqNenscDmRVcmw9DiV8uY7W+ZX3K/s/uG6OURm9yG6p0zCSrPet+PAdmMpn03gHr6GYM7PVrDt/9s4YSapaHqzvy72vP1BRajbzPtfr1G7d3ez6F/+/tIE94tAAtVg3ReBkR8UT5jBFKP4OChxeNUTgO9TMlMTRxxG8leinTmPV7ezKFyL5mitjnTO1ZL1ufjiSG5sMmynro3o2ODsyE3q3NmAntZUREL3/AGU7rRgXC7d21yTDUH1dSnByZ5velUcY23Kp7O4YsZu2ZzFVzBcg+ZatPnp3ofRd5QJVJYd5OV76ZBd4U2mcodlPySedZsjXyfnEPU1Z+C+nUZM+NCnmm2FAWz3MlngWLf08atRzr1yLiyWShFqwdQML7at9r7XBXiXsIaoNbE2ZXTE6tNvFZpvK2+tiisQqWk8itj09sR24sY0jYLy8v4+TkpCT4CfHx3pqCdGXTUlC1PnJvzBCPHC36vZkSGtMGrzcbY5bpCo9tc8qQgxS7XEV/nuPJHB8vl8/V5u6QbI41/KNiHB5IjHj6hjdOoho6qQm4nvFrTrIeOhyI7+6sEbcuj6Ea7OMEbQmSKFNcDutrz2bf+UwGu3miV1Z21hcR4fP19XW8ffv2CdLzZ2sTVIjHA+vkX2vF4ODgoGxZH3Ip+OnlMR7GndS8N1O6NZRD94/3+L10VWuT2JWdtlXoVDXvj7ev5uo4jVEUXt9nURwZJPWK/J7pdNo78NYFVAI+BpX4ROu6x6P8Ip6+lXtI6fD/mnC4UGR94ArO2Ppr99UG1pUOBZyfer6WUp8JOIWv67pePENlZc8OWbExAhfRP6fVlcqHDx+ebMevTYBMgXofZc0nk0lvK7w+uVyq51qK0eMKul/b6DUOUp6OujJejtkSobk1tEw7xKtsDrNvYxHw4HIsNbcqppLgdzYyu04oVqOWhhRT3B2qwcLJ5DG41pqg+k15DDyrNFMOtdOXfg+qCUCGRsaW4/EjlUliP2v8juijlxpUz34Xr/2YSdY9tm/+LPfbqBwFkbn/hvKc1c820G2IeEBKZ2dnqetbm8wuO+R9TXHW+E9+cp74c1nWsOoem6AXMSIBjAzKCiSE06Ryy8jO+WafFrUg9tgyamvYGWyWC8RrmQJUX4baUGN+a+J5+7werjq06mhNcAqWxs7rzfiVfdb65fWyPt+OznaJ6KqMQWjev6xcfbobE9FfjfH+Z33Rn4LOWnnxlaxN5IbjkpEbcP/Nrzt6Yn+zeh3ZtmgwNOsW1jVZRJSj9HR/bR26Zs28vkxZOIphO8ZMQrUr25REdKHTq4bW0x2+1gY7s7CboBMXkDG8VR8zWMt2UMhdQWbKI+uT6ODgIG2T/nSYUs0NcKPwHHShtqmMVqYtVy+8/y5fLYPFd8B6n33rxBhrTrenhkiGqLVBz8nnQs1wOD1rTceZ7Vlumkgtho+11i1Gj+koJ1QGjYdozKBtbW2Vo/Ra8LzWvk0myVBZpFpQeIy1Yl1UuppwnAjr9cPRgs5Pn4C3t7dPdhCz/8vlsrw5LmvfkCyMQSYtZeiK1N9D7O3tuseY23q9LjuKeS+vjXEBuM/HjeZQGY6SfFz9hWEtGpLJpqvC5JuIRwuVbZZyDdmCW4KENaJC+BTrPOb3oYk7RnHxPIbPTWNQWus5f96XtfWbw+CWYm25P6rD5YErJTV+63Aopqx7HT4hMpeC/fKEPJJPxq7rv1fHX7ZUez6i/zJzfVcgXdeHUr8lS2qTj2GW9q/fHWk5ryKid1DycxGdaHTmqAbBG8RJzlhIxijCVi49ZfW1MjB57TmMqD3jjB47WWvLs6JPHSSVsanyGNOOGl/HlkPD4ShzU/fs7du3VYPhaLEFxTOXqzU22b2apIvFoqClLBbEtnjm8Hq97p1T6vzzHKiWmzh2TDgfncaMwVgZG5XH0dKQDg/JzOw5HQzsTGMZm3Sg1mYvu1VuJohjBJ5l8k11YwbZ72m5OWOJqCITxlqZ7nuPqZN7L2SpfZJm7oOPydi0eJWb9Yf/M/CqlyZxlcyfoYUmGhY6ur29LSswbJe75nQpWJcbFaIxV1q13JUxLpjzKqOWweSLs7zOjEYpDqENaVO9H4K/eacd6opRjDwPuQhDlL1bc4hcSDJGZTCv1jbBS4eYXdeVE9B1PD6p5jaNVRSttg1Z49pzY9Cbflsul7G9vR2np6dpOVkZrSxbujn6zJRO7fQy7wOfa2UzUxYyZeRBVCqlq6urnkLRXNDzzB/xDZpC3nwr4CZ5FBllfW99J/lqyycpjlrD3M3IBowMotAy+pxN4Fbd3nENjCamBMuzXJ3oMgmWtuqJeJop6zwhSUg2ebdKraxa+8dc30Sh1hBirY6Li4snL8B26M5nPFArw+JGyU9U84mdTS5OWHeP6WK4hdez/C5y1Jat0OiF5UxYi3g8Ya3lnrRWfJ5Lm44/kSbb565VRqMUx3w+LwezEM6JpK28MmrS7e3tJ5aXVqaVEefWQf/f3t6WKLxv8W6V5Zqf7eF1lcG3ekkQx2TwjT3w5XOR52W03BOnIcVRc3mo+P19I2qLT0Ddn6Wz8xmexN5S2G6EmH+UjXGtTyxHMhvRP2iY7dDvntgo2bi7u+u9r0bPatOdzukYi/aeQxmf+BvrzuZZjZqKQy8r8sr4/lG9MzNrMAXh9vY21YhjGxqRB+UuLy+ftO/+/r5Yg4xUX+3VDs7s6XQae3t7ZX+MB8r8uZqgjunfp1ifzLKyXfots7ZDbpkEjJNZ11158MXeGQQmetCKg6y277mpKQ711ycl26vxzdybTGmQF84j3j+fz3vbHWazWUkAc9TF2Jee134rR+Q18rY8l3wsMyMzhDREzTVE+Wd6DZ40q4RCB/C0JooGUIymZpaQsVO1drSY68LFOEqNasqO5ej/u7u7uLi46Pmj2fNj4X6Lxva1RTV3oXYv622VT/heU7R6QfVf/vKXmM/nxZ1kklymsIjiuCRK4+N/2SRX2cw5qfFDhtH7pfayf0xNIKJSW/WmwIjovXjas5Cn02lRJu4ODSHYMTQGObqS1XW6KEP1NRHH4eFhnJ6e9qwGLc7d3V3vdYlsvFdO90aNdEUypsFDxNThGhHGUhFQEH0Arq+vS4ZsbRmN5Y9xDVpvJ3+uAmK9HrT2Mv2dMzVrq3vZtxpK1Ji+e/eurGbQhWCMgu4Ilb4bEyabsY01Rebfs/TvbIx97Oli6/f7+/uey637eHC1EKpe8O2uDvMx6FaNGdOxVEOdQyEBP0KhRk3FoYi5BFwDtbu7W15GfHJy0hsAtxIZ1CNEGupw1jnW4/drsOl3sm2c9NkuV/VzOp0WdyfLmPQ+s21jiQLH9798Dqrxx5GChFi8orVnG7ONfV4mDcP79++fZGLWUEpLGWk8xgSas2ezcfLfRVq+dcMwn89Lxmv2JnptU+A1JoNlc0CfvjK4CVpkO8b87oqkpvjHUFNxeKaatOP19XW8f//+iXbKtLee9caz4c7QTcnr3dnZifl8HqvVqud7TqfTWCwWcXV1VQZ2a2srDg4O4urqqlgLwtj7+/vY3t6O3d3d8jtdFr7yj+nUQyQBIyTniWZj+ltzlzIoqvKZ0iy+8LWD6pfSk3nKPF9jKEWna7rXobnaSSUypAQyefBA56ZCn7lHTh5U5aqPxpljr/KyIDg3z6l+KmOSv4R6UxozbyQPitG5Id/d3S2/1Q5JJo16r0r2fwaBMyjM969mZdes0BC5u6Fr6vjW1lbZIn93d1cmvdwNXdfEkWLQq/D0UiJNcJUrv10I5Pr6OqbTaezv7486V8FpzPEAm1LNYknId3d3i6KLeLC04qOWs9UXKQrx2xWpFAcTpe7v72M2m/WSqHigkiMYld2KgQytkDj/HHGxD/5+VpF2R/MVmeSFntnd3Y2I/gHZRAtd15XFAEe57Iu7gjTOmy7ljyG1xY33ZPIQ8N3Z2UmPNcho8CCfWtKOWz1nhO7zZ5184o8lPkNLwTeJa4DlXikhixZeKcVcd9/a2ir3alLJCtNq6xoFwifEUP9JntL8uWm9XpfAnOC3ENpyuYzd3d04Pj6O9XrdC4bP5/OiVG5ubuL6+rogkq7rZ83qlZjr9br4+IvFohenYMap+uruDmkT6F77bayBUj9lHFarVVkI2N3dLa6Fgp9aYZxMJnF+fl5kR3IjY6Tr4pcM2Xw+LzIspSUX+XO5raIslqY6zs7OBs+uIQ1ucnM/TcRJ4ZFrarKx/imfaxF/d43Pia4BUz+kzdUetk3C3HVd6TOty2KxiJOTk17yGq2RlCtjK2MH3a3kWD49h6RY6cpsbW3FarWK8/PzODg4KC9k+uWXX3ptWywWvVUCGhTFQei6LhaLuLi4KHWIT1lSVJbHQyvO78/hg8ZLdWQBeTeSdEUj+q8t0KKA5EyyoMOGb29vy+/cMJeVK5LcMKlujMswRM5TzpnnxhkjRqScZ5l8Xol+y44M3ERzuk/LjmWBpay9XFWhz66kG+7SZHxBPjuTevzsSxdwF6zZbBbb29txcXHxrH7XYhbOg7HkijwiCgpjpJ/nuF5fXxfro/rW63VcXl6mbkbE07GKeMhfkIWdz+fF4u7s7MTp6WmvbS2kxe8ei2n1t8WPIT55AFxKw+M8qod5JypPz2fxD7pllGcmizEn5DmUeQCu8N3tG4ohkZqKQ5NJAsMGsLKIfqqvtC8ttA92SwFFRC+O4HX58/6/FAVfIUmUQNcmS09nKrFO3JaLwjqkeCRYguzPUZT8TkF2f3RTYpm0qnJTiJqIJvz9quQZf1OANOLRcGjcpMQ1DpeXlwWFqE8tftT64hNANITcMtQiFOab1tQ/9k0yoUAp0akbIskrkayvXElp0oXb2dkpQUq1eZM+ZpQp9ow3m57Rp9wAACAASURBVNColHMKhzM/QyPb29slcusRd+9AC4Lys/WiYE6K6+vruLu7i93d3djb2yuWYjablRjFcrmM09PT+PDhQ0Q8ZP7t7u4W31O+JjMCdR+DZYSss9ksLi8ve2n57ENGmdaXgsqSzTZVHl63ymY5PJlLE5x+PAPcivvomhSq0vHpture2WzWO8BH/XmOJc0OAaLb5Tx33tZ4pL7qO3lDBUEDyFgX6yAaIWphf+nmirSKt1gsYrlc9hQsaZPxV72cd0N8Jy9aNBgcdV8su4f/r9frePHiRdzf38fJyUlpLK29+1mZBdJ7PThRM6a58hCzbm5uYrFYFCYsFov4+uuvi8CLoXJhVBdzKiQ4aiN5IWivDX+yQpsObAZhNWFV9ybltfhzdXUVNzc3JYJ+d3dXgqAKEksBrFar0gYFPK+vrwuSkBKOiLJLVPKie6+vr2OxWPTQKhPANukb+8PJzj5zn0yNJIt8p61W4Vg2g+xUokKYTJGXfCgGpICnVuIcxYnnVLoKvr58+TLOzs6e9NHdcFe+LZfWFWlLeYx1j0YfW0Ut6g2WsGsifPjwoVgjjwd89dVXEfG4XTljgu7XMxRg/p61S0uFTJ2dTCZxc3MT7969i/Pz87i9ve3t7oyIYlVFXJHRYGkJl31i0CxDGpk7wj/nMRVXjTctysqmi6G+arx0PUMiVJREdVngji4fff+zs7PeKeNU0pSZGvKq9VF10ZKKb04elJ3P5z1Z0vMqWzJ3d3cXq9Wq1KV2anlZCkBKQ/XTPdb/jJXJeOl5Lef/+c9/jqurqzg+Pi7ojS7QpoF38osGyflam0stGv1elWwCZPdLY4nhDrNbGYheTsTjOYnKpciepUATLtJayA05OjoqykNWReVpCVfuh65LwH0FRgpqiMljeKe6XFk8B9LX6lFuC39XDsb19XVPkXJVQEhMk4+WnqiQSkDBUE1kxpMUQ/KgosbKVzeG3D21o+aycNIqj0ebM+k+cVWD8R4qAJXJnAcpGyEXlSWe7+zslLT1LM52dHQUr169itevX8fbt2+L0iba8L61xnnsb59Cg4qDFjciz/gk8xk0o2aTpj47O+sJWlYnIdn5+Xm53lI0EnC+J1QDK6XC7D8GsiTEHuQSKtIgSmHQldEE4/PkzXMoK+NzCIBQm082vhGPyUmcNBR0tU3XXNGK5vN5bwmU99It8mfHwmWR5yawv654FKStxaJ8346UHPstHsrNkHtCxCNEJleGq3VKbeeYrNfrWK1Wsb+/H7PZLPb392MymcTf/va33lmhGfkczUh8aC3BbiJjozJHNQA1iE3me7TbBcMnWNYBZWdqWVPXaxaIiubi4iIWi0UcHh7G8fFxXF1dlSCmfHsqQVoAKTQliynQqjqkeKSQBGnlXrAPz1EctOTZUjR5/Rza2no4jV1Q/ddffy2TIyKKKyZeCYJr7InQPKdFnzrxnntePBW/tpxK91KfYxRxK5iXuYNUWk4uXwzuE0UQDahPNDheD4Oh/gLwN2/exPb2dhweHsZk8hCLuri4qL710Ps2RtaIENnXoftrNHpVJauQjXFry0bRgkloHK5SIckH1GSme0BEE/H0ODqtoBwdHfU2GwkyKpCniT6bzUp7dL/aQETByaB7u64r9WjbvfhBZet8a5Ge8/6yLFfgtYg5EYLu297ejqOjo/jhhx8KmqJy5r0RjysLclsEySXUqsOD3gwG6n/KDeXFhTRrT41/GWoY4q/yVviMGyYqh4jHrQGz2aw3/jIoMiqSLxmVjJdCLFoSv7m5iZ9//rm0R8q21s+Mhty5Gq95LUNoNRp0VTIY6Q0lsyP6y7ecBCwzy+vQbzc3N09WFjKI5XBXz1xcXBQNrzoET3XP1tZW2dmosgirmekoGKm+RTwqt67r4vDwMK6urnoTgn7sWATiSsL758hDY9GKCRAFKsD766+/FsHUhCBq0Pj4MuR0Oi0ZoYxHdF3XW50Rf2ezWVlWzAQ/89lrfjx/J2kJk/uMhkiHP9WQM2WUCs+XUIVOt7a2Yn9/Pw4ODuKXX34pCll8OD8/j7u7u+LaTKfTePHiRbx586b0QchYaJvLvpu6EeyHYli8Lj6yXP8cWvEatVeF/lFtYDNt5YNM5eKBxtYzPiHcytACyxIeHx8X2KiBuLq66vnzYupkMinIg2vzDkPlkjCI+Pr1696JUJqk9Dt92dD7SuWiieq/tQaSPCT/PMYUEXF8fBzT6bRYPS0P0kJGPD2rU9cuLi56yiYiekhN7dcqgzYMind0bcYSx9j9dFnuxWLR29peK7+mwL18yqojJBkg9Ul7Vg4ODkoOk5SMZMzHVqs6VEpZG2vodSwdHh7GyclJDw17WbW30bVo0rKEX331VaeKDg4OSgwh09S7u7s9AWIjI/ruiwTd4aKIy3QO10Xuh3IyE176QbJ81n3S9XpdchZ0XWjHz2FwS04Fy8Fxi84y6G7VYGLGByrKrEyVoUCx911Iivyn0tSkcaHWpNcfzxAhHGe8aHt7O/7whz/EarWK7e3tsonOg+M1OfQ2cJmayI7GRQaE1x0pu0LO4ktEjZmb1XX91wro9H8fSyUUulGRESKqk7JSOVRez6EXL14UxJPxlXLkxmm1WlUrHdyrQqhPcgspqFqDpLUGu1VjmfzjhIxovxVd2jw7MyLi8ZR2CZ7cFtXPieybs7qu62V1Mu1YbacQsl7yTn3g95qyzLJ2Mx5xXKgUPWeCATcGhzWh1S6PH+m7K1z59Gw3J8Nvv/1WJrOONBijNMgb3efy5UpD5DEiL9OVTk12GXhXWewrY2jZ6ofQHQ2RlDLL5aqf7w9rKQ3JX42HJycn6W/O18xgtWhUcLTrHuMOvJY1hpPOfUI+K2HjqsVQMpWWWzm5PInMg5gSbk12Jvfc3d3FYrF4YIRtoBLE1v/qG+thv7VWzwma8ccnfgZBpZBUF4WDvOWE4WQXhHa0tbe3F1tbWyWrU/yU8tBeGx0VoLp4bgdXSsQTuj2MGcktFPKjO+NUC5a6DDifHNbru6fsO599DIlgNKE1kfm8jMh0On2yZZ6KQPLDPTtCYL6EL1mNeDB62lDofMgmt8ajtpO2htKdv5vS6FdAiriqwN84manJszKo4TyBShMiUxoRT90C/XGgtTPTd8TqOW5/992L8u15dgfLVzl6RhORiqU2GGM1+/b2dvzd3/1dnJ2dxeXlZcm2ff/+fU+IidjcgolnVOSnp6dloqutOg1NiVFcbqXSke8uxUxlKsUgZeGKTPwbCha721BTIIxD8fdMbpz4O9EveajJ6DKnPybFCUlJ2V5fX/dQIg2SypWcnp+flzokQ5pfLcNC8qXu1v2Sjwy5ZTxq0aiUczZsZ2cnvv3229IxLtd55a44sgixhCp7N0sGxbnFmW1Q2Z6TEPFU6dC6XV5eloi2hEIBLwaUmCKs9lxfX8fFxUUv1yFjem2S1FwtWUPmDXDnLeugO0GlQf7TkvKcElk9TXz1Vxv9uFQunp+fnz+xiFK2tHrcUkBy5Mbr6k9LcFvwXdc8H2SoLBKNiZadI56OF906KeWzs7MyTipbSpoJZ46EVBezo5k5Wmsr++HzZJM+P4eawdFvv/2287eMZxPffXUyhOjDLSQ//R63qM50PevwUQiGgTseX8hPr4PE/Ruqn26BWyzVS6Xk/WS8peaXqk3chJUhOJJbHPKIy6wRj5NBSIF9Uj0SdLVH7iH3Z2gjl+7VZBPi8HHyzFHFOShPfKZlDakkM1elNqbZuGS/E6X5veSRkIXcObVpsVjEYrEoxouxtohHWWfQXWOtzYM1lOYoyQ10ZoT8OstoKeCLi4vnBUeZsq1PP0FKDfFB4QRzyKnfKWwR/VOW3CI5w/g/LaoGVZmfEY++eUSUgZQQaxKrr5rYhOAMrnJAqVy4UtASeoeL7Bt5yVWHltJwnrAeXqcC9liSlAb57gE9jafOViW6YdzJjUaWik+F5athNXnReJHPHkOjYXgO+Zi4gtf/WXCTGclC5W/evOkpTN1LpSMeS5bc0GbyRJ7MZrO4v78fPCjbEWjWZ/+tRYOrKpkFoEUhQsgUStZwPkvUENHf8dii2mS5vLws/jpPbVaZDORxwHSvoKrS09Vm+rUeAJTFcd54+2h5M56SJxFPD7X1MltWMytbyoAHHGlTn4RPY6HEpuvr654BUTCVCoEnwstl0bUMsekZlUm+sb3sE5eA3aiwv63JM1ahUJ417kR/XIVSvELX1fbLy8seqpLMZStQQqyOKpw/tbZm/MhoDI9035ACGZU56lC31QgxN6KfmOSWLuJpPINWPSu/5gJEPFrU9XpdkAZXJiKiN8gR0RN+962pUOTvSzi0pMg6NXEygc545NbVB4uC5X2vkdflAiuBZ5q5FCgRIqE6XSse3uMBRNbJE7LoyviqXDbOGXoVOX/JH47pENWUTsZPlS3ZdHSme6R4pRBfv35dllhppPTGN6aUE2VQMVMRuZzLMPKg6DFoa6yCefHiRfOeUdvqvWJHFw6DsgAXhYwwzzujCeQrJ2yPWy+VwVTyq6ursoV8e3u7dwgN3ZbVatV7laXqUtKOvnPvTESUCaFUc02WFlqisqghJufpkPav3ePuT9c9HHWgLEsu20Y8PeNSy++0ijyzhIqHvwuBSZnqBDW1IVsRYxupyDnm2d4SfXJ59LluCkll6yQ5tp/9JX+Vxs64BVGI0tMjHt+gp4zdruuebJz0ttAlUZ2ZS5f1pWa4av3uuq7sSq/RoKvilbuFyL4fHh7G/f3j0fJCGxpkDQKzF9l4JlVRkKh4siW5iOgdY+cQWwOosvTeFPqxDEzpf9UlhEJLQHhPvmUIQsrF07lrAiBeDO0o9vGqkdrJvkU8xrIYq6A7psnDwDOFm0qG47tarYqlFXng+1MspMp5+fJlRES8ffu2PFMzekPE5+SWSlZ1jaejCQVlq4vaC6U+S579zW+q11duiN614iKjfH9/39tz43GzWp81njs7O2X/lfdb/WzRRoiDWp4F02pMp9NyirX+aoE+KokMomYTsqawWL/QAINVvmrD8rk+T5fJN7sR+kuBuABJOdXcKpbBPlCBsE9ZklI2HvocmmhcIdE1nnYm3vDcDD6ftVPKQmOpez2l3Z+lHNEtqFHWt67ryv4ZyobzYiwSyZS2X5crwXRzX+ana6G+erYu28gjLEmSs2zRwD/H9G0yeXh1BVG436P37LRoMAHM3ZKIp34eA5uaFFwGpJUhYwn9+b02EA5juT4uxtKl0MDzjAidMyF/XZPJ++hKjbBTdcmHVXmcHLSqar/ucaXLPjpvOQ618cnGK7tHQU9HPD7BGfRTu9VfIiC1nasMRHWcWN4e1u/GgMQxqZEsb40nYycW75WcMVNWCtWXsiP6xiniccLzusZXwXsaN8qWeLdYLOL8/PyJYRXPtXKYufyZUlEbtPU/QxUaG6KRjEa/AtIFnJW4BnWNT/jqAT9XDm6ZqExYRs3KKjg6mUxib2+vDL62d6/X6xL7EPzb3d2N5XIZ0+nDIbti7OHhYfGdtVdH5zFoAOS76nWTahP7o/99oFpIo8b3oSg76xeJB8pA1Tt16cJFPB7Ow5UWLjErPV88Fu98pUp8UjsVB/IxdgSWLQvT6LDfhOyuXJ6DNMg7TUzmBok3qlfp5EIedGXkUsiocJL68jPr8nmlsXDFqP5ubW0V5TKkNNT2iMcsVfKV/e+6rhe8zWgU4tjb2+udhsVJzU/XimKUtLVDST0r7e7POmwlQ1xB6TuDnx54Un1EETrLwcuYTCa9HA5BfEcLQhHz+by3TMl+qk7ylP87YmO/MmXNCef8oXIRSYiV2agTzSOiZL7qlG214/7+vpxCJaHWC5vEYykdKRG+DlHlMoVa8uDohGPorov3Sf+Lr2NWUYaIfN7f34/pdBqr1aocc8mT2pVlHBGFj3d3d+Vkch6WfXl52VOkQsia6Io1rNfrolx1ZOB6/fASrOVyWVal3OW/vr6OFy9eVF+lUCN3F59Dow8r5jW3lBJ+KgduaMosQqYhfTJwUtWUhpOCo5x8clH0vCa7hIGR862trWItHTVp4EWCi84z8mNMAJCunZfj/K9Bd046Fwi9N+bs7Ky4aNpfoZ3C7O/29nZ899138eOPP/b2rOjlVEIfs9ksFotFzOfzWK1W8eHDh2JRqaCzHAXyMuuPw3M3OjU+OGUKNiP1+/DwMNbrde9tfNfX10Wu2AbGHzI0zHbzdxoRX8ZnIDXiAR28fPkydnZ24s2bN70zM7g8PhTM3ITGKJRmyvnXX3/d+e8+YTPLL6vglpRM9MmtsjNIr2fIfD6jspjhyXvYB66CUCgd+Xh/mGPi0Fo8YJyD/aFQZHyLeLpHwxXHZPIQxKy9AiBDdM6jiCgb2tQeKlGiAd0b8YDc9vf34+zsrKA37cGQS6MXUtFwqGx/1QT5yj/d4/3hdR+zsZZ26D7x9+joKE5OTno8lsyoHL5ThdfEO6EIKnIqVCkKxTqoJLlzlvkc+/v7sbe3F+/fv+/l3ei8jWxLfzZ3s37X/j87O6tqj1GuCtO3CYPpqlBRqNE+2HzGr/skd0Fyy5EpjsmkvzzqKEiwmz6o2sSIt7+ASPWoLV6HysgUlbfVFSj5ksFxIizFUYgSaujLeaWlUgqmeOIKWUrYtxeIN5wQUiQKppEnVKq1tnGMWb+jEyZS8ZmxNAZ53N/fx+npadzd3cXe3l55lQYViXigdqrNdPP0J2WstwSqDJ6az1UstpWoQwhIrox24DKO4jQWjWWfY2hUyjmPfFNHsslBf3UyeRq44qdbX33WFI6IwTKVo8QjMVhBu8PDw+i6ruxsFWSXQDO3RO1VIFRBUN2j9Xi9WFoWNeLxXR0R0QuisW9DxL6TP+zr6enpEyXk/BNlvJMyIDR2P1t5FwqEKhfh7OysBELlcxOpqH756AxG+7F1tf77/27lM35tQjXXWySltL+/Hy9evOidaSK5U+an2sRVEM0VZokqAL21tRVnZ2e9V0ZERE/B8FoWFNYYzOfzWC6XsVqtiqLjvNlUafAAobHUVBx890Nt+UyarybELaF2Acl+8/skpPSjqQC4zEUrwcxOTkxl5EkQ7+/vS0IT92DwdX57e3uF4cvlMpbLZZycnKSHwrbI7xtaYXLrnSmPjGcipc7z3NXr6+v4+uuv4+joKP77v/871uuH3Z1/+tOf4u7urvjVEixmQGpPz3K5jPX64R04evGVJ0ZRfmr+uAt/jRfPURoZ8iEfIx5R33K5jK7r4t27d6UeTWwZoYjoydTl5WXPZe66x1W3y8vL2N/fL0pXgVC+cfDy8rIc8tPKZZEiV4C1hmB9nmb9VVuXy2VMJpPyytYx1FQcmVtBQW5BZLcufh81LH9nQE3W0WManPjuHjGyr2u+yiJlx2i2lIQgsQRC3yUk7Md0+vAWrvl8Xg4BrgnnEBrQdSaftcpRGe6OtZSWhF5oQn08Pz+Pb7/9tocWpQyOj49LLoEUiDJuxUct8+p9IKpLq1Jcmm3B6pYi5b2bKg0+O6TU7+/v4+PHj0WOpBDkouk8E+eD3AfGc7jVXoZISlXBYR2SzfwjujEkyjTR/Rji2FJ2hKb5nuUxNCrlXJWwwewEVwU02EzdZqf1DJGKC4Oen8/nRWsPKSJOWllU3i/oyPiEKx/uMVBZ3PrPfRj67ddffy3PkAdqY81KZgJcQxm1sfHcmdp9qk8vmtZ3PXd8fBzff/99afdqtYq//vWvcXh4mCYYEX2tVqv47bffYm9v78nOVdXBPS7sO6G5t9Xv4zW6dJuSj4HLrYyJ+q1Jzns9/hTx1IDRYDHnhOkCROo0TG6Aav0d6n9rzqiu9fohBZ4xljHUXFX55ptvOkbaXQmok4w6c70+c2uIHhgzIQk5aJOUJonudTeGdTJ4xwAgA6O6zp2fKof9YLs8MUnlavDlHgmiU2nQzy+MT+AjeSXe1u7lM60xpHJUmYrWs4zJ5CH4ypPPuKRMfjvqYj0194kTgpmqXLbNiMpUY6gEpk0UR+1erVjwHTtUymo72zu2HrfsGYLSfa40eDjSGMOQ9Y/PMAlT31WPlJmXcXx8/LxVFZ9M3lgP8oh8tUGf2ZIjPznhsgh7NmHoS6u95+fnPSip4N7Ozk45sFexDJXHyURoqXpfvHgRs9ksTk9PeysIgvR6d0VtkNn2LPCp8iS0mdtDIdEk0jJcC8GoLD8GT9ek/Bn1V7voMrkrRSHkWFPpuVLgWLsh8X661eUhTByvFvnEytyhs7OzFMFSprjqJmND5csNjOKtcn8cmahcbppkgNmPnxhyr8YoUJcRyYXvFZO7rjhOjUbhE54NwEoZDPKOckdnBr8ctajhEjhNQt5LRUJtToUjv5pWSb6pVl98D0tElNUAafrlcllcm+l0Gn/84x/j6OgoPnz4EN9//33c3t7GcrmMf/qnf4rVahX/+Z//2dsUVxtsTj5XkhGPlqClNKTI5vN5/O1vf2uOHVELx4qJXVJEWjna29sr79E5PT3tZYuqfVJYOzs7ZaXAz25VnSo3UxCOHmu/Z7IzlsZAet5LPknWJctSvsyF0b2ULcmAku0kp1RGmkOebu7K5rnEeZKhfBoVtaeF/kijYhy0hN4oF3wx2AWdW6u9UxF5Tkd2jaRy+RuTa2hxuZ/Cl0rVDz2zt7cXr169itPT0wJhf/nll+IPis7OznqRaFkY8oDw1pGE158JMO8Tn2ezWbx8+bKqOLLJ2HVdSYGWkry/v++5gjqTJCLi6OgoIqKcZLVcLkvKugKHKpeIRfKg5W9m7HpfhmC/eJP9P+ZZf84nYwsZ3t7exmq1iq7rSsp51z3uxJ1Op7G3t1fu1dkaPI5Sx0pow5r45C703d1dXF5eluVuKR8ff5L3Y8hV4XOZG0yFwjyhGjUVhwRdgiI/fmdnJw4PD+P9+/dFYLSsozMYWEaWtKNnvJPZ5GlpQIf7tIpUfNor4Egp4vHFTbr3+vo6fvzxx147Tk5O4uzsrBfYur6+jv/4j//ouR/ux2uQuFoU0T+Yxt0w1uv/y+rd3t7G69evnyzhirf8TiERFCYf5AZoReDq6io+fvwYBwcHpX2r1aqHAkXap8EzW9VGbc2m0nCl6n3zfmdU+33ouSxtoFUO+eUxgoh4EoPwrORMft0FV7toAP33sXypkRsm/q+44OXl5RO5adGoPA4vUJpRwieBUH5/RP+AY3cn1OCsgR4sy+Crf2e5hIUeqHXEFNEXDqIjCokEgWUQdtI6uJLUM1JOgvKZomgR75tOH16epANlKIzeN7VXaIhLfV33sHyovnOc3759G+/fvy/XuDIixaN71+t1WQHTNVnnTPg/Bwx3GlMe73El4shmMnk8CJiyypcsaVxpALlaJ4PrMqNrdFv8TBTKrupp9bOFnlqu83q9LmhICYbkTY1GxTi4hDeZPCSunJ+f9wJG6qwmnmtadw/YcLpBY4WKCoMbhXS6kjZxKe4gX5MujMpReyUUgqDMpJzP52WVR/BeG71ubm4KrK2RBs4Va9Yn8oG8oZJTCnIWRJNAUkkwP4UulAu2ysn8XVpSIiXxjwpXSNP75K4t273p5G9daxEDkVSWRK/qj8ul74eiEhDSlCERymV//ZBmKWSdk+K83d7ejm+++aa8nIvB8CH3JEMY/vv29nZBG24sWzT66EB1Wj4rrQkrEfP8GsvzZbWIpyeDUXOTxDAiGjLw9va2l9QVEWWidV1Xgp4RUbIcZ7NZ7O3tlc1af/7zn2M6ncZPP/1U3nqvRK+PHz8Wy7q/vx8vX76M4+Pj3nZrd8moLNy6iZgfw4ntiGoymZQMUKENL0/KjK+AvLm56Z0bojNV5/N52YukvTnz+by8BkEuCt9WJp9cY8nJQEU7nU7LVvzM7SK1JsEY92ITIk8pZ5Q/vk1egXQFPzVGChprTiiGcXV11TujQ4ZN/0sB0dDW+HN7exsfP34sp+6PWYp2hVFDG13XlZdvOWodotFHB0ohcLux+6qiLADIZ7QV+7fffkvrG4JKrXiAAqFyWXh6lSaxhIXLanSRjo+PY7lc9q4fHx+XDElZTb3CT5OIPPHB9bZyxUnC0+of+xjRdx18DGiViAp1pKPOkViv13FwcBD/8A//EP/+7/9e+CGl8t1338Xr169LmrqCq36uq/rPw3hfvHhReCuF4gpwSCF8bndGZaoP3LfjdXEzoZRg13Ul4Ml08fPz83IINAOc2tim5MPJZNILInOLhJSNnmUaw2q16p3IRUTCa5m71VIaLIsoawzqH6U4NPBcd+bAS3O7IEhDOlw9PT2N09PTXgcJG1sR8xqcJ5rRH5XV/v5+gex6llBUCmK9XsdPP/1UEsk0wT5+/NhTmhGPJ2n5EixdBO8LoTqX5AiJNYDcTJYpSR9cV9aZe6gNgPpNKegKkKndUgYaH2VT0nqqbH2Xv69+qw+ZNXV3sdYHXvscJP4zduFIj/eIZ8xG7rquIJCIR6Oo+4TqOM5ersf+aEz4WeNB7TuRVEY+FhwH50OTj60bvvvuu06+v4RY2lZJIg7D2SCuR3tH6W97Y4cskCuOTLO6n6g6XcOSYVlg0xUbFZS7V5ywtMi8jwqGK0vsU7ZS4m1xqikWXwrWqhjHgP61SG6bSHkwjJmQl1KCnofC12+qndlLlXxsN6FNkUkLDfKa2sl0cO5+5gqZxlJxNcmTlARPkCPioCvJsRjKGq2h2jFIw/8c+cpYPfs8DlXOXZ+C/z7J3LKKacvlMl69ehU//fRTLzLM+zPXptWe7BoHQ0RFQRgmYeYE0vNac9fpSmqnYLjiC5yAWjFhH9z9UVsdrmf9qSnZzIr4MnDGP9bvMFjLyrxXkyJ7Y733wwXb+eBxHbdy7PfvRep/Dd2I13rzXUQ/P4huprsiLMvfZkeEqbpUpupgRq5TFvcj3zw3wylD6DSsfHZoPEa/iVLQtwAAEHZJREFUAjKzCBnjOUkFyxSEpF/Ozwy+upWq/UZar9elLp1adXd3V/zVyeThnS+qS+dELpfLoml1fNzd3V0cHx8X+K0EMipM9c8nJPnjS9GOTJynus6YQA12utLMiMuxOktE58fKp46IoiglyPL/hZg4ORQsnEwmvaSxiMcDffi6SR58OzSGpBoK8etjyqqVX1Pecne1IhcRhV9yS8R3xUC03C4eMi1ASkO7YbnrW0hDCD5DExH9JC1HF64Ms776ahbLpCvF6zUavSXOrQshqisT3ad8g59++imFplQIzogMxmbMcAXjyTecuJoIfooVYx+KirPeruvKdnFaa575qNcu+MAJunNg/b7MBVMdQ9BT/eWyXw3ZrFarIrxqk7bMU/hvb2/LQcU83p+p51JuUqwMRKvtei5rm1y9Mft7fMx9/D8XeVl6yx9lTDEtBd/FLyl65hFJjqREJ5PH9HuOj4LZlNkszkejlRngMUjDn2G5QkOK07Ros720kUdv9VlrmE8S7zhjIUOUZV6SZC2Vd8HlQW7Pl1WgRYl4sLxv377tHcnGQJlDzVp+ikgTland2gOTwXaiDN/v40qVguZtyJQRt8MLQvOaVlvu7+9LoHR/f7/wWwccCWlEPC7zMvtWwVVOJidu5Pq93JQhtDOZPKw4yfJfXl7G5eVlSZASetaqjPJ9uA1B4yF+dF1XeEQXXXIpJaHAM9P5VR4/2f6WYnUDL9nTb47i+SyD4zUavRybWfcM9rhfzw6ywRLAbOVhrI/nFlr3RDxo+cViURKzfOVCz3MnKF0IKhlXboRzWbIb+SP3iYPCmEoGw115uLIdsrrshxKWFOxk8lPE46nw5I2Um9rrOTNSLvpdMRK2g766Pj2+8hz3YiwadcrGx79rdUkIgitImSwQtXJiuuy7zPMwH2aScmIrE9eNypCb2+KJG5/MFWy5vb2yWox/9epV12qIW0xtWdfGL0Zo6UK0lltbtFwuiyXzNuk7E5L4Rnme4BTxqDRo2bky4ALOzMCaELpV8BWnTPmy7VmZmeJg0NfrZ/mKa8zn8zg5OYmtra1yWrm7iCK3hLqP/SfP2Qf20xWRBFLuzafSpyidTNnOZrOYzWZPXm4UEb0DkLh6qP7QXdQeHfFY9+lsV75PVrLWdf0DsohK2HZH5rU4BOedzzemuXvZrHsymTz/PA5SNlAOdS4uLkoCzM3NTcxmswL7qKVdi3r5jnT4f7ZUqWc0ALSunmOwt7dXfE8NlLdTVprKREKxtfV4BobckIh4kq9C/97b2OKpIwsm5NQgdjYR9F1H3akMJqupfCkF+treXq02SXFL0H15Wc/pxDSe7u3ykvXD218jIgCO1ZhnvA5dY3oBUYBcOcmAFCED45IbnVeqcaQsXV5e9njWdY8rfBxnV25sJ42D6vvmm29iOp3G999/X9wotS9bscyUhpA33a4WjT5ztEUcRK5za0Kq0R4g1HXmPfB3r18H9NTqV5u77ulrBzWBZrNZeTsWtz3rXp5sLabrrIqrq6veEp3KpXKgYHoU3NvqlKGAmqLJUA/5qPRoBu4cmah//F9lSJnoee389InKJUfeJ3SnPtH1+5yksseQB2jFi4joHcAT8bjL2t0VBrsZTGTfdDCQnvcjByL6yIVIorb5k/3VOM1ms/iXf/mX+Ld/+7f44Ycf4qeffiqBbc41ukqK3XFuUYFlRiijpqvy7bffdmRuqyNiSqYceJ9eJ8mEowyGjRUw5fBrdYDPZoI6nU7jL3/5S0REfP/99+kE5A5ICRvRR0R+6lVWlsrTRGq5J+568FqG1By2Kl4hBbm1tVVeklSzthl8l+LgCWnkgVKlfUmR1o1WmoqGyYQtXoyhT1FCNZmmPHuGZ0Q8QRmSe05Mvj6Dytl5KFnyxMosG5n9Vf0HBwdl3F+9ehX/8z//Ex8+fEgVpJ5nnIptz5Tps12VzJKyAjHA0UQWhJTP5Tttaxappvk4CTQpaQ2HNOZ6vY6//vWvT2Czo4jJ5CFNXQe3OMTc2dmJg4OD+PjxYzNpp+u61AUYo9UJW8lfBmSZVCc+SnnUXhycKRFvn1L02Te6NbyWtTuTHcaUeP33pBbfqThl/SP67dSEd6Wt56kYXCa5eEA0SnSrVazsMB3K6N3dXXz8+JGTPL755ps4Pj5OPYWWQVY7x8hkeaY1eN99911BHC0Lyf/JLBGhrAd8sg658pDFcsuYKYkhN0D1PmEErrWskX5n0LG2rOjty9o7hObYdlfak8nkSaBRaIBKZ6jMLM+CCpoTJLOAVBIutESBaheR16fQcxTPGMXRKpcIhMrTkRsNKMukoaXLE/GoRKSgaydx+bjq+9HRUaxWqyfLw/4M5Ygy4PRZgqM1KN76robJfyLcpr/n97M8ohdXImOFL3NdakrEUUumcLruIRmK7+scUkat75kr4ff5s740rTLkEmWC4O4O4XdLgXsdfo331ZaaKQdjkGGL/q+QSsYDH5dsGTlzkYmsanXQfXHXobbyxOc5/tPpNM7OztI265q3kYqrJX8ZNfNKCYtrAkPBIazOPt1qDtXNumTl//mf/7mkjQ89l/1GP85RBndLulVxcvdhiLKysoj3Jv3xtX+WxXyaGs9byGoIuXE8d3Z2ekHj7F7xtLaEOJZakHuIxiqqmoLPZIZjqP6N6SPLjIh0HtUok7sMhdMF8bYO1THUh0HEwV2gbn1dM6tBrDxbkhua3KyHKxLX19fxww8/9LRxZvEzOF27l+S/fw447W1SHdvb2/H111/HfD6P//3f/232SUoti6V4folD5p2dndjf34+tra0Sj6lRpmSGJiiRxJBhcCv5XGq1yeXruWPYQouZKzJGpj+nPLFOl62u69LYU0T7rBu2c0hxNH/tusej+LIBdyuXTUwKu2s/RpxbTCXTLy4u4ubmpqwgDJFr3SGh1iTV3g21tTYQm5LawO30OtTX71G7NDlrk8Ktia5tbW3F0dFRL4N2qG1ZO7zsiEejEPG4Qc5T5FWGNna5q7opZT462/ocyP0cqvGkRl999VU5+Pn/sk38n65IRtncYfuGlrgHZ95Y+OSkhnMTlMM4VyAt8nXpw8PDshw19OxY9yXicUIwFVv7XjaxZI7G/P67u7v48OFDvH37tuzOJVF5teqoIS6hDe29UT7NWF6zbAXrvG81N8sneNd1o41ErX9eNstvoYPPSZThmjuQ0Xq9jj/84Q+fbHwyhC9S1jZ5rjaTqPSy9o4do402uUmDtQbKXYNM8Mn0LBBV++Q9OkEsE6waQ3h/rQ+y7opoZ0tyXk4WiKK7QAhPF6zrHoKsHz58SMvf29sr93hdHmOpKXVF2JnMNplMSn6H9yNz7bITtrM9Kllfu657siw81gDVeMvPVrl+z6egHT2zCdIQrVar+Md//MfyWoux/edqC0MFzB9h+7JwQmsuZNc9laJGzeXYP/3pT+VHwkxOzmyiqnPsqK7zLxMM/53XHb57p2sd9rToMQNHS3lwcNA7YdrrrD1bi5m04i5jyuc48FoW/1gsFsWlUPt1mLGOCvDyJHB+WNMYvk2n07JRrAaTN0GunxobmEwmcXBwUE7l5ysvxzxLYtJWq71ZOVoF28SVcsXBrFPKWGbMVS+VB1EIn+Ozr169ipubm3j//n18+PDhecux7qbQmrBxWYPZSGlHZpbW4FKmWKhdM4vjg+GKxzcUbSK46/W6vIippSy9D1kb+VuNXDmOsRjZuOj65eVlL+1fCGZ/fz/m83nvt6F6MgTo1HXdEwXrv29Cn+p2CPGoXm7n/z3qVxs2Sbknj5lhW2sbA5467NjfKUx5cref7VqtVoNncUSMWI7lRNXk28RflWajwuAkdB9R9bhiqaENle/W0iftEOLgNfdF2a4xfXZypeGoIuvbmHoy3vFPmZ88c0Nl6lAe7k1xZJj1YUzfHTLz+u9NXdeVwHBLeWdK0mX/uUoks/pD90fEE2PrMkFZ0Tj+8Y9/jH/9139NXY4sgO7z8PT0NF6/fj3YxqbioFCxkqwBLdKgZQqhJmSuADJryrbUBoa/1VLDfUIvl8uedR3q5xjfORukTQSxdX9NGCL6/rH/n1mebJzGKDBvh3//v1YaGiu++8R/r1GmdGu/D5HnszCXpqVQM4MoV5nypb0wGSKdTCbx7t27+K//+q8nOVRZf7K6x7qGG7kqhDTsUA1WD/nrY6+JfOcgO08IxrZI8WWvGajVrfePOFKpQdyxCkDZhLx/bDBK2/draciZsBM5MdVZApnVqz5vghYcKdXa83+pPFQ+X8cwtr6aHLqib5H4KaXLCduSPVcYjFdwH4sypnmejOrTeCpg/eOPP6b1jR2DMf0d3FZf047UoPyt1pAxNPS8C7wHhSj0GeQfM/iZBla9GarhYPBIQO9XZk1UJ/vEsxRYBxXGWNgr9CBeMS9FJ7n7/ZPJw+lTPLy4Bu1byuW5sP65xHFiuncLObYsMb/XlGlmtXXkYkuWs+eImKj8spRw3TuZPO7g1eFBnn/B+rhL2ecH2+BGJ6NRp5xvoiQ2obFlZK6AJoD+pzJziNx1XUEdLjREKq2gWcZoXue5DTXKBLDr+udJZNF+51Mtil6rM6IfpD48PIxXr17Fjz/+mAoyg5u+J8bLrkHmjDyG4ryoWeRN+qn/fY9T7V5eG+uODNXtfaqlemeI/eXLl3F+ft5b8Vqv1+W9KxFPVwnX64c3Dfrk97a4MdKn3iY3xCPSqDwOVyDsdEZsFJehfDNQ7blMUdS0fa2MTOurDWyfrFTWr6w90sbuu0bUs+0y16Cm5IbIrc4QyQLLL76+vo6Li4u4vr6Oo6Oj2NnZieVyWbYFnJ6ePnkHrqMqujwRT9PetU3B8wyydkU8fRG3o76xpHa2FDjH1E9Xaym/rC01JMbfM2WU9U9t/+23355cb7lNGV+9PZ6lrPchS+7JL3ezatRUHP6KO1//ZUXeEUItwi3+llmCGnzc2uq/TpD1jrV4LC/i6fmLrhw5uSnoum+9Xhf3pJZO7VDU26K+iFfkz1D7WxPTiW+Uu729jZ9//jkWi0Xc398XiJsJqdpTm5Rd15XsYN3vcRz/v6aQxYds4vu4tPg0pFQ9n2IIbfh92W8ZtcobajvrbOWO1HjL8niGiN4LrFPXtbpGWR86hSxihKvCk65oachsRxh6NrMkLeKEdDjv9Tpcp7AMIRoKeHa/l6O9FuqHjhJUGV5u1idvI8ldgSEl4C9Gqj0nAfVt8xKg7ESyllKX8nTEkVnOrE+cAH6PK+kWalD/eXasl1Pjdc1yt2hofDdFRln9bsC8vrFtzYivl4yIojj8/cDKcVEwtkWjzhzl8hy1EyGpuwBsDIVCk54M4YSUULsAZL5bJrBDlFnUFmKSRd3d3Y2Dg4M4Pj7uxQB2d3fL+aZZuz6FMkujk8f29vbi7OyspKtnwkbYqbYNZT6OUVrMCdF4Ei0wyKwyW+2jfEnRtcaTJ75l92V9yCz5mIk5ZsJu0obWPS7zmyi3rEyXC77rRUdtTiYPmxAXi0Wcn5+PTolvppx/oS/0hb5QRp92qsoX+kJf6P9L+qI4vtAX+kIb0xfF8YW+0BfamL4oji/0hb7QxvRFcXyhL/SFNqYviuMLfaEvtDH9PyDjtEF/WQ6jAAAAAElFTkSuQmCC\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "tracked_labels[100].plot(scale=0.25)"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "tracked_labels.save(\"retracked.slp\")"
-      ],
+      "execution_count": 10,
       "metadata": {
         "id": "D3YMi3C0C0uh"
       },
-      "execution_count": 8,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "tracked_labels.save(\"retracked.slp\")"
+      ]
     }
-  ]
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "SLEAP - Post-inference tracking.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
diff --git a/docs/notebooks/Training_and_inference_on_an_example_dataset.ipynb b/docs/notebooks/Training_and_inference_on_an_example_dataset.ipynb
index a397089d5..b5d2fa78d 100644
--- a/docs/notebooks/Training_and_inference_on_an_example_dataset.ipynb
+++ b/docs/notebooks/Training_and_inference_on_an_example_dataset.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -49,10 +49,20 @@
     "id": "DUfnkxMtLcK3",
     "outputId": "a6340ef1-eaac-42ef-f8d4-bcc499feb57b"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mERROR: Cannot uninstall opencv-python 4.6.0, RECORD file not found. Hint: The package was installed by conda.\u001b[0m\u001b[31m\n",
+      "\u001b[0m\u001b[31mERROR: Cannot uninstall shiboken2 5.15.6, RECORD file not found. You might be able to recover from this via: 'pip install --force-reinstall --no-deps shiboken2==5.15.6'.\u001b[0m\u001b[31m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
    "source": [
-    "!pip uninstall -y opencv-python opencv-contrib-python\n",
-    "!pip install sleap"
+    "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n",
+    "!pip install -qqq sleap[pypi]"
    ]
   },
   {
@@ -67,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -75,7 +85,53 @@
     "id": "fm3cU1Bc0tWc",
     "outputId": "c0ac5677-e3c5-477c-a2f7-44d619208b22"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\n",
+      "E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?\n",
+      "--2023-09-01 13:30:33--  https://github.com/talmolab/sleap-datasets/releases/download/dm-courtship-v1/drosophila-melanogaster-courtship.zip\n",
+      "Resolving github.com (github.com)... 192.30.255.113\n",
+      "Connecting to github.com (github.com)|192.30.255.113|:443... connected.\n",
+      "HTTP request sent, awaiting response... 302 Found\n",
+      "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/263375180/16df8d00-94f1-11ea-98d1-6c03a2f89e1c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230901%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230901T203033Z&X-Amz-Expires=300&X-Amz-Signature=b9b0638744af3144affdc46668c749128bd6c4f23ca2a1313821c7bbcd54ccdd&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=263375180&response-content-disposition=attachment%3B%20filename%3Ddrosophila-melanogaster-courtship.zip&response-content-type=application%2Foctet-stream [following]\n",
+      "--2023-09-01 13:30:33--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/263375180/16df8d00-94f1-11ea-98d1-6c03a2f89e1c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230901%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230901T203033Z&X-Amz-Expires=300&X-Amz-Signature=b9b0638744af3144affdc46668c749128bd6c4f23ca2a1313821c7bbcd54ccdd&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=263375180&response-content-disposition=attachment%3B%20filename%3Ddrosophila-melanogaster-courtship.zip&response-content-type=application%2Foctet-stream\n",
+      "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
+      "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 111973079 (107M) [application/octet-stream]\n",
+      "Saving to: ‘dataset.zip’\n",
+      "\n",
+      "dataset.zip         100%[===================>] 106.79M  63.0MB/s    in 1.7s    \n",
+      "\n",
+      "2023-09-01 13:30:35 (63.0 MB/s) - ‘dataset.zip’ saved [111973079/111973079]\n",
+      "\n",
+      "Archive:  dataset.zip\n",
+      "   creating: dataset/drosophila-melanogaster-courtship/\n",
+      "  inflating: dataset/drosophila-melanogaster-courtship/.DS_Store  \n",
+      "   creating: dataset/__MACOSX/\n",
+      "   creating: dataset/__MACOSX/drosophila-melanogaster-courtship/\n",
+      "  inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._.DS_Store  \n",
+      "  inflating: dataset/drosophila-melanogaster-courtship/20190128_113421.mp4  \n",
+      "  inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._20190128_113421.mp4  \n",
+      "  inflating: dataset/drosophila-melanogaster-courtship/courtship_labels.slp  \n",
+      "  inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._courtship_labels.slp  \n",
+      "  inflating: dataset/drosophila-melanogaster-courtship/example.jpg  \n",
+      "  inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._example.jpg  \n",
+      "\u001b[01;34mdataset\u001b[00m\n",
+      "├── \u001b[01;34mdrosophila-melanogaster-courtship\u001b[00m\n",
+      "│   ├── \u001b[01;32m20190128_113421.mp4\u001b[00m\n",
+      "│   ├── \u001b[01;32mcourtship_labels.slp\u001b[00m\n",
+      "│   └── \u001b[01;35mexample.jpg\u001b[00m\n",
+      "└── \u001b[01;34m__MACOSX\u001b[00m\n",
+      "    └── \u001b[01;34mdrosophila-melanogaster-courtship\u001b[00m\n",
+      "\n",
+      "3 directories, 3 files\n"
+     ]
+    }
+   ],
    "source": [
     "!apt-get install tree\n",
     "!wget -O dataset.zip https://github.com/talmolab/sleap-datasets/releases/download/dm-courtship-v1/drosophila-melanogaster-courtship.zip\n",
@@ -105,11 +161,382 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {
     "id": "QKf6qzMqNBUi"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:sleap.nn.training:Versions:\n",
+      "SLEAP: 1.3.2\n",
+      "TensorFlow: 2.7.0\n",
+      "Numpy: 1.21.5\n",
+      "Python: 3.7.12\n",
+      "OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n",
+      "INFO:sleap.nn.training:Training labels file: dataset/drosophila-melanogaster-courtship/courtship_labels.slp\n",
+      "INFO:sleap.nn.training:Training profile: /home/talmolab/sleap-estimates-animal-poses/pull-requests/sleap/sleap/training_profiles/baseline.centroid.json\n",
+      "INFO:sleap.nn.training:\n",
+      "INFO:sleap.nn.training:Arguments:\n",
+      "INFO:sleap.nn.training:{\n",
+      "    \"training_job_path\": \"baseline.centroid.json\",\n",
+      "    \"labels_path\": \"dataset/drosophila-melanogaster-courtship/courtship_labels.slp\",\n",
+      "    \"video_paths\": [\n",
+      "        \"dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\"\n",
+      "    ],\n",
+      "    \"val_labels\": null,\n",
+      "    \"test_labels\": null,\n",
+      "    \"base_checkpoint\": null,\n",
+      "    \"tensorboard\": false,\n",
+      "    \"save_viz\": false,\n",
+      "    \"zmq\": false,\n",
+      "    \"run_name\": \"courtship.centroid\",\n",
+      "    \"prefix\": \"\",\n",
+      "    \"suffix\": \"\",\n",
+      "    \"cpu\": false,\n",
+      "    \"first_gpu\": false,\n",
+      "    \"last_gpu\": false,\n",
+      "    \"gpu\": \"auto\"\n",
+      "}\n",
+      "INFO:sleap.nn.training:\n",
+      "INFO:sleap.nn.training:Training job:\n",
+      "INFO:sleap.nn.training:{\n",
+      "    \"data\": {\n",
+      "        \"labels\": {\n",
+      "            \"training_labels\": null,\n",
+      "            \"validation_labels\": null,\n",
+      "            \"validation_fraction\": 0.1,\n",
+      "            \"test_labels\": null,\n",
+      "            \"split_by_inds\": false,\n",
+      "            \"training_inds\": null,\n",
+      "            \"validation_inds\": null,\n",
+      "            \"test_inds\": null,\n",
+      "            \"search_path_hints\": [],\n",
+      "            \"skeletons\": []\n",
+      "        },\n",
+      "        \"preprocessing\": {\n",
+      "            \"ensure_rgb\": false,\n",
+      "            \"ensure_grayscale\": false,\n",
+      "            \"imagenet_mode\": null,\n",
+      "            \"input_scaling\": 0.5,\n",
+      "            \"pad_to_stride\": null,\n",
+      "            \"resize_and_pad_to_target\": true,\n",
+      "            \"target_height\": null,\n",
+      "            \"target_width\": null\n",
+      "        },\n",
+      "        \"instance_cropping\": {\n",
+      "            \"center_on_part\": null,\n",
+      "            \"crop_size\": null,\n",
+      "            \"crop_size_detection_padding\": 16\n",
+      "        }\n",
+      "    },\n",
+      "    \"model\": {\n",
+      "        \"backbone\": {\n",
+      "            \"leap\": null,\n",
+      "            \"unet\": {\n",
+      "                \"stem_stride\": null,\n",
+      "                \"max_stride\": 16,\n",
+      "                \"output_stride\": 2,\n",
+      "                \"filters\": 16,\n",
+      "                \"filters_rate\": 2.0,\n",
+      "                \"middle_block\": true,\n",
+      "                \"up_interpolate\": true,\n",
+      "                \"stacks\": 1\n",
+      "            },\n",
+      "            \"hourglass\": null,\n",
+      "            \"resnet\": null,\n",
+      "            \"pretrained_encoder\": null\n",
+      "        },\n",
+      "        \"heads\": {\n",
+      "            \"single_instance\": null,\n",
+      "            \"centroid\": {\n",
+      "                \"anchor_part\": null,\n",
+      "                \"sigma\": 2.5,\n",
+      "                \"output_stride\": 2,\n",
+      "                \"loss_weight\": 1.0,\n",
+      "                \"offset_refinement\": false\n",
+      "            },\n",
+      "            \"centered_instance\": null,\n",
+      "            \"multi_instance\": null,\n",
+      "            \"multi_class_bottomup\": null,\n",
+      "            \"multi_class_topdown\": null\n",
+      "        },\n",
+      "        \"base_checkpoint\": null\n",
+      "    },\n",
+      "    \"optimization\": {\n",
+      "        \"preload_data\": true,\n",
+      "        \"augmentation_config\": {\n",
+      "            \"rotate\": true,\n",
+      "            \"rotation_min_angle\": -15.0,\n",
+      "            \"rotation_max_angle\": 15.0,\n",
+      "            \"translate\": false,\n",
+      "            \"translate_min\": -5,\n",
+      "            \"translate_max\": 5,\n",
+      "            \"scale\": false,\n",
+      "            \"scale_min\": 0.9,\n",
+      "            \"scale_max\": 1.1,\n",
+      "            \"uniform_noise\": false,\n",
+      "            \"uniform_noise_min_val\": 0.0,\n",
+      "            \"uniform_noise_max_val\": 10.0,\n",
+      "            \"gaussian_noise\": false,\n",
+      "            \"gaussian_noise_mean\": 5.0,\n",
+      "            \"gaussian_noise_stddev\": 1.0,\n",
+      "            \"contrast\": false,\n",
+      "            \"contrast_min_gamma\": 0.5,\n",
+      "            \"contrast_max_gamma\": 2.0,\n",
+      "            \"brightness\": false,\n",
+      "            \"brightness_min_val\": 0.0,\n",
+      "            \"brightness_max_val\": 10.0,\n",
+      "            \"random_crop\": false,\n",
+      "            \"random_crop_height\": 256,\n",
+      "            \"random_crop_width\": 256,\n",
+      "            \"random_flip\": false,\n",
+      "            \"flip_horizontal\": true\n",
+      "        },\n",
+      "        \"online_shuffling\": true,\n",
+      "        \"shuffle_buffer_size\": 128,\n",
+      "        \"prefetch\": true,\n",
+      "        \"batch_size\": 4,\n",
+      "        \"batches_per_epoch\": null,\n",
+      "        \"min_batches_per_epoch\": 200,\n",
+      "        \"val_batches_per_epoch\": null,\n",
+      "        \"min_val_batches_per_epoch\": 10,\n",
+      "        \"epochs\": 200,\n",
+      "        \"optimizer\": \"adam\",\n",
+      "        \"initial_learning_rate\": 0.0001,\n",
+      "        \"learning_rate_schedule\": {\n",
+      "            \"reduce_on_plateau\": true,\n",
+      "            \"reduction_factor\": 0.5,\n",
+      "            \"plateau_min_delta\": 1e-08,\n",
+      "            \"plateau_patience\": 5,\n",
+      "            \"plateau_cooldown\": 3,\n",
+      "            \"min_learning_rate\": 1e-08\n",
+      "        },\n",
+      "        \"hard_keypoint_mining\": {\n",
+      "            \"online_mining\": false,\n",
+      "            \"hard_to_easy_ratio\": 2.0,\n",
+      "            \"min_hard_keypoints\": 2,\n",
+      "            \"max_hard_keypoints\": null,\n",
+      "            \"loss_scale\": 5.0\n",
+      "        },\n",
+      "        \"early_stopping\": {\n",
+      "            \"stop_training_on_plateau\": true,\n",
+      "            \"plateau_min_delta\": 1e-08,\n",
+      "            \"plateau_patience\": 20\n",
+      "        }\n",
+      "    },\n",
+      "    \"outputs\": {\n",
+      "        \"save_outputs\": true,\n",
+      "        \"run_name\": \"courtship.centroid\",\n",
+      "        \"run_name_prefix\": \"\",\n",
+      "        \"run_name_suffix\": null,\n",
+      "        \"runs_folder\": \"models\",\n",
+      "        \"tags\": [],\n",
+      "        \"save_visualizations\": true,\n",
+      "        \"delete_viz_images\": true,\n",
+      "        \"zip_outputs\": false,\n",
+      "        \"log_to_csv\": true,\n",
+      "        \"checkpointing\": {\n",
+      "            \"initial_model\": false,\n",
+      "            \"best_model\": true,\n",
+      "            \"every_epoch\": false,\n",
+      "            \"latest_model\": false,\n",
+      "            \"final_model\": false\n",
+      "        },\n",
+      "        \"tensorboard\": {\n",
+      "            \"write_logs\": false,\n",
+      "            \"loss_frequency\": \"epoch\",\n",
+      "            \"architecture_graph\": false,\n",
+      "            \"profile_graph\": false,\n",
+      "            \"visualizations\": true\n",
+      "        },\n",
+      "        \"zmq\": {\n",
+      "            \"subscribe_to_controller\": false,\n",
+      "            \"controller_address\": \"tcp://127.0.0.1:9000\",\n",
+      "            \"controller_polling_timeout\": 10,\n",
+      "            \"publish_updates\": false,\n",
+      "            \"publish_address\": \"tcp://127.0.0.1:9001\"\n",
+      "        }\n",
+      "    },\n",
+      "    \"name\": \"\",\n",
+      "    \"description\": \"\",\n",
+      "    \"sleap_version\": \"1.3.2\",\n",
+      "    \"filename\": \"/home/talmolab/sleap-estimates-animal-poses/pull-requests/sleap/sleap/training_profiles/baseline.centroid.json\"\n",
+      "}\n",
+      "INFO:sleap.nn.training:\n",
+      "2023-09-01 13:30:38.827290: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:30:38.831845: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:30:38.832633: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "INFO:sleap.nn.training:Auto-selected GPU 0 with 22980 MiB of free memory.\n",
+      "INFO:sleap.nn.training:Using GPU 0 for acceleration.\n",
+      "INFO:sleap.nn.training:Disabled GPU memory pre-allocation.\n",
+      "INFO:sleap.nn.training:System:\n",
+      "GPUs: 1/1 available\n",
+      "  Device: /physical_device:GPU:0\n",
+      "         Available: True\n",
+      "        Initalized: False\n",
+      "     Memory growth: True\n",
+      "INFO:sleap.nn.training:\n",
+      "INFO:sleap.nn.training:Initializing trainer...\n",
+      "INFO:sleap.nn.training:Loading training labels from: dataset/drosophila-melanogaster-courtship/courtship_labels.slp\n",
+      "INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1\n",
+      "INFO:sleap.nn.training:  Splits: Training = 134 / Validation = 15.\n",
+      "INFO:sleap.nn.training:Setting up for training...\n",
+      "INFO:sleap.nn.training:Setting up pipeline builders...\n",
+      "INFO:sleap.nn.training:Setting up model...\n",
+      "INFO:sleap.nn.training:Building test pipeline...\n",
+      "2023-09-01 13:30:39.755154: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-09-01 13:30:39.756024: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:30:39.757213: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:30:39.758315: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:30:40.089801: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:30:40.090652: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:30:40.091464: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:30:40.092164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21084 MB memory:  -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
+      "INFO:sleap.nn.training:Loaded test example. [1.326s]\n",
+      "INFO:sleap.nn.training:  Input shape: (512, 512, 3)\n",
+      "INFO:sleap.nn.training:Created Keras model.\n",
+      "INFO:sleap.nn.training:  Backbone: UNet(stacks=1, filters=16, filters_rate=2.0, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=4, middle_block=True, up_blocks=3, up_interpolate=True, block_contraction=False)\n",
+      "INFO:sleap.nn.training:  Max stride: 16\n",
+      "INFO:sleap.nn.training:  Parameters: 1,953,393\n",
+      "INFO:sleap.nn.training:  Heads: \n",
+      "INFO:sleap.nn.training:    [0] = CentroidConfmapsHead(anchor_part=None, sigma=2.5, output_stride=2, loss_weight=1.0)\n",
+      "INFO:sleap.nn.training:  Outputs: \n",
+      "INFO:sleap.nn.training:    [0] = KerasTensor(type_spec=TensorSpec(shape=(None, 256, 256, 1), dtype=tf.float32, name=None), name='CentroidConfmapsHead/BiasAdd:0', description=\"created by layer 'CentroidConfmapsHead'\")\n",
+      "INFO:sleap.nn.training:Training from scratch\n",
+      "INFO:sleap.nn.training:Setting up data pipelines...\n",
+      "INFO:sleap.nn.training:Training set: n = 134\n",
+      "INFO:sleap.nn.training:Validation set: n = 15\n",
+      "INFO:sleap.nn.training:Setting up optimization...\n",
+      "INFO:sleap.nn.training:  Learning rate schedule: LearningRateScheduleConfig(reduce_on_plateau=True, reduction_factor=0.5, plateau_min_delta=1e-08, plateau_patience=5, plateau_cooldown=3, min_learning_rate=1e-08)\n",
+      "INFO:sleap.nn.training:  Early stopping: EarlyStoppingConfig(stop_training_on_plateau=True, plateau_min_delta=1e-08, plateau_patience=20)\n",
+      "INFO:sleap.nn.training:Setting up outputs...\n",
+      "INFO:sleap.nn.training:Created run path: models/courtship.centroid\n",
+      "INFO:sleap.nn.training:Setting up visualization...\n",
+      "INFO:sleap.nn.training:Finished trainer set up. [3.5s]\n",
+      "INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation...\n",
+      "INFO:sleap.nn.training:Finished creating training datasets. [5.4s]\n",
+      "INFO:sleap.nn.training:Starting training loop...\n",
+      "Epoch 1/200\n",
+      "2023-09-01 13:30:49.814560: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n",
+      "2023-09-01 13:31:07.940585: I tensorflow/stream_executor/cuda/cuda_blas.cc:1774] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n",
+      "200/200 - 20s - loss: 2.5945e-04 - val_loss: 1.5190e-04 - lr: 1.0000e-04 - 20s/epoch - 99ms/step\n",
+      "Epoch 2/200\n",
+      "200/200 - 11s - loss: 1.2513e-04 - val_loss: 9.5694e-05 - lr: 1.0000e-04 - 11s/epoch - 57ms/step\n",
+      "Epoch 3/200\n",
+      "200/200 - 11s - loss: 9.6987e-05 - val_loss: 6.8224e-05 - lr: 1.0000e-04 - 11s/epoch - 57ms/step\n",
+      "Epoch 4/200\n",
+      "200/200 - 12s - loss: 8.1486e-05 - val_loss: 5.0657e-05 - lr: 1.0000e-04 - 12s/epoch - 58ms/step\n",
+      "Epoch 5/200\n",
+      "200/200 - 11s - loss: 7.2174e-05 - val_loss: 5.3859e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n",
+      "Epoch 6/200\n",
+      "200/200 - 11s - loss: 5.9181e-05 - val_loss: 7.0259e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n",
+      "Epoch 7/200\n",
+      "200/200 - 11s - loss: 4.9353e-05 - val_loss: 4.9832e-05 - lr: 1.0000e-04 - 11s/epoch - 57ms/step\n",
+      "Epoch 8/200\n",
+      "200/200 - 11s - loss: 3.8997e-05 - val_loss: 4.4787e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n",
+      "Epoch 9/200\n",
+      "200/200 - 11s - loss: 3.5596e-05 - val_loss: 6.5150e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n",
+      "Epoch 10/200\n",
+      "200/200 - 12s - loss: 2.9256e-05 - val_loss: 3.8968e-05 - lr: 1.0000e-04 - 12s/epoch - 58ms/step\n",
+      "Epoch 11/200\n",
+      "200/200 - 11s - loss: 2.8572e-05 - val_loss: 3.5451e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n",
+      "Epoch 12/200\n",
+      "200/200 - 11s - loss: 2.2156e-05 - val_loss: 4.8602e-05 - lr: 1.0000e-04 - 11s/epoch - 53ms/step\n",
+      "Epoch 13/200\n",
+      "200/200 - 11s - loss: 1.7656e-05 - val_loss: 4.1905e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n",
+      "Epoch 14/200\n",
+      "200/200 - 11s - loss: 1.6440e-05 - val_loss: 3.6607e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n",
+      "Epoch 15/200\n",
+      "200/200 - 11s - loss: 1.4415e-05 - val_loss: 4.1699e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n",
+      "Epoch 16/200\n",
+      "200/200 - 11s - loss: 1.3589e-05 - val_loss: 3.5362e-05 - lr: 1.0000e-04 - 11s/epoch - 56ms/step\n",
+      "Epoch 17/200\n",
+      "200/200 - 11s - loss: 1.0888e-05 - val_loss: 2.1600e-05 - lr: 1.0000e-04 - 11s/epoch - 56ms/step\n",
+      "Epoch 18/200\n",
+      "200/200 - 11s - loss: 1.0426e-05 - val_loss: 3.6782e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n",
+      "Epoch 19/200\n",
+      "200/200 - 11s - loss: 9.9092e-06 - val_loss: 3.8284e-05 - lr: 1.0000e-04 - 11s/epoch - 56ms/step\n",
+      "Epoch 20/200\n",
+      "200/200 - 11s - loss: 8.0018e-06 - val_loss: 2.9439e-05 - lr: 1.0000e-04 - 11s/epoch - 57ms/step\n",
+      "Epoch 21/200\n",
+      "200/200 - 11s - loss: 7.7977e-06 - val_loss: 2.8703e-05 - lr: 1.0000e-04 - 11s/epoch - 56ms/step\n",
+      "Epoch 22/200\n",
+      "\n",
+      "Epoch 00022: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n",
+      "200/200 - 11s - loss: 6.5981e-06 - val_loss: 3.6030e-05 - lr: 1.0000e-04 - 11s/epoch - 55ms/step\n",
+      "Epoch 23/200\n",
+      "200/200 - 11s - loss: 4.6479e-06 - val_loss: 2.8081e-05 - lr: 5.0000e-05 - 11s/epoch - 55ms/step\n",
+      "Epoch 24/200\n",
+      "200/200 - 11s - loss: 4.2579e-06 - val_loss: 3.7954e-05 - lr: 5.0000e-05 - 11s/epoch - 55ms/step\n",
+      "Epoch 25/200\n",
+      "200/200 - 11s - loss: 3.9628e-06 - val_loss: 2.6399e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 26/200\n",
+      "200/200 - 11s - loss: 3.6915e-06 - val_loss: 1.9973e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 27/200\n",
+      "200/200 - 11s - loss: 3.4726e-06 - val_loss: 3.5831e-05 - lr: 5.0000e-05 - 11s/epoch - 55ms/step\n",
+      "Epoch 28/200\n",
+      "200/200 - 11s - loss: 3.2110e-06 - val_loss: 2.7290e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 29/200\n",
+      "200/200 - 11s - loss: 3.3421e-06 - val_loss: 3.1827e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 30/200\n",
+      "200/200 - 11s - loss: 3.3472e-06 - val_loss: 3.4653e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 31/200\n",
+      "\n",
+      "Epoch 00031: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n",
+      "200/200 - 11s - loss: 3.1221e-06 - val_loss: 2.7741e-05 - lr: 5.0000e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 32/200\n",
+      "200/200 - 11s - loss: 2.5739e-06 - val_loss: 3.2486e-05 - lr: 2.5000e-05 - 11s/epoch - 55ms/step\n",
+      "Epoch 33/200\n",
+      "200/200 - 11s - loss: 2.5589e-06 - val_loss: 3.3135e-05 - lr: 2.5000e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 34/200\n",
+      "200/200 - 11s - loss: 2.4215e-06 - val_loss: 2.8923e-05 - lr: 2.5000e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 35/200\n",
+      "200/200 - 11s - loss: 2.4033e-06 - val_loss: 2.8776e-05 - lr: 2.5000e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 36/200\n",
+      "200/200 - 11s - loss: 2.3358e-06 - val_loss: 2.5874e-05 - lr: 2.5000e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 37/200\n",
+      "200/200 - 11s - loss: 2.2922e-06 - val_loss: 3.6051e-05 - lr: 2.5000e-05 - 11s/epoch - 55ms/step\n",
+      "Epoch 38/200\n",
+      "\n",
+      "Epoch 00038: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n",
+      "200/200 - 11s - loss: 2.1278e-06 - val_loss: 2.4898e-05 - lr: 2.5000e-05 - 11s/epoch - 55ms/step\n",
+      "Epoch 39/200\n",
+      "200/200 - 11s - loss: 2.0474e-06 - val_loss: 2.8901e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 40/200\n",
+      "200/200 - 11s - loss: 2.0612e-06 - val_loss: 3.7469e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 41/200\n",
+      "200/200 - 11s - loss: 1.8414e-06 - val_loss: 2.8496e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 42/200\n",
+      "200/200 - 11s - loss: 2.0196e-06 - val_loss: 3.5206e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 43/200\n",
+      "200/200 - 11s - loss: 1.8551e-06 - val_loss: 2.6483e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 44/200\n",
+      "200/200 - 11s - loss: 1.9705e-06 - val_loss: 2.4643e-05 - lr: 1.2500e-05 - 11s/epoch - 55ms/step\n",
+      "Epoch 45/200\n",
+      "\n",
+      "Epoch 00045: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n",
+      "200/200 - 11s - loss: 1.9136e-06 - val_loss: 2.8379e-05 - lr: 1.2500e-05 - 11s/epoch - 56ms/step\n",
+      "Epoch 46/200\n",
+      "200/200 - 11s - loss: 1.7911e-06 - val_loss: 4.0055e-05 - lr: 6.2500e-06 - 11s/epoch - 56ms/step\n",
+      "Epoch 00046: early stopping\n",
+      "INFO:sleap.nn.training:Finished training loop. [8.7 min]\n",
+      "INFO:sleap.nn.training:Deleting visualization directory: models/courtship.centroid/viz\n",
+      "INFO:sleap.nn.training:Saving evaluation metrics to model folder...\n",
+      "\u001b[2KPredicting... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m ETA: \u001b[36m0:00:00\u001b[0m \u001b[31m33.7 FPS\u001b[0m31m51.9 FPS\u001b[0m31m52.6 FPS\u001b[0mFPS\u001b[0m\n",
+      "\u001b[?25hINFO:sleap.nn.evals:Saved predictions: models/courtship.centroid/labels_pr.train.slp\n",
+      "INFO:sleap.nn.evals:Saved metrics: models/courtship.centroid/metrics.train.npz\n",
+      "INFO:sleap.nn.evals:OKS mAP: 0.725241\n",
+      "\u001b[2KPredicting... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m ETA: \u001b[36m0:00:00\u001b[0m \u001b[31m7.3 FPS\u001b[0m0:00:01\u001b[0m \u001b[31m184.6 FPS\u001b[0mm\n",
+      "\u001b[?25hINFO:sleap.nn.evals:Saved predictions: models/courtship.centroid/labels_pr.val.slp\n",
+      "INFO:sleap.nn.evals:Saved metrics: models/courtship.centroid/metrics.val.npz\n",
+      "INFO:sleap.nn.evals:OKS mAP: 0.870526\n"
+     ]
+    }
+   ],
    "source": [
     "!sleap-train baseline.centroid.json \"dataset/drosophila-melanogaster-courtship/courtship_labels.slp\" --run_name \"courtship.centroid\" --video-paths \"dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\""
    ]
@@ -125,11 +552,361 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {
     "id": "ufbULTDw4Hbh"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:sleap.nn.training:Versions:\n",
+      "SLEAP: 1.3.2\n",
+      "TensorFlow: 2.7.0\n",
+      "Numpy: 1.21.5\n",
+      "Python: 3.7.12\n",
+      "OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n",
+      "INFO:sleap.nn.training:Training labels file: dataset/drosophila-melanogaster-courtship/courtship_labels.slp\n",
+      "INFO:sleap.nn.training:Training profile: /home/talmolab/sleap-estimates-animal-poses/pull-requests/sleap/sleap/training_profiles/baseline_medium_rf.topdown.json\n",
+      "INFO:sleap.nn.training:\n",
+      "INFO:sleap.nn.training:Arguments:\n",
+      "INFO:sleap.nn.training:{\n",
+      "    \"training_job_path\": \"baseline_medium_rf.topdown.json\",\n",
+      "    \"labels_path\": \"dataset/drosophila-melanogaster-courtship/courtship_labels.slp\",\n",
+      "    \"video_paths\": [\n",
+      "        \"dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\"\n",
+      "    ],\n",
+      "    \"val_labels\": null,\n",
+      "    \"test_labels\": null,\n",
+      "    \"base_checkpoint\": null,\n",
+      "    \"tensorboard\": false,\n",
+      "    \"save_viz\": false,\n",
+      "    \"zmq\": false,\n",
+      "    \"run_name\": \"courtship.topdown_confmaps\",\n",
+      "    \"prefix\": \"\",\n",
+      "    \"suffix\": \"\",\n",
+      "    \"cpu\": false,\n",
+      "    \"first_gpu\": false,\n",
+      "    \"last_gpu\": false,\n",
+      "    \"gpu\": \"auto\"\n",
+      "}\n",
+      "INFO:sleap.nn.training:\n",
+      "INFO:sleap.nn.training:Training job:\n",
+      "INFO:sleap.nn.training:{\n",
+      "    \"data\": {\n",
+      "        \"labels\": {\n",
+      "            \"training_labels\": null,\n",
+      "            \"validation_labels\": null,\n",
+      "            \"validation_fraction\": 0.1,\n",
+      "            \"test_labels\": null,\n",
+      "            \"split_by_inds\": false,\n",
+      "            \"training_inds\": null,\n",
+      "            \"validation_inds\": null,\n",
+      "            \"test_inds\": null,\n",
+      "            \"search_path_hints\": [],\n",
+      "            \"skeletons\": []\n",
+      "        },\n",
+      "        \"preprocessing\": {\n",
+      "            \"ensure_rgb\": false,\n",
+      "            \"ensure_grayscale\": false,\n",
+      "            \"imagenet_mode\": null,\n",
+      "            \"input_scaling\": 1.0,\n",
+      "            \"pad_to_stride\": null,\n",
+      "            \"resize_and_pad_to_target\": true,\n",
+      "            \"target_height\": null,\n",
+      "            \"target_width\": null\n",
+      "        },\n",
+      "        \"instance_cropping\": {\n",
+      "            \"center_on_part\": null,\n",
+      "            \"crop_size\": null,\n",
+      "            \"crop_size_detection_padding\": 16\n",
+      "        }\n",
+      "    },\n",
+      "    \"model\": {\n",
+      "        \"backbone\": {\n",
+      "            \"leap\": null,\n",
+      "            \"unet\": {\n",
+      "                \"stem_stride\": null,\n",
+      "                \"max_stride\": 16,\n",
+      "                \"output_stride\": 4,\n",
+      "                \"filters\": 24,\n",
+      "                \"filters_rate\": 2.0,\n",
+      "                \"middle_block\": true,\n",
+      "                \"up_interpolate\": true,\n",
+      "                \"stacks\": 1\n",
+      "            },\n",
+      "            \"hourglass\": null,\n",
+      "            \"resnet\": null,\n",
+      "            \"pretrained_encoder\": null\n",
+      "        },\n",
+      "        \"heads\": {\n",
+      "            \"single_instance\": null,\n",
+      "            \"centroid\": null,\n",
+      "            \"centered_instance\": {\n",
+      "                \"anchor_part\": null,\n",
+      "                \"part_names\": null,\n",
+      "                \"sigma\": 2.5,\n",
+      "                \"output_stride\": 4,\n",
+      "                \"loss_weight\": 1.0,\n",
+      "                \"offset_refinement\": false\n",
+      "            },\n",
+      "            \"multi_instance\": null,\n",
+      "            \"multi_class_bottomup\": null,\n",
+      "            \"multi_class_topdown\": null\n",
+      "        },\n",
+      "        \"base_checkpoint\": null\n",
+      "    },\n",
+      "    \"optimization\": {\n",
+      "        \"preload_data\": true,\n",
+      "        \"augmentation_config\": {\n",
+      "            \"rotate\": true,\n",
+      "            \"rotation_min_angle\": -15.0,\n",
+      "            \"rotation_max_angle\": 15.0,\n",
+      "            \"translate\": false,\n",
+      "            \"translate_min\": -5,\n",
+      "            \"translate_max\": 5,\n",
+      "            \"scale\": false,\n",
+      "            \"scale_min\": 0.9,\n",
+      "            \"scale_max\": 1.1,\n",
+      "            \"uniform_noise\": false,\n",
+      "            \"uniform_noise_min_val\": 0.0,\n",
+      "            \"uniform_noise_max_val\": 10.0,\n",
+      "            \"gaussian_noise\": false,\n",
+      "            \"gaussian_noise_mean\": 5.0,\n",
+      "            \"gaussian_noise_stddev\": 1.0,\n",
+      "            \"contrast\": false,\n",
+      "            \"contrast_min_gamma\": 0.5,\n",
+      "            \"contrast_max_gamma\": 2.0,\n",
+      "            \"brightness\": false,\n",
+      "            \"brightness_min_val\": 0.0,\n",
+      "            \"brightness_max_val\": 10.0,\n",
+      "            \"random_crop\": false,\n",
+      "            \"random_crop_height\": 256,\n",
+      "            \"random_crop_width\": 256,\n",
+      "            \"random_flip\": false,\n",
+      "            \"flip_horizontal\": true\n",
+      "        },\n",
+      "        \"online_shuffling\": true,\n",
+      "        \"shuffle_buffer_size\": 128,\n",
+      "        \"prefetch\": true,\n",
+      "        \"batch_size\": 4,\n",
+      "        \"batches_per_epoch\": null,\n",
+      "        \"min_batches_per_epoch\": 200,\n",
+      "        \"val_batches_per_epoch\": null,\n",
+      "        \"min_val_batches_per_epoch\": 10,\n",
+      "        \"epochs\": 200,\n",
+      "        \"optimizer\": \"adam\",\n",
+      "        \"initial_learning_rate\": 0.0001,\n",
+      "        \"learning_rate_schedule\": {\n",
+      "            \"reduce_on_plateau\": true,\n",
+      "            \"reduction_factor\": 0.5,\n",
+      "            \"plateau_min_delta\": 1e-08,\n",
+      "            \"plateau_patience\": 5,\n",
+      "            \"plateau_cooldown\": 3,\n",
+      "            \"min_learning_rate\": 1e-08\n",
+      "        },\n",
+      "        \"hard_keypoint_mining\": {\n",
+      "            \"online_mining\": false,\n",
+      "            \"hard_to_easy_ratio\": 2.0,\n",
+      "            \"min_hard_keypoints\": 2,\n",
+      "            \"max_hard_keypoints\": null,\n",
+      "            \"loss_scale\": 5.0\n",
+      "        },\n",
+      "        \"early_stopping\": {\n",
+      "            \"stop_training_on_plateau\": true,\n",
+      "            \"plateau_min_delta\": 1e-08,\n",
+      "            \"plateau_patience\": 10\n",
+      "        }\n",
+      "    },\n",
+      "    \"outputs\": {\n",
+      "        \"save_outputs\": true,\n",
+      "        \"run_name\": \"courtship.topdown_confmaps\",\n",
+      "        \"run_name_prefix\": \"\",\n",
+      "        \"run_name_suffix\": null,\n",
+      "        \"runs_folder\": \"models\",\n",
+      "        \"tags\": [],\n",
+      "        \"save_visualizations\": true,\n",
+      "        \"delete_viz_images\": true,\n",
+      "        \"zip_outputs\": false,\n",
+      "        \"log_to_csv\": true,\n",
+      "        \"checkpointing\": {\n",
+      "            \"initial_model\": false,\n",
+      "            \"best_model\": true,\n",
+      "            \"every_epoch\": false,\n",
+      "            \"latest_model\": false,\n",
+      "            \"final_model\": false\n",
+      "        },\n",
+      "        \"tensorboard\": {\n",
+      "            \"write_logs\": false,\n",
+      "            \"loss_frequency\": \"epoch\",\n",
+      "            \"architecture_graph\": true,\n",
+      "            \"profile_graph\": false,\n",
+      "            \"visualizations\": true\n",
+      "        },\n",
+      "        \"zmq\": {\n",
+      "            \"subscribe_to_controller\": false,\n",
+      "            \"controller_address\": \"tcp://127.0.0.1:9000\",\n",
+      "            \"controller_polling_timeout\": 10,\n",
+      "            \"publish_updates\": false,\n",
+      "            \"publish_address\": \"tcp://127.0.0.1:9001\"\n",
+      "        }\n",
+      "    },\n",
+      "    \"name\": \"\",\n",
+      "    \"description\": \"\",\n",
+      "    \"sleap_version\": \"1.3.2\",\n",
+      "    \"filename\": \"/home/talmolab/sleap-estimates-animal-poses/pull-requests/sleap/sleap/training_profiles/baseline_medium_rf.topdown.json\"\n",
+      "}\n",
+      "INFO:sleap.nn.training:\n",
+      "2023-09-01 13:39:43.324520: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:39:43.329181: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:39:43.329961: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "INFO:sleap.nn.training:Auto-selected GPU 0 with 23056 MiB of free memory.\n",
+      "INFO:sleap.nn.training:Using GPU 0 for acceleration.\n",
+      "INFO:sleap.nn.training:Disabled GPU memory pre-allocation.\n",
+      "INFO:sleap.nn.training:System:\n",
+      "GPUs: 1/1 available\n",
+      "  Device: /physical_device:GPU:0\n",
+      "         Available: True\n",
+      "        Initalized: False\n",
+      "     Memory growth: True\n",
+      "INFO:sleap.nn.training:\n",
+      "INFO:sleap.nn.training:Initializing trainer...\n",
+      "INFO:sleap.nn.training:Loading training labels from: dataset/drosophila-melanogaster-courtship/courtship_labels.slp\n",
+      "INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1\n",
+      "INFO:sleap.nn.training:  Splits: Training = 134 / Validation = 15.\n",
+      "INFO:sleap.nn.training:Setting up for training...\n",
+      "INFO:sleap.nn.training:Setting up pipeline builders...\n",
+      "INFO:sleap.nn.training:Setting up model...\n",
+      "INFO:sleap.nn.training:Building test pipeline...\n",
+      "2023-09-01 13:39:44.254912: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-09-01 13:39:44.255468: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:39:44.256291: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:39:44.257158: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:39:44.546117: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:39:44.546866: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:39:44.547533: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:39:44.548184: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21151 MB memory:  -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
+      "INFO:sleap.nn.training:Loaded test example. [1.684s]\n",
+      "INFO:sleap.nn.training:  Input shape: (144, 144, 3)\n",
+      "INFO:sleap.nn.training:Created Keras model.\n",
+      "INFO:sleap.nn.training:  Backbone: UNet(stacks=1, filters=24, filters_rate=2.0, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=4, middle_block=True, up_blocks=2, up_interpolate=True, block_contraction=False)\n",
+      "INFO:sleap.nn.training:  Max stride: 16\n",
+      "INFO:sleap.nn.training:  Parameters: 4,311,877\n",
+      "INFO:sleap.nn.training:  Heads: \n",
+      "INFO:sleap.nn.training:    [0] = CenteredInstanceConfmapsHead(part_names=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], anchor_part=None, sigma=2.5, output_stride=4, loss_weight=1.0)\n",
+      "INFO:sleap.nn.training:  Outputs: \n",
+      "INFO:sleap.nn.training:    [0] = KerasTensor(type_spec=TensorSpec(shape=(None, 36, 36, 13), dtype=tf.float32, name=None), name='CenteredInstanceConfmapsHead/BiasAdd:0', description=\"created by layer 'CenteredInstanceConfmapsHead'\")\n",
+      "INFO:sleap.nn.training:Training from scratch\n",
+      "INFO:sleap.nn.training:Setting up data pipelines...\n",
+      "INFO:sleap.nn.training:Training set: n = 134\n",
+      "INFO:sleap.nn.training:Validation set: n = 15\n",
+      "INFO:sleap.nn.training:Setting up optimization...\n",
+      "INFO:sleap.nn.training:  Learning rate schedule: LearningRateScheduleConfig(reduce_on_plateau=True, reduction_factor=0.5, plateau_min_delta=1e-08, plateau_patience=5, plateau_cooldown=3, min_learning_rate=1e-08)\n",
+      "INFO:sleap.nn.training:  Early stopping: EarlyStoppingConfig(stop_training_on_plateau=True, plateau_min_delta=1e-08, plateau_patience=10)\n",
+      "INFO:sleap.nn.training:Setting up outputs...\n",
+      "INFO:sleap.nn.training:Created run path: models/courtship.topdown_confmaps\n",
+      "INFO:sleap.nn.training:Setting up visualization...\n",
+      "INFO:sleap.nn.training:Finished trainer set up. [3.2s]\n",
+      "INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation...\n",
+      "INFO:sleap.nn.training:Finished creating training datasets. [5.9s]\n",
+      "INFO:sleap.nn.training:Starting training loop...\n",
+      "Epoch 1/200\n",
+      "2023-09-01 13:39:54.940083: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n",
+      "2023-09-01 13:40:00.337645: I tensorflow/stream_executor/cuda/cuda_blas.cc:1774] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n",
+      "200/200 - 8s - loss: 0.0108 - head: 0.0073 - thorax: 0.0067 - abdomen: 0.0111 - wingL: 0.0125 - wingR: 0.0126 - forelegL4: 0.0111 - forelegR4: 0.0108 - midlegL4: 0.0127 - midlegR4: 0.0128 - hindlegL4: 0.0131 - hindlegR4: 0.0131 - eyeL: 0.0082 - eyeR: 0.0083 - val_loss: 0.0087 - val_head: 0.0033 - val_thorax: 0.0039 - val_abdomen: 0.0089 - val_wingL: 0.0105 - val_wingR: 0.0106 - val_forelegL4: 0.0091 - val_forelegR4: 0.0091 - val_midlegL4: 0.0123 - val_midlegR4: 0.0116 - val_hindlegL4: 0.0128 - val_hindlegR4: 0.0116 - val_eyeL: 0.0045 - val_eyeR: 0.0045 - lr: 1.0000e-04 - 8s/epoch - 38ms/step\n",
+      "Epoch 2/200\n",
+      "200/200 - 4s - loss: 0.0064 - head: 0.0019 - thorax: 0.0029 - abdomen: 0.0057 - wingL: 0.0061 - wingR: 0.0073 - forelegL4: 0.0075 - forelegR4: 0.0078 - midlegL4: 0.0092 - midlegR4: 0.0092 - hindlegL4: 0.0099 - hindlegR4: 0.0102 - eyeL: 0.0025 - eyeR: 0.0025 - val_loss: 0.0061 - val_head: 0.0015 - val_thorax: 0.0024 - val_abdomen: 0.0049 - val_wingL: 0.0056 - val_wingR: 0.0078 - val_forelegL4: 0.0079 - val_forelegR4: 0.0067 - val_midlegL4: 0.0086 - val_midlegR4: 0.0089 - val_hindlegL4: 0.0093 - val_hindlegR4: 0.0081 - val_eyeL: 0.0037 - val_eyeR: 0.0032 - lr: 1.0000e-04 - 4s/epoch - 19ms/step\n",
+      "Epoch 3/200\n",
+      "200/200 - 3s - loss: 0.0048 - head: 8.9048e-04 - thorax: 0.0019 - abdomen: 0.0036 - wingL: 0.0041 - wingR: 0.0051 - forelegL4: 0.0063 - forelegR4: 0.0066 - midlegL4: 0.0076 - midlegR4: 0.0076 - hindlegL4: 0.0076 - hindlegR4: 0.0080 - eyeL: 0.0015 - eyeR: 0.0015 - val_loss: 0.0058 - val_head: 0.0014 - val_thorax: 0.0021 - val_abdomen: 0.0044 - val_wingL: 0.0051 - val_wingR: 0.0070 - val_forelegL4: 0.0072 - val_forelegR4: 0.0063 - val_midlegL4: 0.0088 - val_midlegR4: 0.0085 - val_hindlegL4: 0.0097 - val_hindlegR4: 0.0079 - val_eyeL: 0.0038 - val_eyeR: 0.0032 - lr: 1.0000e-04 - 3s/epoch - 16ms/step\n",
+      "Epoch 4/200\n",
+      "200/200 - 3s - loss: 0.0041 - head: 7.6417e-04 - thorax: 0.0015 - abdomen: 0.0028 - wingL: 0.0035 - wingR: 0.0041 - forelegL4: 0.0058 - forelegR4: 0.0060 - midlegL4: 0.0066 - midlegR4: 0.0064 - hindlegL4: 0.0066 - hindlegR4: 0.0070 - eyeL: 0.0013 - eyeR: 0.0012 - val_loss: 0.0048 - val_head: 7.6555e-04 - val_thorax: 0.0013 - val_abdomen: 0.0034 - val_wingL: 0.0042 - val_wingR: 0.0065 - val_forelegL4: 0.0063 - val_forelegR4: 0.0064 - val_midlegL4: 0.0069 - val_midlegR4: 0.0071 - val_hindlegL4: 0.0080 - val_hindlegR4: 0.0062 - val_eyeL: 0.0028 - val_eyeR: 0.0026 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 5/200\n",
+      "200/200 - 3s - loss: 0.0034 - head: 6.1233e-04 - thorax: 0.0012 - abdomen: 0.0023 - wingL: 0.0028 - wingR: 0.0032 - forelegL4: 0.0052 - forelegR4: 0.0054 - midlegL4: 0.0052 - midlegR4: 0.0051 - hindlegL4: 0.0057 - hindlegR4: 0.0058 - eyeL: 0.0011 - eyeR: 0.0011 - val_loss: 0.0044 - val_head: 9.3809e-04 - val_thorax: 0.0012 - val_abdomen: 0.0027 - val_wingL: 0.0032 - val_wingR: 0.0048 - val_forelegL4: 0.0062 - val_forelegR4: 0.0053 - val_midlegL4: 0.0068 - val_midlegR4: 0.0063 - val_hindlegL4: 0.0067 - val_hindlegR4: 0.0065 - val_eyeL: 0.0035 - val_eyeR: 0.0032 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 6/200\n",
+      "200/200 - 3s - loss: 0.0028 - head: 5.5957e-04 - thorax: 9.3519e-04 - abdomen: 0.0019 - wingL: 0.0023 - wingR: 0.0025 - forelegL4: 0.0045 - forelegR4: 0.0045 - midlegL4: 0.0040 - midlegR4: 0.0040 - hindlegL4: 0.0047 - hindlegR4: 0.0048 - eyeL: 0.0010 - eyeR: 9.7287e-04 - val_loss: 0.0038 - val_head: 7.6837e-04 - val_thorax: 9.9723e-04 - val_abdomen: 0.0027 - val_wingL: 0.0025 - val_wingR: 0.0046 - val_forelegL4: 0.0058 - val_forelegR4: 0.0049 - val_midlegL4: 0.0054 - val_midlegR4: 0.0058 - val_hindlegL4: 0.0057 - val_hindlegR4: 0.0065 - val_eyeL: 0.0023 - val_eyeR: 0.0022 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 7/200\n",
+      "200/200 - 3s - loss: 0.0024 - head: 4.7941e-04 - thorax: 7.5772e-04 - abdomen: 0.0017 - wingL: 0.0020 - wingR: 0.0022 - forelegL4: 0.0039 - forelegR4: 0.0041 - midlegL4: 0.0033 - midlegR4: 0.0033 - hindlegL4: 0.0039 - hindlegR4: 0.0040 - eyeL: 9.3055e-04 - eyeR: 8.9191e-04 - val_loss: 0.0036 - val_head: 6.1078e-04 - val_thorax: 0.0010 - val_abdomen: 0.0023 - val_wingL: 0.0025 - val_wingR: 0.0039 - val_forelegL4: 0.0053 - val_forelegR4: 0.0058 - val_midlegL4: 0.0049 - val_midlegR4: 0.0056 - val_hindlegL4: 0.0054 - val_hindlegR4: 0.0049 - val_eyeL: 0.0026 - val_eyeR: 0.0024 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 8/200\n",
+      "200/200 - 3s - loss: 0.0020 - head: 4.4425e-04 - thorax: 6.8283e-04 - abdomen: 0.0014 - wingL: 0.0015 - wingR: 0.0017 - forelegL4: 0.0035 - forelegR4: 0.0035 - midlegL4: 0.0027 - midlegR4: 0.0026 - hindlegL4: 0.0033 - hindlegR4: 0.0033 - eyeL: 7.7111e-04 - eyeR: 7.2022e-04 - val_loss: 0.0035 - val_head: 7.1555e-04 - val_thorax: 9.1508e-04 - val_abdomen: 0.0022 - val_wingL: 0.0023 - val_wingR: 0.0033 - val_forelegL4: 0.0054 - val_forelegR4: 0.0049 - val_midlegL4: 0.0049 - val_midlegR4: 0.0052 - val_hindlegL4: 0.0052 - val_hindlegR4: 0.0051 - val_eyeL: 0.0025 - val_eyeR: 0.0025 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 9/200\n",
+      "200/200 - 3s - loss: 0.0017 - head: 3.8990e-04 - thorax: 5.4963e-04 - abdomen: 0.0012 - wingL: 0.0012 - wingR: 0.0014 - forelegL4: 0.0030 - forelegR4: 0.0031 - midlegL4: 0.0022 - midlegR4: 0.0022 - hindlegL4: 0.0027 - hindlegR4: 0.0027 - eyeL: 6.9041e-04 - eyeR: 6.7679e-04 - val_loss: 0.0034 - val_head: 5.6666e-04 - val_thorax: 7.9156e-04 - val_abdomen: 0.0023 - val_wingL: 0.0020 - val_wingR: 0.0041 - val_forelegL4: 0.0043 - val_forelegR4: 0.0048 - val_midlegL4: 0.0041 - val_midlegR4: 0.0051 - val_hindlegL4: 0.0053 - val_hindlegR4: 0.0052 - val_eyeL: 0.0024 - val_eyeR: 0.0026 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 10/200\n",
+      "200/200 - 3s - loss: 0.0015 - head: 3.6281e-04 - thorax: 5.2471e-04 - abdomen: 0.0010 - wingL: 0.0011 - wingR: 0.0012 - forelegL4: 0.0027 - forelegR4: 0.0028 - midlegL4: 0.0019 - midlegR4: 0.0019 - hindlegL4: 0.0023 - hindlegR4: 0.0024 - eyeL: 7.0986e-04 - eyeR: 6.9581e-04 - val_loss: 0.0024 - val_head: 4.8376e-04 - val_thorax: 6.2502e-04 - val_abdomen: 0.0016 - val_wingL: 0.0014 - val_wingR: 0.0027 - val_forelegL4: 0.0035 - val_forelegR4: 0.0033 - val_midlegL4: 0.0028 - val_midlegR4: 0.0041 - val_hindlegL4: 0.0036 - val_hindlegR4: 0.0038 - val_eyeL: 0.0015 - val_eyeR: 0.0016 - lr: 1.0000e-04 - 3s/epoch - 16ms/step\n",
+      "Epoch 11/200\n",
+      "200/200 - 3s - loss: 0.0013 - head: 3.1183e-04 - thorax: 4.7891e-04 - abdomen: 9.4567e-04 - wingL: 9.6811e-04 - wingR: 0.0011 - forelegL4: 0.0023 - forelegR4: 0.0025 - midlegL4: 0.0016 - midlegR4: 0.0016 - hindlegL4: 0.0020 - hindlegR4: 0.0021 - eyeL: 5.7635e-04 - eyeR: 5.3648e-04 - val_loss: 0.0028 - val_head: 5.2940e-04 - val_thorax: 6.6554e-04 - val_abdomen: 0.0020 - val_wingL: 0.0013 - val_wingR: 0.0024 - val_forelegL4: 0.0041 - val_forelegR4: 0.0041 - val_midlegL4: 0.0034 - val_midlegR4: 0.0042 - val_hindlegL4: 0.0047 - val_hindlegR4: 0.0040 - val_eyeL: 0.0025 - val_eyeR: 0.0022 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 12/200\n",
+      "200/200 - 3s - loss: 0.0011 - head: 2.8863e-04 - thorax: 4.2604e-04 - abdomen: 8.0488e-04 - wingL: 8.1238e-04 - wingR: 8.5798e-04 - forelegL4: 0.0021 - forelegR4: 0.0021 - midlegL4: 0.0014 - midlegR4: 0.0014 - hindlegL4: 0.0017 - hindlegR4: 0.0018 - eyeL: 5.1007e-04 - eyeR: 4.5654e-04 - val_loss: 0.0031 - val_head: 8.1802e-04 - val_thorax: 7.9789e-04 - val_abdomen: 0.0018 - val_wingL: 0.0014 - val_wingR: 0.0028 - val_forelegL4: 0.0040 - val_forelegR4: 0.0048 - val_midlegL4: 0.0057 - val_midlegR4: 0.0037 - val_hindlegL4: 0.0053 - val_hindlegR4: 0.0050 - val_eyeL: 0.0020 - val_eyeR: 0.0018 - lr: 1.0000e-04 - 3s/epoch - 14ms/step\n",
+      "Epoch 13/200\n",
+      "200/200 - 3s - loss: 0.0010 - head: 2.8818e-04 - thorax: 4.1018e-04 - abdomen: 7.8027e-04 - wingL: 7.8017e-04 - wingR: 8.4529e-04 - forelegL4: 0.0019 - forelegR4: 0.0019 - midlegL4: 0.0013 - midlegR4: 0.0013 - hindlegL4: 0.0015 - hindlegR4: 0.0016 - eyeL: 4.6272e-04 - eyeR: 4.3265e-04 - val_loss: 0.0026 - val_head: 3.5806e-04 - val_thorax: 6.6352e-04 - val_abdomen: 0.0017 - val_wingL: 0.0015 - val_wingR: 0.0037 - val_forelegL4: 0.0036 - val_forelegR4: 0.0042 - val_midlegL4: 0.0034 - val_midlegR4: 0.0032 - val_hindlegL4: 0.0041 - val_hindlegR4: 0.0047 - val_eyeL: 0.0013 - val_eyeR: 0.0013 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 14/200\n",
+      "200/200 - 3s - loss: 9.4029e-04 - head: 2.8339e-04 - thorax: 3.6739e-04 - abdomen: 7.0118e-04 - wingL: 7.4831e-04 - wingR: 7.1158e-04 - forelegL4: 0.0017 - forelegR4: 0.0017 - midlegL4: 0.0012 - midlegR4: 0.0011 - hindlegL4: 0.0014 - hindlegR4: 0.0015 - eyeL: 4.2793e-04 - eyeR: 4.1400e-04 - val_loss: 0.0024 - val_head: 3.4292e-04 - val_thorax: 7.1119e-04 - val_abdomen: 0.0014 - val_wingL: 0.0013 - val_wingR: 0.0028 - val_forelegL4: 0.0030 - val_forelegR4: 0.0043 - val_midlegL4: 0.0031 - val_midlegR4: 0.0030 - val_hindlegL4: 0.0039 - val_hindlegR4: 0.0038 - val_eyeL: 0.0017 - val_eyeR: 0.0015 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 15/200\n",
+      "200/200 - 3s - loss: 7.8295e-04 - head: 2.3028e-04 - thorax: 3.3006e-04 - abdomen: 5.9391e-04 - wingL: 5.8825e-04 - wingR: 6.0989e-04 - forelegL4: 0.0015 - forelegR4: 0.0015 - midlegL4: 9.6945e-04 - midlegR4: 9.3611e-04 - hindlegL4: 0.0011 - hindlegR4: 0.0012 - eyeL: 3.4493e-04 - eyeR: 3.1164e-04 - val_loss: 0.0019 - val_head: 4.4152e-04 - val_thorax: 5.4500e-04 - val_abdomen: 0.0013 - val_wingL: 0.0012 - val_wingR: 0.0026 - val_forelegL4: 0.0024 - val_forelegR4: 0.0037 - val_midlegL4: 0.0024 - val_midlegR4: 0.0024 - val_hindlegL4: 0.0030 - val_hindlegR4: 0.0030 - val_eyeL: 0.0011 - val_eyeR: 0.0011 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 16/200\n",
+      "200/200 - 3s - loss: 7.3208e-04 - head: 2.3573e-04 - thorax: 3.0631e-04 - abdomen: 5.5007e-04 - wingL: 5.3431e-04 - wingR: 5.9773e-04 - forelegL4: 0.0013 - forelegR4: 0.0014 - midlegL4: 9.1004e-04 - midlegR4: 8.7803e-04 - hindlegL4: 0.0010 - hindlegR4: 0.0011 - eyeL: 3.3279e-04 - eyeR: 2.9841e-04 - val_loss: 0.0023 - val_head: 3.5381e-04 - val_thorax: 7.0128e-04 - val_abdomen: 0.0015 - val_wingL: 0.0013 - val_wingR: 0.0022 - val_forelegL4: 0.0031 - val_forelegR4: 0.0041 - val_midlegL4: 0.0033 - val_midlegR4: 0.0028 - val_hindlegL4: 0.0036 - val_hindlegR4: 0.0033 - val_eyeL: 0.0017 - val_eyeR: 0.0014 - lr: 1.0000e-04 - 3s/epoch - 14ms/step\n",
+      "Epoch 17/200\n",
+      "200/200 - 3s - loss: 6.3161e-04 - head: 2.0100e-04 - thorax: 2.8088e-04 - abdomen: 4.9153e-04 - wingL: 4.7586e-04 - wingR: 4.9866e-04 - forelegL4: 0.0011 - forelegR4: 0.0012 - midlegL4: 7.6100e-04 - midlegR4: 8.0266e-04 - hindlegL4: 8.9697e-04 - hindlegR4: 8.9149e-04 - eyeL: 2.8189e-04 - eyeR: 2.7208e-04 - val_loss: 0.0018 - val_head: 2.8070e-04 - val_thorax: 5.1903e-04 - val_abdomen: 0.0011 - val_wingL: 9.8509e-04 - val_wingR: 0.0025 - val_forelegL4: 0.0022 - val_forelegR4: 0.0026 - val_midlegL4: 0.0025 - val_midlegR4: 0.0021 - val_hindlegL4: 0.0031 - val_hindlegR4: 0.0031 - val_eyeL: 0.0011 - val_eyeR: 9.7838e-04 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 18/200\n",
+      "200/200 - 3s - loss: 5.7844e-04 - head: 1.9896e-04 - thorax: 2.9112e-04 - abdomen: 4.7495e-04 - wingL: 4.5591e-04 - wingR: 4.5877e-04 - forelegL4: 0.0011 - forelegR4: 0.0012 - midlegL4: 6.9042e-04 - midlegR4: 6.6195e-04 - hindlegL4: 7.9452e-04 - hindlegR4: 7.6819e-04 - eyeL: 2.5989e-04 - eyeR: 2.4763e-04 - val_loss: 0.0018 - val_head: 3.1925e-04 - val_thorax: 6.0394e-04 - val_abdomen: 0.0012 - val_wingL: 9.0835e-04 - val_wingR: 0.0019 - val_forelegL4: 0.0022 - val_forelegR4: 0.0029 - val_midlegL4: 0.0026 - val_midlegR4: 0.0024 - val_hindlegL4: 0.0033 - val_hindlegR4: 0.0022 - val_eyeL: 0.0015 - val_eyeR: 0.0011 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 19/200\n",
+      "200/200 - 3s - loss: 5.1323e-04 - head: 1.8346e-04 - thorax: 2.5475e-04 - abdomen: 4.2159e-04 - wingL: 4.3027e-04 - wingR: 3.9814e-04 - forelegL4: 9.5814e-04 - forelegR4: 9.9765e-04 - midlegL4: 5.9968e-04 - midlegR4: 5.8423e-04 - hindlegL4: 6.7869e-04 - hindlegR4: 6.9121e-04 - eyeL: 2.4343e-04 - eyeR: 2.3077e-04 - val_loss: 0.0021 - val_head: 3.3346e-04 - val_thorax: 5.9007e-04 - val_abdomen: 0.0014 - val_wingL: 0.0013 - val_wingR: 0.0031 - val_forelegL4: 0.0026 - val_forelegR4: 0.0036 - val_midlegL4: 0.0029 - val_midlegR4: 0.0021 - val_hindlegL4: 0.0037 - val_hindlegR4: 0.0036 - val_eyeL: 0.0011 - val_eyeR: 9.4254e-04 - lr: 1.0000e-04 - 3s/epoch - 14ms/step\n",
+      "Epoch 20/200\n",
+      "200/200 - 3s - loss: 4.7991e-04 - head: 1.7328e-04 - thorax: 2.2397e-04 - abdomen: 4.2417e-04 - wingL: 3.9313e-04 - wingR: 3.9871e-04 - forelegL4: 8.8547e-04 - forelegR4: 8.9704e-04 - midlegL4: 5.3515e-04 - midlegR4: 5.8294e-04 - hindlegL4: 6.5212e-04 - hindlegR4: 6.2828e-04 - eyeL: 2.2438e-04 - eyeR: 2.2012e-04 - val_loss: 0.0014 - val_head: 2.7034e-04 - val_thorax: 4.7978e-04 - val_abdomen: 9.7903e-04 - val_wingL: 8.6477e-04 - val_wingR: 0.0020 - val_forelegL4: 0.0018 - val_forelegR4: 0.0024 - val_midlegL4: 0.0019 - val_midlegR4: 0.0018 - val_hindlegL4: 0.0024 - val_hindlegR4: 0.0022 - val_eyeL: 9.9423e-04 - val_eyeR: 8.4541e-04 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 21/200\n",
+      "200/200 - 3s - loss: 4.4100e-04 - head: 1.6076e-04 - thorax: 2.4080e-04 - abdomen: 3.8343e-04 - wingL: 3.6759e-04 - wingR: 3.7489e-04 - forelegL4: 8.1060e-04 - forelegR4: 8.1600e-04 - midlegL4: 4.7288e-04 - midlegR4: 5.2695e-04 - hindlegL4: 5.6401e-04 - hindlegR4: 6.3519e-04 - eyeL: 1.9033e-04 - eyeR: 1.8954e-04 - val_loss: 0.0018 - val_head: 2.5764e-04 - val_thorax: 5.8718e-04 - val_abdomen: 0.0011 - val_wingL: 9.6939e-04 - val_wingR: 0.0019 - val_forelegL4: 0.0022 - val_forelegR4: 0.0026 - val_midlegL4: 0.0025 - val_midlegR4: 0.0026 - val_hindlegL4: 0.0032 - val_hindlegR4: 0.0028 - val_eyeL: 0.0014 - val_eyeR: 0.0011 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 22/200\n",
+      "200/200 - 3s - loss: 3.7738e-04 - head: 1.4725e-04 - thorax: 2.0905e-04 - abdomen: 3.2447e-04 - wingL: 3.2224e-04 - wingR: 3.0585e-04 - forelegL4: 6.2169e-04 - forelegR4: 6.7379e-04 - midlegL4: 4.5061e-04 - midlegR4: 4.3931e-04 - hindlegL4: 5.1129e-04 - hindlegR4: 5.2449e-04 - eyeL: 1.9372e-04 - eyeR: 1.8213e-04 - val_loss: 0.0015 - val_head: 2.2947e-04 - val_thorax: 5.4640e-04 - val_abdomen: 9.8293e-04 - val_wingL: 8.6663e-04 - val_wingR: 0.0013 - val_forelegL4: 0.0018 - val_forelegR4: 0.0027 - val_midlegL4: 0.0021 - val_midlegR4: 0.0019 - val_hindlegL4: 0.0027 - val_hindlegR4: 0.0022 - val_eyeL: 0.0013 - val_eyeR: 0.0010 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 23/200\n",
+      "200/200 - 3s - loss: 3.6084e-04 - head: 1.4440e-04 - thorax: 2.0277e-04 - abdomen: 3.0561e-04 - wingL: 3.0192e-04 - wingR: 2.8845e-04 - forelegL4: 6.3221e-04 - forelegR4: 6.7722e-04 - midlegL4: 3.9143e-04 - midlegR4: 4.3545e-04 - hindlegL4: 5.1985e-04 - hindlegR4: 4.5058e-04 - eyeL: 1.7636e-04 - eyeR: 1.6468e-04 - val_loss: 0.0015 - val_head: 2.9639e-04 - val_thorax: 4.6412e-04 - val_abdomen: 0.0011 - val_wingL: 9.0466e-04 - val_wingR: 0.0021 - val_forelegL4: 0.0015 - val_forelegR4: 0.0025 - val_midlegL4: 0.0018 - val_midlegR4: 0.0016 - val_hindlegL4: 0.0029 - val_hindlegR4: 0.0022 - val_eyeL: 8.7357e-04 - val_eyeR: 7.0067e-04 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 24/200\n",
+      "200/200 - 3s - loss: 3.4886e-04 - head: 1.4382e-04 - thorax: 1.9157e-04 - abdomen: 3.2551e-04 - wingL: 3.0634e-04 - wingR: 3.0727e-04 - forelegL4: 6.3863e-04 - forelegR4: 6.0904e-04 - midlegL4: 3.5949e-04 - midlegR4: 4.1201e-04 - hindlegL4: 4.2893e-04 - hindlegR4: 4.8121e-04 - eyeL: 1.6669e-04 - eyeR: 1.6464e-04 - val_loss: 0.0022 - val_head: 3.2159e-04 - val_thorax: 7.2743e-04 - val_abdomen: 0.0014 - val_wingL: 0.0011 - val_wingR: 0.0027 - val_forelegL4: 0.0025 - val_forelegR4: 0.0037 - val_midlegL4: 0.0033 - val_midlegR4: 0.0020 - val_hindlegL4: 0.0043 - val_hindlegR4: 0.0031 - val_eyeL: 0.0017 - val_eyeR: 0.0012 - lr: 1.0000e-04 - 3s/epoch - 14ms/step\n",
+      "Epoch 25/200\n",
+      "\n",
+      "Epoch 00025: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n",
+      "200/200 - 3s - loss: 3.0444e-04 - head: 1.2563e-04 - thorax: 1.7247e-04 - abdomen: 2.6934e-04 - wingL: 2.5754e-04 - wingR: 2.4728e-04 - forelegL4: 5.8390e-04 - forelegR4: 5.3959e-04 - midlegL4: 3.3003e-04 - midlegR4: 3.6432e-04 - hindlegL4: 4.0270e-04 - hindlegR4: 3.5518e-04 - eyeL: 1.5609e-04 - eyeR: 1.5365e-04 - val_loss: 0.0017 - val_head: 2.5420e-04 - val_thorax: 5.5809e-04 - val_abdomen: 0.0011 - val_wingL: 9.6708e-04 - val_wingR: 0.0022 - val_forelegL4: 0.0018 - val_forelegR4: 0.0033 - val_midlegL4: 0.0025 - val_midlegR4: 0.0017 - val_hindlegL4: 0.0031 - val_hindlegR4: 0.0031 - val_eyeL: 9.8718e-04 - val_eyeR: 8.0263e-04 - lr: 1.0000e-04 - 3s/epoch - 15ms/step\n",
+      "Epoch 26/200\n",
+      "200/200 - 3s - loss: 2.3368e-04 - head: 1.1149e-04 - thorax: 1.5177e-04 - abdomen: 2.1763e-04 - wingL: 2.2159e-04 - wingR: 1.9396e-04 - forelegL4: 3.8234e-04 - forelegR4: 3.8248e-04 - midlegL4: 2.7555e-04 - midlegR4: 2.8653e-04 - hindlegL4: 2.7842e-04 - hindlegR4: 2.8074e-04 - eyeL: 1.3157e-04 - eyeR: 1.2374e-04 - val_loss: 0.0017 - val_head: 2.1815e-04 - val_thorax: 5.0063e-04 - val_abdomen: 0.0011 - val_wingL: 8.2248e-04 - val_wingR: 0.0020 - val_forelegL4: 0.0019 - val_forelegR4: 0.0035 - val_midlegL4: 0.0022 - val_midlegR4: 0.0016 - val_hindlegL4: 0.0031 - val_hindlegR4: 0.0022 - val_eyeL: 0.0013 - val_eyeR: 9.8071e-04 - lr: 5.0000e-05 - 3s/epoch - 14ms/step\n",
+      "Epoch 27/200\n",
+      "200/200 - 3s - loss: 2.0711e-04 - head: 9.7513e-05 - thorax: 1.4018e-04 - abdomen: 2.0210e-04 - wingL: 1.8693e-04 - wingR: 1.7399e-04 - forelegL4: 3.1753e-04 - forelegR4: 3.7613e-04 - midlegL4: 2.2838e-04 - midlegR4: 2.4643e-04 - hindlegL4: 2.4471e-04 - hindlegR4: 2.4706e-04 - eyeL: 1.1696e-04 - eyeR: 1.1452e-04 - val_loss: 0.0011 - val_head: 1.7855e-04 - val_thorax: 3.7885e-04 - val_abdomen: 7.0074e-04 - val_wingL: 6.4821e-04 - val_wingR: 0.0012 - val_forelegL4: 0.0012 - val_forelegR4: 0.0017 - val_midlegL4: 0.0014 - val_midlegR4: 0.0013 - val_hindlegL4: 0.0019 - val_hindlegR4: 0.0018 - val_eyeL: 8.8941e-04 - val_eyeR: 7.0606e-04 - lr: 5.0000e-05 - 3s/epoch - 15ms/step\n",
+      "Epoch 28/200\n",
+      "200/200 - 3s - loss: 1.9539e-04 - head: 9.4716e-05 - thorax: 1.3617e-04 - abdomen: 1.8547e-04 - wingL: 1.8173e-04 - wingR: 1.6716e-04 - forelegL4: 3.2783e-04 - forelegR4: 3.1060e-04 - midlegL4: 2.2172e-04 - midlegR4: 2.2648e-04 - hindlegL4: 2.3846e-04 - hindlegR4: 2.2823e-04 - eyeL: 1.1204e-04 - eyeR: 1.0944e-04 - val_loss: 0.0012 - val_head: 1.9505e-04 - val_thorax: 3.8105e-04 - val_abdomen: 7.7888e-04 - val_wingL: 6.8985e-04 - val_wingR: 0.0016 - val_forelegL4: 0.0015 - val_forelegR4: 0.0020 - val_midlegL4: 0.0017 - val_midlegR4: 0.0011 - val_hindlegL4: 0.0022 - val_hindlegR4: 0.0019 - val_eyeL: 9.1223e-04 - val_eyeR: 7.0778e-04 - lr: 5.0000e-05 - 3s/epoch - 15ms/step\n",
+      "Epoch 29/200\n",
+      "200/200 - 3s - loss: 1.8262e-04 - head: 9.2364e-05 - thorax: 1.3126e-04 - abdomen: 1.7625e-04 - wingL: 1.7494e-04 - wingR: 1.5998e-04 - forelegL4: 3.0159e-04 - forelegR4: 2.9470e-04 - midlegL4: 1.9773e-04 - midlegR4: 2.0446e-04 - hindlegL4: 2.0576e-04 - hindlegR4: 2.1560e-04 - eyeL: 1.1218e-04 - eyeR: 1.0720e-04 - val_loss: 0.0015 - val_head: 2.2535e-04 - val_thorax: 4.8031e-04 - val_abdomen: 9.5428e-04 - val_wingL: 7.7468e-04 - val_wingR: 0.0016 - val_forelegL4: 0.0017 - val_forelegR4: 0.0025 - val_midlegL4: 0.0021 - val_midlegR4: 0.0018 - val_hindlegL4: 0.0029 - val_hindlegR4: 0.0019 - val_eyeL: 0.0013 - val_eyeR: 9.6936e-04 - lr: 5.0000e-05 - 3s/epoch - 15ms/step\n",
+      "Epoch 30/200\n",
+      "200/200 - 3s - loss: 1.7461e-04 - head: 8.9617e-05 - thorax: 1.2428e-04 - abdomen: 1.7234e-04 - wingL: 1.6780e-04 - wingR: 1.5580e-04 - forelegL4: 2.7324e-04 - forelegR4: 2.8042e-04 - midlegL4: 1.9090e-04 - midlegR4: 2.0420e-04 - hindlegL4: 1.9914e-04 - hindlegR4: 2.0318e-04 - eyeL: 1.0518e-04 - eyeR: 1.0386e-04 - val_loss: 0.0015 - val_head: 1.9058e-04 - val_thorax: 4.9603e-04 - val_abdomen: 0.0011 - val_wingL: 9.7566e-04 - val_wingR: 0.0018 - val_forelegL4: 0.0016 - val_forelegR4: 0.0028 - val_midlegL4: 0.0022 - val_midlegR4: 0.0015 - val_hindlegL4: 0.0028 - val_hindlegR4: 0.0028 - val_eyeL: 9.9699e-04 - val_eyeR: 8.3721e-04 - lr: 5.0000e-05 - 3s/epoch - 15ms/step\n",
+      "Epoch 31/200\n",
+      "200/200 - 3s - loss: 1.7064e-04 - head: 8.7373e-05 - thorax: 1.2365e-04 - abdomen: 1.6765e-04 - wingL: 1.5656e-04 - wingR: 1.4505e-04 - forelegL4: 2.7352e-04 - forelegR4: 2.6274e-04 - midlegL4: 1.9639e-04 - midlegR4: 1.9628e-04 - hindlegL4: 2.0323e-04 - hindlegR4: 1.9917e-04 - eyeL: 1.0639e-04 - eyeR: 1.0032e-04 - val_loss: 0.0011 - val_head: 1.7938e-04 - val_thorax: 3.6727e-04 - val_abdomen: 7.7820e-04 - val_wingL: 6.4437e-04 - val_wingR: 0.0014 - val_forelegL4: 0.0014 - val_forelegR4: 0.0020 - val_midlegL4: 0.0016 - val_midlegR4: 0.0010 - val_hindlegL4: 0.0021 - val_hindlegR4: 0.0016 - val_eyeL: 8.0607e-04 - val_eyeR: 6.6172e-04 - lr: 5.0000e-05 - 3s/epoch - 16ms/step\n",
+      "Epoch 32/200\n",
+      "\n",
+      "Epoch 00032: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n",
+      "200/200 - 4s - loss: 1.6547e-04 - head: 8.6407e-05 - thorax: 1.1578e-04 - abdomen: 1.6160e-04 - wingL: 1.5752e-04 - wingR: 1.4326e-04 - forelegL4: 2.5855e-04 - forelegR4: 2.8317e-04 - midlegL4: 1.7880e-04 - midlegR4: 1.8021e-04 - hindlegL4: 1.9743e-04 - hindlegR4: 1.8831e-04 - eyeL: 1.0074e-04 - eyeR: 9.9381e-05 - val_loss: 0.0012 - val_head: 1.9257e-04 - val_thorax: 3.7361e-04 - val_abdomen: 7.0451e-04 - val_wingL: 7.8240e-04 - val_wingR: 0.0015 - val_forelegL4: 0.0014 - val_forelegR4: 0.0020 - val_midlegL4: 0.0016 - val_midlegR4: 0.0011 - val_hindlegL4: 0.0020 - val_hindlegR4: 0.0019 - val_eyeL: 8.9328e-04 - val_eyeR: 7.3886e-04 - lr: 5.0000e-05 - 4s/epoch - 18ms/step\n",
+      "Epoch 33/200\n",
+      "200/200 - 3s - loss: 1.4767e-04 - head: 8.0575e-05 - thorax: 1.1097e-04 - abdomen: 1.4927e-04 - wingL: 1.4112e-04 - wingR: 1.3113e-04 - forelegL4: 2.1913e-04 - forelegR4: 2.1998e-04 - midlegL4: 1.6045e-04 - midlegR4: 1.6535e-04 - hindlegL4: 1.8091e-04 - hindlegR4: 1.7343e-04 - eyeL: 9.5387e-05 - eyeR: 9.2035e-05 - val_loss: 0.0014 - val_head: 1.9046e-04 - val_thorax: 4.6921e-04 - val_abdomen: 9.4087e-04 - val_wingL: 7.5647e-04 - val_wingR: 0.0015 - val_forelegL4: 0.0015 - val_forelegR4: 0.0025 - val_midlegL4: 0.0020 - val_midlegR4: 0.0015 - val_hindlegL4: 0.0026 - val_hindlegR4: 0.0021 - val_eyeL: 0.0013 - val_eyeR: 0.0010 - lr: 2.5000e-05 - 3s/epoch - 16ms/step\n",
+      "Epoch 34/200\n",
+      "200/200 - 3s - loss: 1.4506e-04 - head: 7.9790e-05 - thorax: 1.0771e-04 - abdomen: 1.5052e-04 - wingL: 1.4143e-04 - wingR: 1.2485e-04 - forelegL4: 2.2486e-04 - forelegR4: 2.1619e-04 - midlegL4: 1.6584e-04 - midlegR4: 1.6250e-04 - hindlegL4: 1.6521e-04 - hindlegR4: 1.6717e-04 - eyeL: 9.1550e-05 - eyeR: 8.8112e-05 - val_loss: 0.0013 - val_head: 1.8689e-04 - val_thorax: 3.7203e-04 - val_abdomen: 9.3770e-04 - val_wingL: 7.0190e-04 - val_wingR: 0.0019 - val_forelegL4: 0.0015 - val_forelegR4: 0.0023 - val_midlegL4: 0.0016 - val_midlegR4: 0.0012 - val_hindlegL4: 0.0025 - val_hindlegR4: 0.0022 - val_eyeL: 8.0213e-04 - val_eyeR: 6.5036e-04 - lr: 2.5000e-05 - 3s/epoch - 15ms/step\n",
+      "Epoch 35/200\n",
+      "200/200 - 3s - loss: 1.3911e-04 - head: 7.9674e-05 - thorax: 1.0668e-04 - abdomen: 1.4330e-04 - wingL: 1.3906e-04 - wingR: 1.2752e-04 - forelegL4: 1.9657e-04 - forelegR4: 1.9577e-04 - midlegL4: 1.5228e-04 - midlegR4: 1.5642e-04 - hindlegL4: 1.6610e-04 - hindlegR4: 1.6394e-04 - eyeL: 9.1523e-05 - eyeR: 8.9620e-05 - val_loss: 0.0013 - val_head: 1.7511e-04 - val_thorax: 4.2162e-04 - val_abdomen: 9.5009e-04 - val_wingL: 6.7908e-04 - val_wingR: 0.0013 - val_forelegL4: 0.0015 - val_forelegR4: 0.0023 - val_midlegL4: 0.0018 - val_midlegR4: 0.0014 - val_hindlegL4: 0.0027 - val_hindlegR4: 0.0019 - val_eyeL: 0.0012 - val_eyeR: 9.8818e-04 - lr: 2.5000e-05 - 3s/epoch - 16ms/step\n",
+      "Epoch 36/200\n",
+      "200/200 - 3s - loss: 1.3697e-04 - head: 7.5207e-05 - thorax: 1.0507e-04 - abdomen: 1.3913e-04 - wingL: 1.3497e-04 - wingR: 1.2511e-04 - forelegL4: 1.9152e-04 - forelegR4: 2.0264e-04 - midlegL4: 1.5207e-04 - midlegR4: 1.5519e-04 - hindlegL4: 1.6368e-04 - hindlegR4: 1.5869e-04 - eyeL: 9.0233e-05 - eyeR: 8.7055e-05 - val_loss: 0.0013 - val_head: 1.8066e-04 - val_thorax: 4.6591e-04 - val_abdomen: 9.9582e-04 - val_wingL: 7.2600e-04 - val_wingR: 0.0012 - val_forelegL4: 0.0015 - val_forelegR4: 0.0022 - val_midlegL4: 0.0019 - val_midlegR4: 0.0015 - val_hindlegL4: 0.0028 - val_hindlegR4: 0.0018 - val_eyeL: 0.0012 - val_eyeR: 9.6224e-04 - lr: 2.5000e-05 - 3s/epoch - 15ms/step\n",
+      "Epoch 37/200\n",
+      "200/200 - 3s - loss: 1.3638e-04 - head: 7.6822e-05 - thorax: 1.0531e-04 - abdomen: 1.4107e-04 - wingL: 1.4047e-04 - wingR: 1.2177e-04 - forelegL4: 1.9564e-04 - forelegR4: 1.7970e-04 - midlegL4: 1.5364e-04 - midlegR4: 1.5089e-04 - hindlegL4: 1.6647e-04 - hindlegR4: 1.6322e-04 - eyeL: 9.0198e-05 - eyeR: 8.7722e-05 - val_loss: 0.0017 - val_head: 2.3218e-04 - val_thorax: 5.3881e-04 - val_abdomen: 0.0011 - val_wingL: 0.0010 - val_wingR: 0.0019 - val_forelegL4: 0.0021 - val_forelegR4: 0.0028 - val_midlegL4: 0.0025 - val_midlegR4: 0.0016 - val_hindlegL4: 0.0033 - val_hindlegR4: 0.0029 - val_eyeL: 0.0015 - val_eyeR: 0.0012 - lr: 2.5000e-05 - 3s/epoch - 16ms/step\n",
+      "Epoch 00037: early stopping\n",
+      "INFO:sleap.nn.training:Finished training loop. [2.0 min]\n",
+      "INFO:sleap.nn.training:Deleting visualization directory: models/courtship.topdown_confmaps/viz\n",
+      "INFO:sleap.nn.training:Saving evaluation metrics to model folder...\n",
+      "\u001b[2KPredicting... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m ETA: \u001b[36m0:00:00\u001b[0m \u001b[31m39.3 FPS\u001b[0m31m48.8 FPS\u001b[0m31m49.5 FPS\u001b[0mFPS\u001b[0m\n",
+      "\u001b[?25hINFO:sleap.nn.evals:Saved predictions: models/courtship.topdown_confmaps/labels_pr.train.slp\n",
+      "INFO:sleap.nn.evals:Saved metrics: models/courtship.topdown_confmaps/metrics.train.npz\n",
+      "INFO:sleap.nn.evals:OKS mAP: 0.899237\n",
+      "\u001b[2KPredicting... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m ETA: \u001b[36m0:00:00\u001b[0m \u001b[31m14.2 FPS\u001b[0m0:00:01\u001b[0m \u001b[31m270.2 FPS\u001b[0mm\n",
+      "\u001b[?25hINFO:sleap.nn.evals:Saved predictions: models/courtship.topdown_confmaps/labels_pr.val.slp\n",
+      "INFO:sleap.nn.evals:Saved metrics: models/courtship.topdown_confmaps/metrics.val.npz\n",
+      "INFO:sleap.nn.evals:OKS mAP: 0.691378\n"
+     ]
+    }
+   ],
    "source": [
     "!sleap-train baseline_medium_rf.topdown.json \"dataset/drosophila-melanogaster-courtship/courtship_labels.slp\" --run_name \"courtship.topdown_confmaps\" --video-paths \"dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\""
    ]
@@ -145,7 +922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -159,23 +936,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "models/\n",
-      "├── courtship.centroid\n",
+      "\u001b[01;34mmodels/\u001b[00m\n",
+      "├── \u001b[01;34mcourtship.centroid\u001b[00m\n",
       "│   ├── best_model.h5\n",
       "│   ├── initial_config.json\n",
       "│   ├── labels_gt.train.slp\n",
       "│   ├── labels_gt.val.slp\n",
+      "│   ├── labels_pr.train.slp\n",
+      "│   ├── labels_pr.val.slp\n",
+      "│   ├── metrics.train.npz\n",
+      "│   ├── metrics.val.npz\n",
       "│   ├── training_config.json\n",
       "│   └── training_log.csv\n",
-      "└── courtship.topdown_confmaps\n",
+      "└── \u001b[01;34mcourtship.topdown_confmaps\u001b[00m\n",
       "    ├── best_model.h5\n",
       "    ├── initial_config.json\n",
       "    ├── labels_gt.train.slp\n",
       "    ├── labels_gt.val.slp\n",
+      "    ├── labels_pr.train.slp\n",
+      "    ├── labels_pr.val.slp\n",
+      "    ├── metrics.train.npz\n",
+      "    ├── metrics.val.npz\n",
       "    ├── training_config.json\n",
       "    └── training_log.csv\n",
       "\n",
-      "2 directories, 12 files\n"
+      "2 directories, 20 files\n"
      ]
     }
    ],
@@ -195,11 +980,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {
     "id": "CLtjtq9E1Znr"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started inference at: 2023-09-01 13:42:03.066840\n",
+      "Args:\n",
+      "\u001b[1m{\u001b[0m\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'data_path'\u001b[0m: \u001b[32m'dataset/drosophila-melanogaster-courtship/20190128_113421.mp4'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'models'\u001b[0m: \u001b[1m[\u001b[0m\n",
+      "\u001b[2;32m│   │   \u001b[0m\u001b[32m'models/courtship.centroid'\u001b[0m,\n",
+      "\u001b[2;32m│   │   \u001b[0m\u001b[32m'models/courtship.topdown_confmaps'\u001b[0m\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[1m]\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'frames'\u001b[0m: \u001b[32m'0-100'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'only_labeled_frames'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'only_suggested_frames'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'no_empty_frames'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'verbosity'\u001b[0m: \u001b[32m'rich'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'video.dataset'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'video.input_format'\u001b[0m: \u001b[32m'channels_last'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'video.index'\u001b[0m: \u001b[32m''\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'cpu'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'first_gpu'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'last_gpu'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'gpu'\u001b[0m: \u001b[32m'auto'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'max_edge_length_ratio'\u001b[0m: \u001b[1;36m0.25\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'dist_penalty_weight'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'batch_size'\u001b[0m: \u001b[1;36m4\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'open_in_gui'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'peak_threshold'\u001b[0m: \u001b[1;36m0.2\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'max_instances'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.tracker'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.target_instance_count'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.pre_cull_to_target'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.pre_cull_iou_threshold'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.post_connect_single_breaks'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.clean_instance_count'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.clean_iou_threshold'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.similarity'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.match'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.robust'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.track_window'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.min_new_track_points'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.min_match_points'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.img_scale'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.of_window_size'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.of_max_levels'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.save_shifted_instances'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.kf_node_indices'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'tracking.kf_init_frame_count'\u001b[0m: \u001b[3;35mNone\u001b[0m\n",
+      "\u001b[1m}\u001b[0m\n",
+      "\n",
+      "2023-09-01 13:42:03.098811: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:42:03.103255: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:42:03.103982: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "INFO:sleap.nn.inference:Auto-selected GPU 0 with 23050 MiB of free memory.\n",
+      "Versions:\n",
+      "SLEAP: 1.3.2\n",
+      "TensorFlow: 2.7.0\n",
+      "Numpy: 1.21.5\n",
+      "Python: 3.7.12\n",
+      "OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n",
+      "\n",
+      "System:\n",
+      "GPUs: 1/1 available\n",
+      "  Device: /physical_device:GPU:0\n",
+      "         Available: True\n",
+      "        Initalized: False\n",
+      "     Memory growth: True\n",
+      "\n",
+      "Video: dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\n",
+      "2023-09-01 13:42:03.157392: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-09-01 13:42:03.158019: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:42:03.158864: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:42:03.159656: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:42:03.455402: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:42:03.456138: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:42:03.456803: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-09-01 13:42:03.457464: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21145 MB memory:  -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
+      "\u001b[2KPredicting... \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  0%\u001b[0m ETA: \u001b[36m-:--:--\u001b[0m \u001b[31m?\u001b[0m2023-09-01 13:42:07.038687: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201\n",
+      "\u001b[2KPredicting... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m ETA: \u001b[36m0:00:00\u001b[0m \u001b[31m51.9 FPS\u001b[0m[0m \u001b[31m126.4 FPS\u001b[0m FPS\u001b[0mFPS\u001b[0m\n",
+      "\u001b[?25hFinished inference at: 2023-09-01 13:42:10.842469\n",
+      "Total runtime: 7.775644779205322 secs\n",
+      "Predicted frames: 101/101\n",
+      "Provenance:\n",
+      "\u001b[1m{\u001b[0m\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'model_paths'\u001b[0m: \u001b[1m[\u001b[0m\n",
+      "\u001b[2;32m│   │   \u001b[0m\u001b[32m'models/courtship.centroid/training_config.json'\u001b[0m,\n",
+      "\u001b[2;32m│   │   \u001b[0m\u001b[32m'models/courtship.topdown_confmaps/training_config.json'\u001b[0m\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[1m]\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'predictor'\u001b[0m: \u001b[32m'TopDownPredictor'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'sleap_version'\u001b[0m: \u001b[32m'1.3.2'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'platform'\u001b[0m: \u001b[32m'Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'command'\u001b[0m: \u001b[32m'/home/talmolab/micromamba/envs/s0/bin/sleap-track dataset/drosophila-melanogaster-courtship/20190128_113421.mp4 --frames 0-100 -m models/courtship.centroid -m models/courtship.topdown_confmaps'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'data_path'\u001b[0m: \u001b[32m'dataset/drosophila-melanogaster-courtship/20190128_113421.mp4'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'output_path'\u001b[0m: \u001b[32m'dataset/drosophila-melanogaster-courtship/20190128_113421.mp4.predictions.slp'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'total_elapsed'\u001b[0m: \u001b[1;36m7.775644779205322\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'start_timestamp'\u001b[0m: \u001b[32m'2023-09-01 13:42:03.066840'\u001b[0m,\n",
+      "\u001b[2;32m│   \u001b[0m\u001b[32m'finish_timestamp'\u001b[0m: \u001b[32m'2023-09-01 13:42:10.842469'\u001b[0m\n",
+      "\u001b[1m}\u001b[0m\n",
+      "\n",
+      "Saved output: dataset/drosophila-melanogaster-courtship/20190128_113421.mp4.predictions.slp\n"
+     ]
+    }
+   ],
    "source": [
     "!sleap-track \"dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\" --frames 0-100 -m \"models/courtship.centroid\" -m \"models/courtship.topdown_confmaps\""
    ]
@@ -215,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -229,11 +1120,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dataset/drosophila-melanogaster-courtship\n",
-      "├── 20190128_113421.mp4\n",
+      "\u001b[01;34mdataset/drosophila-melanogaster-courtship\u001b[00m\n",
+      "├── \u001b[01;32m20190128_113421.mp4\u001b[00m\n",
       "├── 20190128_113421.mp4.predictions.slp\n",
-      "├── courtship_labels.slp\n",
-      "└── example.jpg\n",
+      "├── \u001b[01;32mcourtship_labels.slp\u001b[00m\n",
+      "└── \u001b[01;35mexample.jpg\u001b[00m\n",
       "\n",
       "0 directories, 4 files\n"
      ]
@@ -254,11 +1145,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {
     "id": "-jbVP_s06hMh"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Labeled frames: 101\n",
+      "Tracks: 0\n",
+      "Video files:\n",
+      "  dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\n",
+      "    labeled frames: 101\n",
+      "    labeled frames from 0 to 100\n",
+      "    user labeled frames: 0\n",
+      "    tracks: 1\n",
+      "    max instances in frame: 2\n",
+      "Total user labeled frames: 0\n",
+      "\n",
+      "Provenance:\n",
+      "  model_paths: ['models/courtship.centroid/training_config.json', 'models/courtship.topdown_confmaps/training_config.json']\n",
+      "  predictor: TopDownPredictor\n",
+      "  sleap_version: 1.3.2\n",
+      "  platform: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid\n",
+      "  command: /home/talmolab/micromamba/envs/s0/bin/sleap-track dataset/drosophila-melanogaster-courtship/20190128_113421.mp4 --frames 0-100 -m models/courtship.centroid -m models/courtship.topdown_confmaps\n",
+      "  data_path: dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\n",
+      "  output_path: dataset/drosophila-melanogaster-courtship/20190128_113421.mp4.predictions.slp\n",
+      "  total_elapsed: 7.775644779205322\n",
+      "  start_timestamp: 2023-09-01 13:42:03.066840\n",
+      "  finish_timestamp: 2023-09-01 13:42:10.842469\n",
+      "  args: {'data_path': 'dataset/drosophila-melanogaster-courtship/20190128_113421.mp4', 'models': ['models/courtship.centroid', 'models/courtship.topdown_confmaps'], 'frames': '0-100', 'only_labeled_frames': False, 'only_suggested_frames': False, 'output': None, 'no_empty_frames': False, 'verbosity': 'rich', 'video.dataset': None, 'video.input_format': 'channels_last', 'video.index': '', 'cpu': False, 'first_gpu': False, 'last_gpu': False, 'gpu': 'auto', 'max_edge_length_ratio': 0.25, 'dist_penalty_weight': 1.0, 'batch_size': 4, 'open_in_gui': False, 'peak_threshold': 0.2, 'max_instances': None, 'tracking.tracker': None, 'tracking.target_instance_count': None, 'tracking.pre_cull_to_target': None, 'tracking.pre_cull_iou_threshold': None, 'tracking.post_connect_single_breaks': None, 'tracking.clean_instance_count': None, 'tracking.clean_iou_threshold': None, 'tracking.similarity': None, 'tracking.match': None, 'tracking.robust': None, 'tracking.track_window': None, 'tracking.min_new_track_points': None, 'tracking.min_match_points': None, 'tracking.img_scale': None, 'tracking.of_window_size': None, 'tracking.of_max_levels': None, 'tracking.save_shifted_instances': None, 'tracking.kf_node_indices': None, 'tracking.kf_init_frame_count': None}\n"
+     ]
+    }
+   ],
    "source": [
     "!sleap-inspect dataset/drosophila-melanogaster-courtship/20190128_113421.mp4.predictions.slp"
    ]
@@ -274,11 +1195,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {
     "id": "Ej2it8dl_BO_"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  adding: models/ (stored 0%)\n",
+      "  adding: models/courtship.topdown_confmaps/ (stored 0%)\n",
+      "  adding: models/courtship.topdown_confmaps/labels_pr.val.slp (deflated 74%)\n",
+      "  adding: models/courtship.topdown_confmaps/metrics.val.npz (deflated 0%)\n",
+      "  adding: models/courtship.topdown_confmaps/labels_pr.train.slp (deflated 67%)\n",
+      "  adding: models/courtship.topdown_confmaps/labels_gt.val.slp (deflated 72%)\n",
+      "  adding: models/courtship.topdown_confmaps/initial_config.json (deflated 73%)\n",
+      "  adding: models/courtship.topdown_confmaps/training_log.csv (deflated 55%)\n",
+      "  adding: models/courtship.topdown_confmaps/metrics.train.npz (deflated 0%)\n",
+      "  adding: models/courtship.topdown_confmaps/labels_gt.train.slp (deflated 61%)\n",
+      "  adding: models/courtship.topdown_confmaps/best_model.h5 (deflated 8%)\n",
+      "  adding: models/courtship.topdown_confmaps/training_config.json (deflated 88%)\n",
+      "  adding: models/courtship.centroid/ (stored 0%)\n",
+      "  adding: models/courtship.centroid/labels_pr.val.slp (deflated 82%)\n",
+      "  adding: models/courtship.centroid/metrics.val.npz (deflated 1%)\n",
+      "  adding: models/courtship.centroid/labels_pr.train.slp (deflated 79%)\n",
+      "  adding: models/courtship.centroid/labels_gt.val.slp (deflated 73%)\n",
+      "  adding: models/courtship.centroid/initial_config.json (deflated 74%)\n",
+      "  adding: models/courtship.centroid/training_log.csv (deflated 57%)\n",
+      "  adding: models/courtship.centroid/metrics.train.npz (deflated 0%)\n",
+      "  adding: models/courtship.centroid/labels_gt.train.slp (deflated 61%)\n",
+      "  adding: models/courtship.centroid/best_model.h5 (deflated 7%)\n",
+      "  adding: models/courtship.centroid/training_config.json (deflated 88%)\n"
+     ]
+    }
+   ],
    "source": [
     "# Zip up the models directory\n",
     "!zip -r trained_models.zip models/\n",
@@ -299,7 +1250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {
     "id": "gdXCYnRV_omC"
    },
@@ -343,7 +1294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.12"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/Training_and_inference_using_Google_Drive.ipynb b/docs/notebooks/Training_and_inference_using_Google_Drive.ipynb
index 26e836a32..0a3fc505b 100644
--- a/docs/notebooks/Training_and_inference_using_Google_Drive.ipynb
+++ b/docs/notebooks/Training_and_inference_using_Google_Drive.ipynb
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -46,10 +46,20 @@
     "id": "DUfnkxMtLcK3",
     "outputId": "988097ae-e996-4b81-eb06-ec85aa0b2d9d"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mERROR: Cannot uninstall opencv-python 4.6.0, RECORD file not found. Hint: The package was installed by conda.\u001b[0m\u001b[31m\n",
+      "\u001b[0m\u001b[31mERROR: Cannot uninstall shiboken2 5.15.6, RECORD file not found. You might be able to recover from this via: 'pip install --force-reinstall --no-deps shiboken2==5.15.6'.\u001b[0m\u001b[31m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
    "source": [
-    "!pip uninstall -y opencv-python opencv-contrib-python\n",
-    "!pip install sleap"
+    "!pip uninstall -qqq -y opencv-python opencv-contrib-python\n",
+    "!pip install -qqq sleap[pypi]"
    ]
   },
   {
@@ -356,7 +366,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.12"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/utils.py b/docs/utils.py
index 2d5bf1969..141189601 100644
--- a/docs/utils.py
+++ b/docs/utils.py
@@ -23,7 +23,7 @@ def find_source_file(obj, root_obj):
     # Get relative filename
     fn = os.path.relpath(
         inspect.getsourcefile(obj),
-        start=os.path.dirname(os.path.dirname(root_obj.__file__))
+        start=os.path.dirname(os.path.dirname(root_obj.__file__)),
     ).replace("\\", "/")
     return fn
 
@@ -32,7 +32,7 @@ def find_source_lines(obj):
     # Find line numbers
     source_code, from_line = inspect.getsourcelines(obj)
     to_line = from_line + len(source_code) - 1
-    
+
     return from_line, to_line
 
 
@@ -40,14 +40,14 @@ def resolve(module, fullname):
     if fullname == "":
         # Submodule specified, just infer path from the module name.
         return module.replace(".", "/") + ".py"
-    
+
     # Search for member within module.
     member = find_member(sys.modules[module], fullname)
-    
+
     if member is None:
         # Member not found, so we won't be linking this.
         return None
-    
+
     try:
         fn = find_source_file(member, sleap)
     except TypeError:
@@ -56,4 +56,3 @@ def resolve(module, fullname):
     from_line, to_line = find_source_lines(member)
 
     return f"{fn}#L{from_line}-L{to_line}"
-
diff --git a/environment.yml b/environment.yml
index 13cece2df..9f9ff903d 100644
--- a/environment.yml
+++ b/environment.yml
@@ -36,7 +36,7 @@ dependencies:
     - conda-forge::scikit-video
     - conda-forge::seaborn
     - sleap::tensorflow >=2.6.3,<2.11  # No windows GPU support for >2.10
-    - conda-forge::tensorflow-hub
+    - conda-forge::tensorflow-hub  # Pinned in meta.yml, but no problems here... yet
 
     # Packages required by tensorflow to find/use GPUs
     - conda-forge::cudatoolkit ==11.3.1
@@ -45,5 +45,4 @@ dependencies:
     - nvidia::cuda-nvcc=11.3
 
     - pip:  
-        - "--editable=."
-        - "--requirement=./dev_requirements.txt"
+        - "--editable=.[conda_dev]"
diff --git a/environment_mac.yml b/environment_mac.yml
index 611715963..85ef7d3b9 100644
--- a/environment_mac.yml
+++ b/environment_mac.yml
@@ -37,5 +37,4 @@ dependencies:
     - conda-forge::seaborn
     - conda-forge::tensorflow-hub
     - pip:
-        - "--editable=./"
-        - "--requirement=./dev_requirements.txt"
+        - "--editable=.[conda_dev]"
diff --git a/environment_no_cuda.yml b/environment_no_cuda.yml
index b3b3bdc08..7e384b5f9 100644
--- a/environment_no_cuda.yml
+++ b/environment_no_cuda.yml
@@ -40,5 +40,4 @@ dependencies:
     - conda-forge::tensorflow-hub
 
     - pip:  
-        - "--editable=."
-        - "--requirement=./dev_requirements.txt"
+        - "--editable=.[conda_dev]"
diff --git a/jupyter_requirements.txt b/jupyter_requirements.txt
new file mode 100644
index 000000000..545f141a4
--- /dev/null
+++ b/jupyter_requirements.txt
@@ -0,0 +1,5 @@
+# This file contains the dependencies to be installed for jupyter lab support. 
+
+ipykernel
+ipywidgets
+jupyterlab
\ No newline at end of file
diff --git a/pip_requirements.txt b/pypi_requirements.txt
similarity index 95%
rename from pip_requirements.txt
rename to pypi_requirements.txt
index 1e6007118..b18637c37 100644
--- a/pip_requirements.txt
+++ b/pypi_requirements.txt
@@ -1,7 +1,7 @@
 # This file contains the full list of dependencies to be installed when only using pypi.
 # This file should look very similar to the environment.yml file. Based on the logic in 
 # setup.py, the packages in requirements.txt will also be installed when running 
-# pip install sleap[pip].
+# pip install sleap[pypi].
 
 # These are also distrubuted through conda and not pip installed when using conda.
 attrs>=21.2.0,<=21.4.0
@@ -31,4 +31,5 @@ scikit-image
 scikit-learn ==1.0.*
 scikit-video
 seaborn
+tensorflow
 tensorflow-hub
diff --git a/setup.py b/setup.py
index 6145f3a3a..a4815bd46 100644
--- a/setup.py
+++ b/setup.py
@@ -27,14 +27,27 @@ def get_requirements(require_name=None):
         return f.read().strip().split("\n")
 
 
+def combine_requirements(req_types):
+    return sum((get_requirements(req_type) for req_type in req_types), [])
+
+
 setup(
     name="sleap",
     version=sleap_version,
     setup_requires=["setuptools_scm"],
     install_requires=get_requirements(),  # Minimal requirements if using conda.
     extras_require={
-        "pip": get_requirements("pip"),  # For pip install
-        "dev": get_requirements("pip") + get_requirements("dev"),
+        "conda_jupyter": get_requirements(
+            "jupyter"
+        ),  # For conda install with jupyter lab
+        "conda_dev": combine_requirements(
+            ["dev", "jupyter"]
+        ),  # For conda install with dev tools
+        "pypi": get_requirements("pypi"),  # For pip install
+        "jupyter": combine_requirements(
+            ["pypi", "jupyter"]
+        ),  # For pip install with jupyter lab
+        "dev": combine_requirements(["pypi", "dev", "jupyter"]),  # For dev pip install
     },
     description="SLEAP (Social LEAP Estimates Animal Poses) is a deep learning framework for animal pose tracking.",
     long_description=long_description,
diff --git a/sleap/config/pipeline_form.yaml b/sleap/config/pipeline_form.yaml
index 77722f0d4..cbcea2be5 100644
--- a/sleap/config/pipeline_form.yaml
+++ b/sleap/config/pipeline_form.yaml
@@ -376,28 +376,39 @@ inference:
   none:
 
   flow:
-    - type: text
-      text: 'Pre-tracker data cleaning:'
-    - name: tracking.target_instance_count
-      label: Target Number of Instances Per Frame
-      type: optional_int
-      none_label: No target
-      default_disabled: true
-      range: 1,100
-      default: 1
-    - name: tracking.pre_cull_to_target
-      label: Cull to Target Instance Count
-      type: bool
-      default: false
-    - name: tracking.pre_cull_iou_threshold
-      label: Cull using IoU Threshold
-      type: double
-      default: 0.8
+    # - type: text
+    #   text: 'Pre-tracker data cleaning:'
+    # - name: tracking.target_instance_count
+    #   label: Target Number of Instances Per Frame
+    #   type: optional_int
+    #   none_label: No target
+    #   default_disabled: true
+    #   range: 1,100
+    #   default: 1
+    # - name: tracking.pre_cull_to_target
+    #   label: Cull to Target Instance Count
+    #   type: bool
+    #   default: false
+    # - name: tracking.pre_cull_iou_threshold
+    #   label: Cull using IoU Threshold
+    #   type: double
+    #   default: 0.8
     - type: text
       text: 'Tracking with optical flow:

       This tracker "shifts" instances from previous frames using optical flow
       before matching instances in each frame to the shifted instances from
       prior frames.'
+    # - name: tracking.max_tracking
+    #   label: Limit max number of tracks
+    #   type: bool
+      default: false
+    - name: tracking.max_tracks
+      label: Max number of tracks
+      type: optional_int
+      none_label: No limit
+      default_disabled: true
+      range: 1,100
+      default: 1
     - name: tracking.similarity
       label: Similarity Method
       type: list
@@ -422,10 +433,10 @@ inference:
       none_label: Use max (non-robust)
       range: 0,1
       default: 0.95
-    - name: tracking.save_shifted_instances
-      label: Save shifted instances
-      type: bool
-      default: false
+    # - name: tracking.save_shifted_instances
+    #   label: Save shifted instances
+    #   type: bool
+    #   default: false
     - type: text
       text: 'Kalman filter-based tracking:

       Uses the above tracking options to track instances for an initial
@@ -449,27 +460,38 @@ inference:
       default: false
 
   simple:
+    # - type: text
+    #   text: 'Pre-tracker data cleaning:'
+    # - name: tracking.target_instance_count
+    #   label: Target Number of Instances Per Frame
+    #   type: optional_int
+    #   none_label: No target
+    #   default_disabled: true
+    #   range: 1,100
+    #   default: 1
+    # - name: tracking.pre_cull_to_target
+    #   label: Cull to Target Instance Count
+    #   type: bool
+    #   default: false
+    # - name: tracking.pre_cull_iou_threshold
+    #   label: Cull using IoU Threshold
+    #   type: double
+    #   default: 0.8
     - type: text
-      text: 'Pre-tracker data cleaning:'
-    - name: tracking.target_instance_count
-      label: Target Number of Instances Per Frame
+      text: 'Tracking:

+        This tracker assigns track identities by matching instances from prior
+        frames to instances on subsequent frames.'
+    # - name: tracking.max_tracking
+    #   label: Limit max number of tracks
+    #   type: bool
+    #   default: false
+    - name: tracking.max_tracks
+      label: Max number of tracks
       type: optional_int
-      none_label: No target
+      none_label: No limit
       default_disabled: true
       range: 1,100
       default: 1
-    - name: tracking.pre_cull_to_target
-      label: Cull to Target Instance Count
-      type: bool
-      default: false
-    - name: tracking.pre_cull_iou_threshold
-      label: Cull using IoU Threshold
-      type: double
-      default: 0.8
-    - type: text
-      text: 'Tracking:

-        This tracker assigns track identities by matching instances from prior
-        frames to instances on subsequent frames.'
     - name: tracking.similarity
       label: Similarity Method
       type: list
diff --git a/sleap/config/shortcuts.yaml b/sleap/config/shortcuts.yaml
index 53dc96814..e4eccea40 100644
--- a/sleap/config/shortcuts.yaml
+++ b/sleap/config/shortcuts.yaml
@@ -39,3 +39,4 @@ frame next medium step: Ctrl+Right
 frame prev medium step: Ctrl+Left
 frame next large step: Ctrl+Alt+Right
 frame prev large step: Ctrl+Alt+Left
+export_analysis_current: Ctrl+Alt+E
\ No newline at end of file
diff --git a/sleap/gui/app.py b/sleap/gui/app.py
index b82372511..de6ce9fbf 100644
--- a/sleap/gui/app.py
+++ b/sleap/gui/app.py
@@ -45,49 +45,44 @@
 """
 
 
-import re
 import os
-import random
 import platform
+import random
+import re
 from pathlib import Path
-
 from typing import Callable, List, Optional, Tuple
 
 from qtpy import QtCore, QtGui
-from qtpy.QtCore import Qt, QEvent
-
-from qtpy.QtWidgets import QApplication, QMainWindow
-from qtpy.QtWidgets import QMessageBox
+from qtpy.QtCore import QEvent, Qt
+from qtpy.QtWidgets import QApplication, QMainWindow, QMessageBox
 
 import sleap
-from sleap.gui.dialogs.metrics import MetricsTableDialog
-from sleap.skeleton import Skeleton
-from sleap.instance import Instance
-from sleap.io.dataset import Labels
-from sleap.io.video import available_video_exts
-from sleap.info.summary import StatisticSeries
+from sleap.gui.color import ColorManager
 from sleap.gui.commands import CommandContext, UpdateTopic
+from sleap.gui.dialogs.filedialog import FileDialog
+from sleap.gui.dialogs.formbuilder import FormBuilderModalDialog
+from sleap.gui.dialogs.metrics import MetricsTableDialog
+from sleap.gui.dialogs.shortcuts import ShortcutDialog
+from sleap.gui.overlays.instance import InstanceOverlay
+from sleap.gui.overlays.tracks import TrackListOverlay, TrackTrailOverlay
+from sleap.gui.shortcuts import Shortcuts
+from sleap.gui.state import GuiState
+from sleap.gui.web import ReleaseChecker, ping_analytics
 from sleap.gui.widgets.docks import (
     InstancesDock,
     SkeletonDock,
     SuggestionsDock,
     VideosDock,
 )
-from sleap.gui.widgets.video import QtVideoPlayer
 from sleap.gui.widgets.slider import set_slider_marks_from_labels
-from sleap.util import parse_uri_path
-
-from sleap.gui.dialogs.filedialog import FileDialog
-from sleap.gui.dialogs.formbuilder import FormBuilderModalDialog
-from sleap.gui.shortcuts import Shortcuts
-from sleap.gui.dialogs.shortcuts import ShortcutDialog
-from sleap.gui.state import GuiState
-from sleap.gui.overlays.tracks import TrackTrailOverlay, TrackListOverlay
-from sleap.gui.color import ColorManager
-from sleap.gui.overlays.instance import InstanceOverlay
-from sleap.gui.web import ReleaseChecker, ping_analytics
-
+from sleap.gui.widgets.video import QtVideoPlayer
+from sleap.info.summary import StatisticSeries
+from sleap.instance import Instance
+from sleap.io.dataset import Labels
+from sleap.io.video import available_video_exts
 from sleap.prefs import prefs
+from sleap.skeleton import Skeleton
+from sleap.util import parse_uri_path
 
 
 class MainWindow(QMainWindow):
@@ -274,10 +269,16 @@ def dropEvent(self, event):
                 # Load
                 self.commands.openProject(filename=filenames[0], first_open=True)
 
-        elif all([ext.lower() in available_video_exts() for ext in exts]):
+        elif all([ext.lower()[1:] in available_video_exts() for ext in exts]):
             # Import videos
             self.commands.showImportVideos(filenames=filenames)
 
+        else:
+            raise TypeError(
+                f"Invalid file type(s) dropped: {', '.join(exts)} \n"
+                f"Supported formats: .slp, .{', .'.join(available_video_exts())}"
+            )
+
     @property
     def labels(self) -> Labels:
         return self.state["labels"]
@@ -484,6 +485,20 @@ def add_submenu_choices(menu, title, options, key):
             lambda: self.commands.exportAnalysisFile(all_videos=True),
         )
 
+        export_csv_menu = fileMenu.addMenu("Export Analysis CSV...")
+        add_menu_item(
+            export_csv_menu,
+            "export_csv_current",
+            "Current Video...",
+            self.commands.exportCSVFile,
+        )
+        add_menu_item(
+            export_csv_menu,
+            "export_csv_all",
+            "All Videos...",
+            lambda: self.commands.exportCSVFile(all_videos=True),
+        )
+
         add_menu_item(fileMenu, "export_nwb", "Export NWB...", self.commands.exportNWB)
 
         fileMenu.addSeparator()
@@ -1102,7 +1117,7 @@ def _update_gui_state(self):
         self._buttons["delete node"].setEnabled(has_selected_node)
         self._buttons["toggle grayscale"].setEnabled(has_video)
         self._buttons["show video"].setEnabled(has_selected_video)
-        self._buttons["remove video"].setEnabled(has_selected_video)
+        self._buttons["remove video"].setEnabled(has_video)
         self._buttons["delete instance"].setEnabled(has_selected_instance)
         self.suggestions_dock.suggestions_form_widget.buttons[
             "generate_button"
@@ -1207,18 +1222,23 @@ def _after_plot_update(self, frame_idx):
     def _after_plot_change(self, player, frame_idx, selected_inst):
         """Called each time a new frame is drawn."""
 
-        # Store the current LabeledFrame (or make new, empty object)
-        self.state["labeled_frame"] = self.labels.find(
-            self.state["video"], frame_idx, return_new=True
-        )[0]
+        # Store the current frame_idx and LabeledFrame (or make new, empty object)
+        self.state["frame_idx"] = frame_idx
+        self.state["labeled_frame"] = (
+            self.labels.find(self.state["video"], frame_idx, return_new=True)[0]
+            if frame_idx is not None
+            else None
+        )
 
         # Show instances, etc, for this frame
         for overlay in self.overlays.values():
-            overlay.add_to_scene(self.state["video"], frame_idx)
+            overlay.redraw(self.state["video"], frame_idx)
 
         # Select instance if there was already selection
         if selected_inst is not None:
             player.view.selectInstance(selected_inst)
+        else:
+            self.state["instance"] = None
 
         if self.state["fit"]:
             player.zoomToFit()
@@ -1240,19 +1260,21 @@ def updateStatusMessage(self, message: Optional[str] = None):
 
         if message is None:
             message = ""
-            if len(self.labels.videos) > 1:
+            if len(self.labels.videos) > 0 and current_video is not None:
                 message += f"Video {self.labels.videos.index(current_video)+1}/"
                 message += f"{len(self.labels.videos)}"
                 message += spacer
 
-            message += f"Frame: {frame_idx+1:,}/{len(current_video):,}"
+            if current_video is not None:
+                message += f"Frame: {frame_idx+1:,}/{len(current_video):,}"
+
             if self.player.seekbar.hasSelection():
                 start, end = self.state["frame_range"]
                 message += spacer
                 message += f"Selection: {start+1:,}-{end:,} ({end-start+1:,} frames)"
 
             message += f"{spacer}Labeled Frames: "
-            if current_video is not None and current_video in self.labels.videos:
+            if current_video is not None:
                 message += str(
                     self.labels.get_labeled_frame_count(current_video, "user")
                 )
@@ -1626,7 +1648,7 @@ def main(args: Optional[list] = None):
 
     app = QApplication([])
     app.setApplicationName(f"SLEAP v{sleap.version.__version__}")
-    app.setWindowIcon(QtGui.QIcon(sleap.util.get_package_file("sleap/gui/icon.png")))
+    app.setWindowIcon(QtGui.QIcon(sleap.util.get_package_file("gui/icon.png")))
 
     window = MainWindow(
         labels_path=args.labels_path, reset=args.reset, no_usage_data=args.no_usage_data
diff --git a/sleap/gui/color.py b/sleap/gui/color.py
index dee888144..6172d236d 100644
--- a/sleap/gui/color.py
+++ b/sleap/gui/color.py
@@ -170,7 +170,9 @@ def get_track_color(self, track: Union[Track, int]) -> ColorTupleType:
         Returns:
             (r, g, b)-tuple
         """
-        track_idx = self.tracks.index(track) if isinstance(track, Track) else track
+        track_idx = track
+        if isinstance(track, Track):
+            track_idx = self.tracks.index(track) if track in self.tracks else None
         if track_idx is None:
             return (0, 0, 0)
 
diff --git a/sleap/gui/commands.py b/sleap/gui/commands.py
index c453e4e8e..698eed756 100644
--- a/sleap/gui/commands.py
+++ b/sleap/gui/commands.py
@@ -30,38 +30,37 @@ class which inherits from `AppCommand` (or a more specialized class such as
 import operator
 import os
 import re
-import sys
 import subprocess
+import sys
+import traceback
 from enum import Enum
 from glob import glob
-from pathlib import PurePath, Path
-import traceback
-from typing import Callable, Dict, Iterator, List, Optional, Type, Tuple
+from pathlib import Path, PurePath
+from typing import Callable, Dict, Iterator, List, Optional, Tuple, Type
 
-import numpy as np
-import cv2
 import attr
-from qtpy import QtCore, QtWidgets, QtGui
-from qtpy.QtWidgets import QMessageBox, QProgressDialog
+import cv2
+import numpy as np
+from qtpy import QtCore, QtGui, QtWidgets
 
-from sleap.util import get_package_file
-from sleap.skeleton import Node, Skeleton
-from sleap.instance import Instance, PredictedInstance, Point, Track, LabeledFrame
-from sleap.io.video import Video
-from sleap.io.convert import default_analysis_filename
-from sleap.io.dataset import Labels
-from sleap.io.format.adaptor import Adaptor
-from sleap.io.format.ndx_pose import NDXPoseAdaptor
 from sleap.gui.dialogs.delete import DeleteDialog
-from sleap.gui.dialogs.importvideos import ImportVideos
 from sleap.gui.dialogs.filedialog import FileDialog
-from sleap.gui.dialogs.missingfiles import MissingFilesDialog
+from sleap.gui.dialogs.importvideos import ImportVideos
 from sleap.gui.dialogs.merge import MergeDialog, ReplaceSkeletonTableDialog
 from sleap.gui.dialogs.message import MessageDialog
+from sleap.gui.dialogs.missingfiles import MissingFilesDialog
 from sleap.gui.dialogs.query import QueryDialog
-from sleap.gui.suggestions import VideoFrameSuggestions
 from sleap.gui.state import GuiState
-
+from sleap.gui.suggestions import VideoFrameSuggestions
+from sleap.instance import Instance, LabeledFrame, Point, PredictedInstance, Track
+from sleap.io.convert import default_analysis_filename
+from sleap.io.dataset import Labels
+from sleap.io.format.adaptor import Adaptor
+from sleap.io.format.csv import CSVAdaptor
+from sleap.io.format.ndx_pose import NDXPoseAdaptor
+from sleap.io.video import Video
+from sleap.skeleton import Node, Skeleton
+from sleap.util import get_package_file
 
 # Indicates whether we support multiple project windows (i.e., "open" opens new window)
 OPEN_IN_NEW = True
@@ -201,6 +200,7 @@ class CommandContext:
     def from_labels(cls, labels: Labels) -> "CommandContext":
         """Creates a command context for use independently of GUI app."""
         state = GuiState()
+        state["labels"] = labels
         app = FakeApp(labels)
         return cls(state=state, app=app)
 
@@ -330,7 +330,11 @@ def saveProjectAs(self):
 
     def exportAnalysisFile(self, all_videos: bool = False):
         """Shows gui for exporting analysis h5 file."""
-        self.execute(ExportAnalysisFile, all_videos=all_videos)
+        self.execute(ExportAnalysisFile, all_videos=all_videos, csv=False)
+
+    def exportCSVFile(self, all_videos: bool = False):
+        """Shows gui for exporting analysis csv file."""
+        self.execute(ExportAnalysisFile, all_videos=all_videos, csv=True)
 
     def exportNWB(self):
         """Show gui for exporting nwb file."""
@@ -1129,13 +1133,20 @@ class ExportAnalysisFile(AppCommand):
     }
     export_filter = ";;".join(export_formats.keys())
 
+    export_formats_csv = {
+        "CSV (*.csv)": "csv",
+    }
+    export_filter_csv = ";;".join(export_formats_csv.keys())
+
     @classmethod
     def do_action(cls, context: CommandContext, params: dict):
-        from sleap.io.format.sleap_analysis import SleapAnalysisAdaptor
         from sleap.io.format.nix import NixAdaptor
+        from sleap.io.format.sleap_analysis import SleapAnalysisAdaptor
 
         for output_path, video in params["analysis_videos"]:
-            if Path(output_path).suffix[1:] == "nix":
+            if params["csv"]:
+                adaptor = CSVAdaptor
+            elif Path(output_path).suffix[1:] == "nix":
                 adaptor = NixAdaptor
             else:
                 adaptor = SleapAnalysisAdaptor
@@ -1148,18 +1159,24 @@ def do_action(cls, context: CommandContext, params: dict):
 
     @staticmethod
     def ask(context: CommandContext, params: dict) -> bool:
-        def ask_for_filename(default_name: str) -> str:
+        def ask_for_filename(default_name: str, csv: bool) -> str:
             """Allow user to specify the filename"""
+            filter = (
+                ExportAnalysisFile.export_filter_csv
+                if csv
+                else ExportAnalysisFile.export_filter
+            )
             filename, selected_filter = FileDialog.save(
                 context.app,
                 caption="Export Analysis File...",
                 dir=default_name,
-                filter=ExportAnalysisFile.export_filter,
+                filter=filter,
             )
             return filename
 
         # Ensure labels has labeled frames
         labels = context.labels
+        is_csv = params["csv"]
         if len(labels.labeled_frames) == 0:
             raise ValueError("No labeled frames in project. Nothing to export.")
 
@@ -1177,7 +1194,7 @@ def ask_for_filename(default_name: str) -> str:
         # Specify (how to get) the output filename
         default_name = context.state["filename"] or "labels"
         fn = PurePath(default_name)
-        file_extension = "h5"
+        file_extension = "csv" if is_csv else "h5"
         if len(videos) == 1:
             # Allow user to specify the filename
             use_default = False
@@ -1190,18 +1207,23 @@ def ask_for_filename(default_name: str) -> str:
                 caption="Select Folder to Export Analysis Files...",
                 dir=str(fn.parent),
             )
-            if len(ExportAnalysisFile.export_formats) > 1:
+            export_format = (
+                ExportAnalysisFile.export_formats_csv
+                if is_csv
+                else ExportAnalysisFile.export_formats
+            )
+            if len(export_format) > 1:
                 item, ok = QtWidgets.QInputDialog.getItem(
                     context.app,
                     "Select export format",
                     "Available export formats",
-                    list(ExportAnalysisFile.export_formats.keys()),
+                    list(export_format.keys()),
                     0,
                     False,
                 )
                 if not ok:
                     return False
-                file_extension = ExportAnalysisFile.export_formats[item]
+                file_extension = export_format[item]
             if len(dirname) == 0:
                 return False
 
@@ -1218,7 +1240,9 @@ def ask_for_filename(default_name: str) -> str:
                 format_suffix=file_extension,
             )
 
-            filename = default_name if use_default else ask_for_filename(default_name)
+            filename = (
+                default_name if use_default else ask_for_filename(default_name, is_csv)
+            )
             # Check that filename is valid and create list of video / output paths
             if len(filename) != 0:
                 analysis_videos.append(video)
@@ -1364,7 +1388,11 @@ def ask(context: CommandContext, params: dict) -> bool:
 
 
 def export_dataset_gui(
-    labels: Labels, filename: str, all_labeled: bool = False, suggested: bool = False
+    labels: Labels,
+    filename: str,
+    all_labeled: bool = False,
+    suggested: bool = False,
+    verbose: bool = True,
 ) -> str:
     """Export dataset with image data and display progress GUI dialog.
 
@@ -1372,12 +1400,15 @@ def export_dataset_gui(
         labels: `sleap.Labels` dataset to export.
         filename: Output filename. Should end in `.pkg.slp`.
         all_labeled: If `True`, export all labeled frames, including frames with no user
-            instances.
-        suggested: If `True`, include image data for suggested frames.
+            instances. Defaults to `False`.
+        suggested: If `True`, include image data for suggested frames. Defaults to
+            `False`.
+        verbose: If `True`, display progress dialog. Defaults to `True`.
     """
-    win = QtWidgets.QProgressDialog(
-        "Exporting dataset with frame images...", "Cancel", 0, 1
-    )
+    if verbose:
+        win = QtWidgets.QProgressDialog(
+            "Exporting dataset with frame images...", "Cancel", 0, 1
+        )
 
     def update_progress(n, n_total):
         if win.wasCanceled():
@@ -1398,15 +1429,16 @@ def update_progress(n, n_total):
         save_frame_data=True,
         all_labeled=all_labeled,
         suggested=suggested,
-        progress_callback=update_progress,
+        progress_callback=update_progress if verbose else None,
     )
 
-    if win.wasCanceled():
-        # Delete output if saving was canceled.
-        os.remove(filename)
-        return "canceled"
+    if verbose:
+        if win.wasCanceled():
+            # Delete output if saving was canceled.
+            os.remove(filename)
+            return "canceled"
 
-    win.hide()
+        win.hide()
 
     return filename
 
@@ -1422,6 +1454,7 @@ def do_action(cls, context: CommandContext, params: dict):
             filename=params["filename"],
             all_labeled=cls.all_labeled,
             suggested=cls.suggested,
+            verbose=params.get("verbose", True),
         )
 
     @staticmethod
@@ -1837,44 +1870,61 @@ def _get_truncation_message(truncation_messages, path, video):
 
 
 class RemoveVideo(EditCommand):
-    topics = [UpdateTopic.video, UpdateTopic.suggestions]
+    topics = [UpdateTopic.video, UpdateTopic.suggestions, UpdateTopic.frame]
 
     @staticmethod
     def do_action(context: CommandContext, params: dict):
-        video = params["video"]
-        # Remove video
-        context.labels.remove_video(video)
+        videos = context.labels.videos
+        row_idxs = context.state["selected_batch_video"]
+        videos_to_be_removed = [videos[i] for i in row_idxs]
+
+        # Remove selected videos in the project
+        for video in videos_to_be_removed:
+            context.labels.remove_video(video)
 
-        # Update view if this was the current video
-        if context.state["video"] == video:
-            if len(context.labels.videos) > 0:
+        # Update the view if state has the removed video
+        if context.state["video"] in videos_to_be_removed:
+            if len(context.labels.videos):
                 context.state["video"] = context.labels.videos[-1]
             else:
                 context.state["video"] = None
 
+        if len(context.labels.videos) == 0:
+            context.app.updateStatusMessage(" ")
+
     @staticmethod
     def ask(context: CommandContext, params: dict) -> bool:
-        video = context.state["selected_video"]
-        if video is None:
-            return False
+        videos = context.labels.videos.copy()
+        row_idxs = context.state["selected_batch_video"]
+        video_file_names = []
+        total_num_labeled_frames = 0
+        for idx in row_idxs:
+
+            video = videos[idx]
+            if video is None:
+                return False
 
-        # Count labeled frames for this video
-        n = len(context.labels.find(video))
+            # Count labeled frames for this video
+            n = len(context.labels.find(video))
+
+            if n > 0:
+                total_num_labeled_frames += n
+                video_file_names.append(
+                    f"{video}".split(", shape")[0].split("filename=")[-1].split("/")[-1]
+                )
 
         # Warn if there are labels that will be deleted
-        if n > 0:
+        if len(video_file_names) >= 1:
             response = QtWidgets.QMessageBox.critical(
                 context.app,
                 "Removing video with labels",
-                f"{n} labeled frames in this video will be deleted, "
-                "are you sure you want to remove this video?",
+                f"{total_num_labeled_frames} labeled frames in {', '.join(video_file_names)} will be deleted, "
+                "are you sure you want to remove the videos?",
                 QtWidgets.QMessageBox.Yes,
                 QtWidgets.QMessageBox.No,
             )
             if response == QtWidgets.QMessageBox.No:
                 return False
-
-        params["video"] = video
         return True
 
 
@@ -1930,15 +1980,28 @@ def delete_extra_skeletons(labels: Labels):
 
             labels.skeletons = skeletons_used
 
+    @staticmethod
+    def get_template_skeleton_filename(context: CommandContext) -> str:
+        """Helper function to get the template skeleton filename from dropdown.
+
+        Args:
+            context: The `CommandContext`.
+
+        Returns:
+            Path to the template skeleton shipped with SLEAP.
+        """
+
+        template = context.app.skeleton_dock.skeleton_templates.currentText()
+        filename = get_package_file(f"skeletons/{template}.json")
+        return filename
+
     @staticmethod
     def ask(context: CommandContext, params: dict) -> bool:
         filters = ["JSON skeleton (*.json)", "HDF5 skeleton (*.h5 *.hdf5)"]
         # Check whether to load from file or preset
         if params.get("template", False):
             # Get selected template from dropdown
-            template = context.app.skeletonTemplates.currentText()
-            # Load from selected preset
-            filename = get_package_file(f"sleap/skeletons/{template}.json")
+            filename = OpenSkeleton.get_template_skeleton_filename(context)
         else:
             filename, selected_filter = FileDialog.open(
                 context.app,
diff --git a/sleap/gui/dataviews.py b/sleap/gui/dataviews.py
index a8c7f42b6..0a008bea7 100644
--- a/sleap/gui/dataviews.py
+++ b/sleap/gui/dataviews.py
@@ -301,6 +301,7 @@ def __init__(
         is_sortable: bool = False,
         is_activatable: bool = False,
         ellipsis_left: bool = False,
+        multiple_selection: bool = False,
     ):
         super(GenericTableView, self).__init__()
 
@@ -309,6 +310,7 @@ def __init__(
         self.name_prefix = name_prefix if name_prefix is not None else self.name_prefix
         self.is_sortable = is_sortable or self.is_sortable
         self.is_activatable = is_activatable or self.is_activatable
+        self.multiple_selection = multiple_selection
 
         self.setModel(model)
 
@@ -317,7 +319,10 @@ def __init__(
             self.setWordWrap(False)
         self.horizontalHeader().setStretchLastSection(True)
         self.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows)
-        self.setSelectionMode(QtWidgets.QAbstractItemView.SingleSelection)
+        if self.multiple_selection:
+            self.setSelectionMode(QtWidgets.QAbstractItemView.ExtendedSelection)
+        else:
+            self.setSelectionMode(QtWidgets.QAbstractItemView.SingleSelection)
         self.setSortingEnabled(self.is_sortable)
 
         self.doubleClicked.connect(self.activateSelected)
@@ -370,6 +375,11 @@ def getSelectedRowItem(self) -> Any:
         not the converted dict.
         """
         idx = self.currentIndex()
+
+        if self.multiple_selection:
+            idx_temp = set([x.row() for x in self.selectedIndexes()])
+            self.state[f"selected_batch_{self.row_name}"] = idx_temp
+
         if not idx.isValid():
             return None
         return self.model().original_items[idx.row()]
diff --git a/sleap/gui/dialogs/filedialog.py b/sleap/gui/dialogs/filedialog.py
index a00a7e68c..930c71b0d 100644
--- a/sleap/gui/dialogs/filedialog.py
+++ b/sleap/gui/dialogs/filedialog.py
@@ -7,15 +7,46 @@
 """
 
 import os, re, sys
-from pathlib import Path
 
+from functools import wraps
+from pathlib import Path
+from typing import Callable
 from qtpy import QtWidgets
 
 
+def os_specific_method(func) -> Callable:
+    """Check if native dialog should be used and update kwargs based on OS.
+
+    Native Mac/Win file dialogs add file extension based on selected file type but
+    non-native dialog (used for Linux) does not do this by default.
+    """
+
+    @wraps(func)
+    def set_dialog_type(cls, *args, **kwargs):
+        is_linux = sys.platform.startswith("linux")
+        env_var_set = os.environ.get("USE_NON_NATIVE_FILE", False)
+        cls.is_non_native = is_linux or env_var_set
+
+        if cls.is_non_native:
+            kwargs["options"] = kwargs.get("options", 0)
+            kwargs["options"] |= QtWidgets.QFileDialog.DontUseNativeDialog
+
+        # Make sure we don't send empty options argument
+        if "options" in kwargs and not kwargs["options"]:
+            del kwargs["options"]
+
+        return func(cls, *args, **kwargs)
+
+    return set_dialog_type
+
+
 class FileDialog:
     """Substitute for QFileDialog; see class methods for details."""
 
+    is_non_native = False
+
     @classmethod
+    @os_specific_method
     def open(cls, *args, **kwargs):
         """
         Wrapper for `QFileDialog.getOpenFileName()`
@@ -24,10 +55,10 @@ def open(cls, *args, **kwargs):
 
         Passes along everything except empty "options" arg.
         """
-        cls._non_native_if_set(kwargs)
         return QtWidgets.QFileDialog.getOpenFileName(*args, **kwargs)
 
     @classmethod
+    @os_specific_method
     def openMultiple(cls, *args, **kwargs):
         """
         Wrapper for `QFileDialog.getOpenFileNames()`
@@ -36,10 +67,10 @@ def openMultiple(cls, *args, **kwargs):
 
         Passes along everything except empty "options" arg.
         """
-        cls._non_native_if_set(kwargs)
         return QtWidgets.QFileDialog.getOpenFileNames(*args, **kwargs)
 
     @classmethod
+    @os_specific_method
     def save(cls, *args, **kwargs):
         """Wrapper for `QFileDialog.getSaveFileName()`
 
@@ -47,11 +78,10 @@ def save(cls, *args, **kwargs):
 
         Passes along everything except empty "options" arg.
         """
-        is_non_native = cls._non_native_if_set(kwargs)
 
         # The non-native file dialog doesn't add file extensions from the
         # file-type menu in the dialog, so we need to do this ourselves.
-        if is_non_native and "filter" in kwargs and "dir" in kwargs:
+        if cls.is_non_native and "filter" in kwargs and "dir" in kwargs:
             filename = kwargs["dir"]
             filters = kwargs["filter"].split(";;")
             if filters:
@@ -61,7 +91,7 @@ def save(cls, *args, **kwargs):
         filename, filter = QtWidgets.QFileDialog.getSaveFileName(*args, **kwargs)
 
         # Make sure filename has appropriate file extension.
-        if is_non_native and filter:
+        if cls.is_non_native and filter:
             fn = Path(filename)
             # Get extension from filter as list of "*.ext"
             match = re.findall("\*(\.[a-zA-Z0-9]+)", filter)
@@ -77,6 +107,7 @@ def save(cls, *args, **kwargs):
         return filename, filter
 
     @classmethod
+    @os_specific_method
     def openDir(cls, *args, **kwargs):
         """Wrapper for `QFileDialog.getExistingDirectory()`
 
@@ -85,20 +116,3 @@ def openDir(cls, *args, **kwargs):
         Passes along everything except empty "options" arg.
         """
         return QtWidgets.QFileDialog.getExistingDirectory(*args, **kwargs)
-
-    @staticmethod
-    def _non_native_if_set(kwargs) -> bool:
-        is_non_native = False
-        is_linux = sys.platform.startswith("linux")
-        env_var_set = os.environ.get("USE_NON_NATIVE_FILE", False)
-
-        if is_linux or env_var_set:
-            is_non_native = True
-            kwargs["options"] = kwargs.get("options", 0)
-            kwargs["options"] |= QtWidgets.QFileDialog.DontUseNativeDialog
-
-        # Make sure we don't send empty options argument
-        if "options" in kwargs and not kwargs["options"]:
-            del kwargs["options"]
-
-        return is_non_native
diff --git a/sleap/gui/dialogs/formbuilder.py b/sleap/gui/dialogs/formbuilder.py
index b46fc6673..83385bcb4 100644
--- a/sleap/gui/dialogs/formbuilder.py
+++ b/sleap/gui/dialogs/formbuilder.py
@@ -27,11 +27,10 @@
 want to add a new type of supported form field.
 """
 
-import yaml
-
 from typing import Any, Dict, List, Optional, Text
 
-from qtpy import QtWidgets, QtCore
+import yaml
+from qtpy import QtCore, QtWidgets
 
 from sleap.gui.dialogs.filedialog import FileDialog
 from sleap.util import get_package_file
@@ -110,7 +109,7 @@ def from_name(cls, form_name: Text, *args, **kwargs) -> "YamlFormWidget":
         Returns:
             Instance of `YamlFormWidget` class.
         """
-        yaml_path = get_package_file(f"sleap/config/{form_name}.yaml")
+        yaml_path = get_package_file(f"config/{form_name}.yaml")
         return cls(yaml_path, *args, **kwargs)
 
     @property
@@ -579,7 +578,7 @@ def _make_file_button(
             def select_file(*args, x=field):
                 filter = item.get("filter", "Any File (*.*)")
                 filename, _ = FileDialog.open(
-                    None, directory=None, caption="Open File", filter=filter
+                    None, dir=None, caption="Open File", filter=filter
                 )
                 if len(filename):
                     x.setText(filename)
@@ -588,7 +587,7 @@ def select_file(*args, x=field):
         elif item["type"].split("_")[-1] == "dir":
             # Define function for button to trigger
             def select_file(*args, x=field):
-                filename = FileDialog.openDir(None, directory=None, caption="Open File")
+                filename = FileDialog.openDir(None, dir=None, caption="Open File")
                 if len(filename):
                     x.setText(filename)
                 self.valueChanged.emit()
diff --git a/sleap/gui/learning/configs.py b/sleap/gui/learning/configs.py
index 0bf22478e..74774ea00 100644
--- a/sleap/gui/learning/configs.py
+++ b/sleap/gui/learning/configs.py
@@ -1,23 +1,22 @@
 """
 Find, load, and show lists of saved `TrainingJobConfig`.
 """
-import attr
 import datetime
-import h5py
 import os
 import re
-import numpy as np
 from pathlib import Path
+from typing import Any, Dict, List, Optional, Text
+
+import attr
+import h5py
+import numpy as np
+from qtpy import QtCore, QtWidgets
 
 from sleap import Labels, Skeleton
 from sleap import util as sleap_utils
 from sleap.gui.dialogs.filedialog import FileDialog
-from sleap.nn.config import TrainingJobConfig
 from sleap.gui.dialogs.formbuilder import FieldComboWidget
-
-from typing import Any, Dict, List, Optional, Text
-
-from qtpy import QtCore, QtWidgets
+from sleap.nn.config import TrainingJobConfig
 
 
 @attr.s(auto_attribs=True, slots=True)
@@ -404,7 +403,7 @@ def get_filtered_configs(
         """Returns filtered subset of loaded configs."""
 
         base_config_dir = os.path.realpath(
-            sleap_utils.get_package_file("sleap/training_profiles")
+            sleap_utils.get_package_file("training_profiles")
         )
 
         cfgs_to_return = []
@@ -474,7 +473,7 @@ def make_from_labels_filename(
             labels_model_dir = os.path.join(os.path.dirname(labels_filename), "models")
             dir_paths.append(labels_model_dir)
 
-        base_config_dir = sleap_utils.get_package_file("sleap/training_profiles")
+        base_config_dir = sleap_utils.get_package_file("training_profiles")
         dir_paths.append(base_config_dir)
 
         return cls(dir_paths=dir_paths, head_filter=head_filter)
diff --git a/sleap/gui/learning/dialog.py b/sleap/gui/learning/dialog.py
index 26531872c..d9f872fda 100644
--- a/sleap/gui/learning/dialog.py
+++ b/sleap/gui/learning/dialog.py
@@ -18,6 +18,7 @@
 
 from qtpy import QtWidgets, QtCore
 
+import json
 
 # List of fields which should show list of skeleton nodes
 NODE_LIST_FIELDS = [
@@ -85,6 +86,9 @@ def __init__(
 
         # Layout for buttons
         buttons = QtWidgets.QDialogButtonBox()
+        self.copy_button = buttons.addButton(
+            "Copy to clipboard", QtWidgets.QDialogButtonBox.ActionRole
+        )
         self.save_button = buttons.addButton(
             "Save configuration files...", QtWidgets.QDialogButtonBox.ActionRole
         )
@@ -94,6 +98,7 @@ def __init__(
         self.cancel_button = buttons.addButton(QtWidgets.QDialogButtonBox.Cancel)
         self.run_button = buttons.addButton("Run", QtWidgets.QDialogButtonBox.ApplyRole)
 
+        self.copy_button.setToolTip("Copy configuration to the clipboard")
         self.save_button.setToolTip("Save scripts and configuration to run pipeline.")
         self.export_button.setToolTip(
             "Export data, configuration, and scripts for remote training and inference."
@@ -140,6 +145,7 @@ def __init__(
         self.connect_signals()
 
         # Connect actions for buttons
+        self.copy_button.clicked.connect(self.copy)
         self.save_button.clicked.connect(self.save)
         self.export_button.clicked.connect(self.export_package)
         self.cancel_button.clicked.connect(self.reject)
@@ -674,10 +680,6 @@ def view_datagen(self):
         datagen.show_datagen_preview(self.labels, config_info_list)
         self.hide()
 
-    def on_button_click(self, button):
-        if button == self.save_button:
-            self.save()
-
     def run(self):
         """Run with current dialog settings."""
 
@@ -717,14 +719,38 @@ def run(self):
             win.setWindowTitle("Inference Results")
             win.exec_()
 
+    def copy(self):
+        """Copy scripts and configs to clipboard"""
+
+        # Get all info from dialog
+        pipeline_form_data = self.pipeline_form_widget.get_form_data()
+        config_info_list = self.get_every_head_config_data(pipeline_form_data)
+        pipeline_form_data = json.dumps(pipeline_form_data, indent=2)
+
+        # Format information for each tab in dialog
+        output = [pipeline_form_data]
+        for config_info in config_info_list:
+            config_info = config_info.config.to_json()
+            config_info = json.loads(config_info)
+            config_info = json.dumps(config_info, indent=2)
+            output.append(config_info)
+        output = "\n".join(output)
+
+        # Set the clipboard text
+        clipboard = QtWidgets.QApplication.clipboard()
+        clipboard.setText(output)
+
     def save(
         self, output_dir: Optional[str] = None, labels_filename: Optional[str] = None
     ):
         """Save scripts and configs to run pipeline."""
         if output_dir is None:
-            models_dir = os.path.join(os.path.dirname(self.labels_filename), "/models")
+            labels_fn = Path(self.labels_filename)
+            models_dir = Path(labels_fn.parent, "models")
             output_dir = FileDialog.openDir(
-                None, directory=models_dir, caption="Select directory to save scripts"
+                None,
+                dir=models_dir.as_posix(),
+                caption="Select directory to save scripts",
             )
 
             if not output_dir:
diff --git a/sleap/gui/learning/runners.py b/sleap/gui/learning/runners.py
index 460ca7e5a..ca60c4127 100644
--- a/sleap/gui/learning/runners.py
+++ b/sleap/gui/learning/runners.py
@@ -224,6 +224,7 @@ def make_predict_cli_call(
 
         optional_items_as_nones = (
             "tracking.target_instance_count",
+            "tracking.max_tracks",
             "tracking.kf_init_frame_count",
             "tracking.robust",
             "max_instances",
@@ -233,6 +234,16 @@ def make_predict_cli_call(
             if key in self.inference_params and self.inference_params[key] is None:
                 del self.inference_params[key]
 
+        # Setting max_tracks to True means we want to use the max_tracking mode.
+        if "tracking.max_tracks" in self.inference_params:
+            self.inference_params["tracking.max_tracking"] = True
+
+            # Hacky:  Update the tracker name to include "maxtracks" suffix.
+            if self.inference_params["tracking.tracker"] in ("simple", "flow"):
+                self.inference_params["tracking.tracker"] = (
+                    self.inference_params["tracking.tracker"] + "maxtracks"
+                )
+
         # --tracking.kf_init_frame_count enables the kalman filter tracking
         # so if not set, then remove other (unused) args
         if "tracking.kf_init_frame_count" not in self.inference_params:
@@ -241,6 +252,7 @@ def make_predict_cli_call(
 
         bool_items_as_ints = (
             "tracking.pre_cull_to_target",
+            "tracking.max_tracking",
             "tracking.post_connect_single_breaks",
             "tracking.save_shifted_instances",
         )
@@ -470,7 +482,6 @@ def write_pipeline_files(
         )
         # And join them into a single call to inference
         inference_script += " ".join(cli_args) + "\n"
-
         # Setup job params
         only_suggested_frames = False
         if type(item_for_inference) == DatasetItemForInference:
diff --git a/sleap/gui/overlays/base.py b/sleap/gui/overlays/base.py
index f648c5a43..019f87355 100644
--- a/sleap/gui/overlays/base.py
+++ b/sleap/gui/overlays/base.py
@@ -61,6 +61,8 @@ def remove_from_scene(self):
 
         This method does not need to be called when changing the plot to a new frame.
         """
+        if self.items is None:
+            return
         for item in self.items:
             self.player.scene.removeItem(item)
 
diff --git a/sleap/gui/shortcuts.py b/sleap/gui/shortcuts.py
index b81eabf05..37db5fb51 100644
--- a/sleap/gui/shortcuts.py
+++ b/sleap/gui/shortcuts.py
@@ -58,6 +58,7 @@ class Shortcuts(object):
         "frame prev medium step",
         "frame next large step",
         "frame prev large step",
+        "export_analysis_current",
     )
 
     def __init__(self):
diff --git a/sleap/gui/widgets/docks.py b/sleap/gui/widgets/docks.py
index ef473ff96..43e218adb 100644
--- a/sleap/gui/widgets/docks.py
+++ b/sleap/gui/widgets/docks.py
@@ -1,25 +1,26 @@
 """Module for creating dock widgets for the `MainWindow`."""
 
 from typing import Callable, Iterable, List, Optional, Type, Union
+
 from qtpy import QtGui
 from qtpy.QtCore import Qt
 from qtpy.QtWidgets import (
-    QWidget,
-    QDockWidget,
-    QMainWindow,
-    QLabel,
     QComboBox,
+    QDockWidget,
     QGroupBox,
+    QHBoxLayout,
+    QLabel,
+    QLayout,
+    QMainWindow,
     QPushButton,
     QTabWidget,
-    QLayout,
-    QHBoxLayout,
     QVBoxLayout,
+    QWidget,
 )
 
 from sleap.gui.dataviews import (
-    GenericTableView,
     GenericTableModel,
+    GenericTableView,
     LabeledFrameTableModel,
     SkeletonEdgesTableModel,
     SkeletonNodeModel,
@@ -179,6 +180,7 @@ def create_tables(self) -> GenericTableView:
             is_activatable=True,
             model=self.model,
             ellipsis_left=True,
+            multiple_selection=True,
         )
 
         return self.table
@@ -192,7 +194,6 @@ def create_video_edit_and_nav_buttons(self) -> QWidget:
         self.add_button(hb, "Show Video", self.table.activateSelected)
         self.add_button(hb, "Add Videos", main_window.commands.addVideo)
         self.add_button(hb, "Remove Video", main_window.commands.removeVideo)
-
         hbw = QWidget()
         hbw.setLayout(hb)
         return hbw
@@ -331,7 +332,7 @@ def create_templates_groupbox(self) -> QGroupBox:
         vb = QVBoxLayout()
         hb = QHBoxLayout()
 
-        skeletons_folder = get_package_file("sleap/skeletons")
+        skeletons_folder = get_package_file("skeletons")
         skeletons_json_files = find_files_by_suffix(
             skeletons_folder, suffix=".json", depth=1
         )
diff --git a/sleap/gui/widgets/slider.py b/sleap/gui/widgets/slider.py
index bfe6bc9dd..084aeb7b0 100644
--- a/sleap/gui/widgets/slider.py
+++ b/sleap/gui/widgets/slider.py
@@ -248,8 +248,10 @@ def value(self) -> float:
         """Returns value of slider."""
         return self._val_main
 
-    def setValue(self, val: float) -> float:
+    def setValue(self, val: Optional[float]):
         """Sets value of slider."""
+        if val is None:
+            return
         self._val_main = val
         x = self._toPos(val)
         self.handle.setPos(x, 0)
diff --git a/sleap/gui/widgets/video.py b/sleap/gui/widgets/video.py
index 8c8bbdbac..502ea388e 100644
--- a/sleap/gui/widgets/video.py
+++ b/sleap/gui/widgets/video.py
@@ -14,7 +14,6 @@
 """
 from collections import deque
 
-
 # FORCE_REQUESTS controls whether we emit a signal to process frame requests
 # if we haven't processed any for a certain amount of time.
 # Usually the processing gets triggered by a timer but if the user is (e.g.)
@@ -25,58 +24,55 @@
 FORCE_REQUESTS = True
 
 
-from qtpy import QtWidgets, QtCore
+import atexit
+import math
+import time
+from typing import Callable, List, Optional, Union
 
-from qtpy.QtWidgets import (
-    QApplication,
-    QVBoxLayout,
-    QWidget,
-    QGraphicsView,
-    QGraphicsScene,
-    QShortcut,
-    QGraphicsItem,
-    QGraphicsObject,
-    QGraphicsEllipseItem,
-    QGraphicsTextItem,
-    QGraphicsRectItem,
-    QGraphicsPolygonItem,
-)
+import numpy as np
+import qimage2ndarray
+from qtpy import QtCore, QtWidgets
+from qtpy.QtCore import QLineF, QMarginsF, QPointF, QRectF, Qt
 from qtpy.QtGui import (
-    QImage,
-    QPixmap,
-    QPainter,
-    QPainterPath,
-    QTransform,
-    QPen,
     QBrush,
     QColor,
     QCursor,
     QFont,
-    QPolygonF,
+    QImage,
     QKeyEvent,
-    QMouseEvent,
     QKeySequence,
+    QMouseEvent,
+    QPainter,
+    QPainterPath,
+    QPen,
+    QPixmap,
+    QPolygonF,
+    QTransform,
+)
+from qtpy.QtWidgets import (
+    QApplication,
+    QGraphicsEllipseItem,
+    QGraphicsItem,
+    QGraphicsObject,
+    QGraphicsPolygonItem,
+    QGraphicsRectItem,
+    QGraphicsScene,
+    QGraphicsTextItem,
+    QGraphicsView,
+    QShortcut,
+    QVBoxLayout,
+    QWidget,
 )
-from qtpy.QtCore import Qt, QRectF, QPointF, QMarginsF, QLineF
-
-import atexit
-import math
-import time
-import numpy as np
-
-from typing import Callable, List, Optional, Union
 
 import sleap
-from sleap.prefs import prefs
-from sleap.skeleton import Node
-from sleap.instance import Instance, PredictedInstance, Point
-from sleap.io.video import Video
-from sleap.gui.widgets.slider import VideoSlider
-from sleap.gui.state import GuiState
 from sleap.gui.color import ColorManager
 from sleap.gui.shortcuts import Shortcuts
-
-import qimage2ndarray
+from sleap.gui.state import GuiState
+from sleap.gui.widgets.slider import VideoSlider
+from sleap.instance import Instance, Point, PredictedInstance
+from sleap.io.video import Video
+from sleap.prefs import prefs
+from sleap.skeleton import Node
 
 
 class LoadImageWorker(QtCore.QObject):
@@ -410,22 +406,33 @@ def load_video(self, video: Video, plot=True):
 
         self.video = video
 
-        # Is this necessary?
-        self.view.scene.setSceneRect(0, 0, video.width, video.height)
+        if self.video is None:
+            self.reset()
+        else:
+            # Is this necessary?
+            self.view.scene.setSceneRect(0, 0, video.width, video.height)
 
-        self.seekbar.setMinimum(0)
-        self.seekbar.setMaximum(self.video.last_frame_idx)
-        self.seekbar.setEnabled(True)
-        self.seekbar.resizeEvent()
+            self.seekbar.setMinimum(0)
+            self.seekbar.setMaximum(self.video.last_frame_idx)
+            self.seekbar.setEnabled(True)
+            self.seekbar.resizeEvent()
 
         if plot:
             self.plot()
 
     def reset(self):
         """Reset viewer by removing all video data."""
+        # Reset view and video
         self.video = None
-        self.state["frame_idx"] = None
         self.view.clear()
+        self.view.setImage(QImage(sleap.util.get_package_file("gui/background.png")))
+
+        # Handle overlays and gui state in callback
+        frame_idx = None
+        selected_instance = None
+        self.changedPlot.emit(self, frame_idx, selected_instance)
+
+        # Reset seekbar
         self.seekbar.setMaximum(0)
         self.seekbar.setEnabled(False)
 
@@ -799,7 +806,7 @@ def __init__(self, state=None, player=None, *args, **kwargs):
         self.setTransformationAnchor(anchor_mode)
 
         # Set icon as default background.
-        self.setImage(QImage(sleap.util.get_package_file("sleap/gui/background.png")))
+        self.setImage(QImage(sleap.util.get_package_file("gui/background.png")))
 
     def dragEnterEvent(self, event):
         if self.parentWidget():
@@ -2156,6 +2163,9 @@ def mousePressEvent(self, event):
             elif self.bottom_right_box.contains(event.pos()):
                 self.resizing = "bottom_right"
                 self.origin = self.rect().topLeft()
+            else:
+                # Pass event down the stack to continue panning
+                event.setAccepted(False)
 
             self.ref_width = self.rect().width()
             self.ref_height = self.rect().height()
@@ -2254,7 +2264,6 @@ def mouseReleaseEvent(self, event):
 
             # Update the instance
             self.parent.updatePoints(complete=True, user_change=True)
-
             self.resizing = None
 
 
diff --git a/sleap/info/write_tracking_h5.py b/sleap/info/write_tracking_h5.py
index 8bd583230..2b714eeb5 100644
--- a/sleap/info/write_tracking_h5.py
+++ b/sleap/info/write_tracking_h5.py
@@ -1,4 +1,4 @@
-"""Generate an HDF5 file with track occupancy and point location data.
+"""Generate an HDF5 or CSV file with track occupancy and point location data.
 
 Ignores tracks that are entirely empty. By default will also ignore
 empty frames from the beginning and end of video, although
@@ -29,6 +29,7 @@
 import json
 import h5py as h5
 import numpy as np
+import pandas as pd
 
 from typing import Any, Dict, List, Tuple, Union
 
@@ -286,12 +287,77 @@ def write_occupancy_file(
     print(f"Saved as {output_path}")
 
 
+def write_csv_file(output_path, data_dict):
+
+    """Write CSV file with data from given dictionary.
+
+    Args:
+        output_path: Path of HDF5 file.
+        data_dict: Dictionary with data to save. Keys are dataset names,
+            values are the data.
+
+    Returns:
+        None
+    """
+
+    if data_dict["tracks"].shape[-1] == 0:
+        print(f"No tracks to export in {data_dict['video_path']}. Skipping the export")
+        return
+
+    data_dict["node_names"] = [s.decode() for s in data_dict["node_names"]]
+    data_dict["track_names"] = [s.decode() for s in data_dict["track_names"]]
+    data_dict["track_occupancy"] = np.transpose(data_dict["track_occupancy"]).astype(
+        bool
+    )
+
+    # Find frames with at least one animal tracked.
+    valid_frame_idxs = np.argwhere(data_dict["track_occupancy"].any(axis=1)).flatten()
+
+    tracks = []
+    for frame_idx in valid_frame_idxs:
+        frame_tracks = data_dict["tracks"][frame_idx]
+
+        for i in range(frame_tracks.shape[-1]):
+            pts = frame_tracks[..., i]
+            conf_scores = data_dict["point_scores"][frame_idx][..., i]
+
+            if np.isnan(pts).all():
+                # Skip if animal wasn't detected in the current frame.
+                continue
+            if data_dict["track_names"]:
+                track = data_dict["track_names"][i]
+            else:
+                track = None
+
+            instance_score = data_dict["instance_scores"][frame_idx][i]
+
+            detection = {
+                "track": track,
+                "frame_idx": frame_idx,
+                "instance.score": instance_score,
+            }
+
+            # Coordinates for each body part.
+            for node_name, score, (x, y) in zip(
+                data_dict["node_names"], conf_scores, pts
+            ):
+                detection[f"{node_name}.x"] = x
+                detection[f"{node_name}.y"] = y
+                detection[f"{node_name}.score"] = score
+
+            tracks.append(detection)
+
+    tracks = pd.DataFrame(tracks)
+    tracks.to_csv(output_path, index=False)
+
+
 def main(
     labels: Labels,
     output_path: str,
     labels_path: str = None,
     all_frames: bool = True,
     video: Video = None,
+    csv: bool = False,
 ):
     """Writes HDF5 file with matrices of track occupancy and coordinates.
 
@@ -306,6 +372,7 @@ def main(
         video: The :py:class:`Video` from which to get data. If no `video` is specified,
             then the first video in `source_object` videos list will be used. If there
             are no labeled frames in the `video`, then no output file will be written.
+        csv: Bool to save the analysis as a csv file if set to True
 
     Returns:
         None
@@ -367,7 +434,10 @@ def main(
         provenance=json.dumps(labels.provenance),  # dict cannot be written to hdf5.
     )
 
-    write_occupancy_file(output_path, data_dict, transpose=True)
+    if csv:
+        write_csv_file(output_path, data_dict)
+    else:
+        write_occupancy_file(output_path, data_dict, transpose=True)
 
 
 if __name__ == "__main__":
diff --git a/sleap/io/dataset.py b/sleap/io/dataset.py
index c54ed2755..45280cc54 100644
--- a/sleap/io/dataset.py
+++ b/sleap/io/dataset.py
@@ -40,6 +40,7 @@
 import itertools
 import os
 from collections.abc import MutableSequence
+from pathlib import Path
 from typing import (
     Callable,
     List,
@@ -194,10 +195,7 @@ def _make_track_occupancy(self, video: Video) -> Dict[Video, RangeList]:
 
     def get_track_occupancy(self, video: Video, track: Track) -> RangeList:
         """Access track occupancy cache that adds video/track as needed."""
-        if video not in self._track_occupancy:
-            self._track_occupancy[video] = dict()
-
-        if track not in self._track_occupancy[video]:
+        if track not in self.get_video_track_occupancy(video=video):
             self._track_occupancy[video][track] = RangeList()
         return self._track_occupancy[video][track]
 
@@ -251,21 +249,18 @@ def track_swap(
 
     def add_track(self, video: Video, track: Track):
         """Add a track to the labels."""
-        self._track_occupancy[video][track] = RangeList()
+        self.get_track_occupancy(video=video, track=track)
 
     def add_instance(self, frame: LabeledFrame, instance: Instance):
         """Add an instance to the labels."""
-        if frame.video not in self._track_occupancy:
-            self._track_occupancy[frame.video] = dict()
 
         # Add track in its not already present in labels
-        if instance.track not in self._track_occupancy[frame.video]:
-            self._track_occupancy[frame.video][instance.track] = RangeList()
-
-        self._track_occupancy[frame.video][instance.track].insert(
-            (frame.frame_idx, frame.frame_idx + 1)
+        track_occupancy = self.get_track_occupancy(
+            video=frame.video, track=instance.track
         )
 
+        track_occupancy.insert((frame.frame_idx, frame.frame_idx + 1))
+
         self.update_counts_for_frame(frame)
 
     def remove_instance(self, frame: LabeledFrame, instance: Instance):
@@ -301,6 +296,10 @@ def get_filtered_frame_idxs(
         self, video: Optional[Video] = None, filter: Text = ""
     ) -> Set[Tuple[int, int]]:
         """Return list of (video_idx, frame_idx) tuples matching video/filter."""
+        if video not in self.labels.videos:
+            # Set value of video to None if not present in the videos list.
+            video = None
+
         if filter == "":
             filter_func = lambda lf: video is None or lf.video == video
         elif filter == "user":
@@ -1335,8 +1334,12 @@ def add_instance(self, frame: LabeledFrame, instance: Instance):
         if instance.track in tracks_in_frame:
             instance.track = None
 
+        # Add instance and track to labels
         frame.instances.append(instance)
+        if (instance.track is not None) and (instance.track not in self.tracks):
+            self.add_track(video=frame.video, track=instance.track)
 
+        # Update cache
         self._cache.add_instance(frame, instance)
 
     def find_track_occupancy(
@@ -2221,7 +2224,12 @@ def from_deepposekit(
         )
 
     def save_frame_data_imgstore(
-        self, output_dir: str = "./", format: str = "png", all_labels: bool = False
+        self,
+        output_dir: str = "./",
+        format: str = "png",
+        all_labeled: bool = False,
+        suggested: bool = False,
+        progress_callback: Optional[Callable[[int, int], None]] = None,
     ) -> List[ImgStoreVideo]:
         """Write images for labeled frames from all videos to imgstore datasets.
 
@@ -2234,28 +2242,55 @@ def save_frame_data_imgstore(
                 Use "png" for lossless, "jpg" for lossy.
                 Other imgstore formats will probably work as well but
                 have not been tested.
-            all_labels: Include any labeled frames, not just the frames
+            all_labeled: Include any labeled frames, not just the frames
                 we'll use for training (i.e., those with `Instance` objects ).
+            suggested: Include suggested frames even if they do not have instances.
+                Useful for inference after training. Defaults to `False`.
+            progress_callback: If provided, this function will be called to report the
+                progress of the frame data saving. This function should be a callable
+                of the form: `fn(n, n_total)` where `n` is the number of frames saved so
+                far and `n_total` is the total number of frames that will be saved. This
+                is called after each video is processed. If the function has a return
+                value and it returns `False`, saving will be canceled and the output
+                deleted.
 
         Returns:
             A list of :class:`ImgStoreVideo` objects with the stored
             frames.
         """
+
+        # Lets gather all the suggestions by video
+        suggestion_frames_by_video = {video: [] for video in self.videos}
+        if suggested:
+            for suggestion in self.suggestions:
+                suggestion_frames_by_video[suggestion.video].append(
+                    suggestion.frame_idx
+                )
+
         # For each label
         imgstore_vids = []
-        for v_idx, v in enumerate(self.videos):
-            frame_nums = [
-                lf.frame_idx
-                for lf in self.labeled_frames
-                if v == lf.video and (all_labels or lf.has_user_instances)
-            ]
+        total_vids = len(self.videos)
+        for v_idx, video in enumerate(self.videos):
+            lfs_v = self.find(video)
+            frame_nums = {
+                lf.frame_idx for lf in lfs_v if all_labeled or lf.has_user_instances
+            }
+
+            if suggested:
+                frame_nums.update(suggestion_frames_by_video[video])
 
             # Join with "/" instead of os.path.join() since we want
             # path to work on Windows and Posix systems
-            frames_filename = output_dir + f"/frame_data_vid{v_idx}"
-            vid = v.to_imgstore(
-                path=frames_filename, frame_numbers=frame_nums, format=format
+            frames_fn = Path(output_dir, f"frame_data_vid{v_idx}")
+            vid = video.to_imgstore(
+                path=frames_fn.as_posix(), frame_numbers=frame_nums, format=format
             )
+            if progress_callback is not None:
+                # Notify update callback.
+                ret = progress_callback(v_idx, total_vids)
+                if ret == False:
+                    vid.close()
+                    return []
 
             # Close the video for now
             vid.close()
@@ -2298,23 +2333,30 @@ def save_frame_data_hdf5(
         Returns:
             A list of :class:`HDF5Video` objects with the stored frames.
         """
+
+        # Lets gather all the suggestions by video
+        suggestion_frames_by_video = {video: [] for video in self.videos}
+        if suggested:
+            for suggestion in self.suggestions:
+                suggestion_frames_by_video[suggestion.video].append(
+                    suggestion.frame_idx
+                )
+
         # Build list of frames to save.
         vids = []
         frame_idxs = []
         for video in self.videos:
             lfs_v = self.find(video)
-            frame_nums = [
+            frame_nums = {
                 lf.frame_idx
                 for lf in lfs_v
                 if all_labeled or (user_labeled and lf.has_user_instances)
-            ]
+            }
+
             if suggested:
-                frame_nums += [
-                    suggestion.frame_idx
-                    for suggestion in self.suggestions
-                    if suggestion.video == video
-                ]
-            frame_nums = sorted(list(set(frame_nums)))
+                frame_nums.update(suggestion_frames_by_video[video])
+
+            frame_nums = sorted(list(frame_nums))
             vids.append(video)
             frame_idxs.append(frame_nums)
 
diff --git a/sleap/io/format/csv.py b/sleap/io/format/csv.py
new file mode 100644
index 000000000..4640ee117
--- /dev/null
+++ b/sleap/io/format/csv.py
@@ -0,0 +1,70 @@
+"""Adaptor for writing SLEAP analysis as csv."""
+
+from sleap.io import format
+
+from sleap import Labels, Video
+from typing import Optional, Callable, List, Text, Union
+
+
+class CSVAdaptor(format.adaptor.Adaptor):
+    FORMAT_ID = 1.0
+
+    # 1.0 initial implementation
+
+    @property
+    def handles(self):
+        return format.adaptor.SleapObjectType.labels
+
+    @property
+    def default_ext(self):
+        return "csv"
+
+    @property
+    def all_exts(self):
+        return ["csv", "xlsx"]
+
+    @property
+    def name(self):
+        return "CSV"
+
+    def can_read_file(self, file: format.filehandle.FileHandle):
+        return False
+
+    def can_write_filename(self, filename: str):
+        return self.does_match_ext(filename)
+
+    def does_read(self) -> bool:
+        return False
+
+    def does_write(self) -> bool:
+        return True
+
+    @classmethod
+    def write(
+        cls,
+        filename: str,
+        source_object: Labels,
+        source_path: str = None,
+        video: Video = None,
+    ):
+        """Writes csv file for :py:class:`Labels` `source_object`.
+
+        Args:
+            filename: The filename for the output file.
+            source_object: The :py:class:`Labels` from which to get data from.
+            source_path: Path for the labels object
+            video: The :py:class:`Video` from which toget data from. If no `video` is
+                specified, then the first video in `source_object` videos list will be
+                used. If there are no :py:class:`Labeled Frame`s in the `video`, then no
+                analysis file will be written.
+        """
+        from sleap.info.write_tracking_h5 import main as write_analysis
+
+        write_analysis(
+            labels=source_object,
+            output_path=filename,
+            labels_path=source_path,
+            all_frames=True,
+            video=video,
+            csv=True,
+        )
diff --git a/sleap/io/format/dispatch.py b/sleap/io/format/dispatch.py
index e4803a87d..43f879627 100644
--- a/sleap/io/format/dispatch.py
+++ b/sleap/io/format/dispatch.py
@@ -5,6 +5,7 @@
 """
 
 import attr
+from pathlib import Path
 from typing import List, Optional, Tuple, Union
 
 from sleap.io.format.adaptor import Adaptor, SleapObjectType
@@ -77,7 +78,9 @@ def write(self, filename: str, source_object: object, *args, **kwargs):
             if adaptor.can_write_filename(filename):
                 return adaptor.write(filename, source_object, *args, **kwargs)
 
-        raise TypeError("No file format adaptor could write this file.")
+        raise TypeError(
+            f"No file format adaptor could write this file: {Path(filename).name}."
+        )
 
     def write_safely(self, *args, **kwargs) -> Optional[BaseException]:
         """Wrapper for writing file without throwing exception."""
diff --git a/sleap/io/format/labels_json.py b/sleap/io/format/labels_json.py
index 50fa7d18d..f284731a6 100644
--- a/sleap/io/format/labels_json.py
+++ b/sleap/io/format/labels_json.py
@@ -241,9 +241,11 @@ def write(
         compress: Optional[bool] = None,
         save_frame_data: bool = False,
         frame_data_format: str = "png",
+        all_labeled: bool = False,
+        suggested: bool = False,
+        progress_callback: Optional[Callable[[int, int], None]] = None,
     ):
-        """
-        Save a Labels instance to a JSON format.
+        """Save a Labels instance to a JSON format.
 
         Args:
             filename: The filename to save the data to.
@@ -276,6 +278,11 @@ def write(
                  Note: 'h264/mkv' and 'avc1/mp4' require separate installation
                  of these codecs on your system. They are excluded from SLEAP
                  because of their GPL license.
+            all_labeled: Whether to save all frames or just the labeled frames to use in
+                training.
+            suggested: Whether to save the suggested labels along with the training
+                labels.
+            progress_callback: A function that will be called with the current progress.
 
         Returns:
             None
@@ -299,7 +306,11 @@ def write(
                 # of the videos. We will only include the labeled frames though. We will
                 # then replace each video with this new video
                 new_videos = labels.save_frame_data_imgstore(
-                    output_dir=tmp_dir, format=frame_data_format
+                    output_dir=tmp_dir,
+                    format=frame_data_format,
+                    all_labeled=all_labeled,
+                    suggested=suggested,
+                    progress_callback=progress_callback,
                 )
 
                 # Make video paths relative
diff --git a/sleap/io/video.py b/sleap/io/video.py
index f8af330ec..b73569fa0 100644
--- a/sleap/io/video.py
+++ b/sleap/io/video.py
@@ -1273,7 +1273,9 @@ def from_filename(cls, filename: str, *args, **kwargs) -> "Video":
         elif filename.lower().endswith(SingleImageVideo.EXTS):
             backend_class = SingleImageVideo
         else:
-            raise ValueError("Could not detect backend for specified filename.")
+            raise ValueError(
+                f"Could not detect backend for specified filename: {filename}"
+            )
 
         kwargs["filename"] = filename
 
diff --git a/sleap/nn/__init__.py b/sleap/nn/__init__.py
index b3c4eacd3..648fd49ff 100644
--- a/sleap/nn/__init__.py
+++ b/sleap/nn/__init__.py
@@ -14,3 +14,6 @@
 import sleap.nn.tracking
 import sleap.nn.viz
 import sleap.nn.identity
+import os
+
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
diff --git a/sleap/nn/evals.py b/sleap/nn/evals.py
index ad8990b9f..002f8a143 100644
--- a/sleap/nn/evals.py
+++ b/sleap/nn/evals.py
@@ -25,7 +25,7 @@
 import numpy as np
 from typing import Any, Dict, List, Optional, Text, Tuple, Union
 import logging
-import sleap
+
 from sleap import Labels, LabeledFrame, Instance, PredictedInstance
 from sleap.nn.config import (
     TrainingJobConfig,
@@ -136,6 +136,7 @@ def compute_oks(
     points_pr: np.ndarray,
     scale: Optional[float] = None,
     stddev: float = 0.025,
+    use_cocoeval: bool = True,
 ) -> np.ndarray:
     """Compute the object keypoints similarity between sets of points.
 
@@ -145,6 +146,12 @@ def compute_oks(
             is the number of Euclidean dimensions (typically 2 or 3). Keypoints
             that are missing/not visible should be represented as NaNs.
         points_pr: Predicted instance of shape (n_pr, n_nodes, n_ed).
+        use_cocoeval: Indicates whether the OKS score is calculated like cocoeval
+            method or not. True indicating the score is calculated using the
+            cocoeval method (widely used and the code can be found here at
+            https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/cocoeval.py#L192C5-L233C20)
+            and False indicating the score is calculated using the method exactly
+            as given in the paper referenced in the Notes below.
         scale: Size scaling factor to use when weighing the scores, typically
             the area of the bounding box of the instance (in pixels). This
             should be of the length n_gt. If a scalar is provided, the same
@@ -203,8 +210,14 @@ def compute_oks(
     assert distance.shape == (n_gt, n_pr, n_nodes)
 
     # Compute the normalization factor per keypoint.
-    spread_factor = (2 * stddev) ** 2
-    scale_factor = 2 * (scale + np.spacing(1))
+    if use_cocoeval:
+        # If use_cocoeval is True, then compute normalization factor according to cocoeval.
+        spread_factor = (2 * stddev) ** 2
+        scale_factor = 2 * (scale + np.spacing(1))
+    else:
+        # If use_cocoeval is False, then compute normalization factor according to the paper.
+        spread_factor = stddev ** 2
+        scale_factor = 2 * ((scale + np.spacing(1)) ** 2)
     normalization_factor = np.reshape(spread_factor, (1, 1, n_nodes)) * np.reshape(
         scale_factor, (n_gt, 1, 1)
     )
@@ -471,7 +484,7 @@ def compute_generalized_voc_metrics(
 
 def compute_dists(
     positive_pairs: List[Tuple[Instance, PredictedInstance, Any]]
-) -> np.ndarray:
+) -> Dict[str, Union[np.ndarray, List[int], List[str]]]:
     """Compute Euclidean distances between matched pairs of instances.
 
     Args:
@@ -479,20 +492,37 @@ def compute_dists(
             containing the matched pair of instances.
 
     Returns:
-        An array of pairwise distances of shape `(n_positive_pairs, n_nodes)`.
+        A dictionary with the following keys:
+            dists: An array of pairwise distances of shape `(n_positive_pairs, n_nodes)`
+            frame_idxs: A list of frame indices corresponding to the `dists`
+            video_paths: A list of video paths corresponding to the `dists`
     """
     dists = []
+    frame_idxs = []
+    video_paths = []
     for instance_gt, instance_pr, _ in positive_pairs:
         points_gt = instance_gt.points_array
         points_pr = instance_pr.points_array
 
         dists.append(np.linalg.norm(points_pr - points_gt, axis=-1))
+        frame_idxs.append(instance_gt.frame.frame_idx)
+        video_paths.append(instance_gt.frame.video.backend.filename)
+
     dists = np.array(dists)
 
-    return dists
+    # Bundle everything into a dictionary
+    dists_dict = {
+        "dists": dists,
+        "frame_idxs": frame_idxs,
+        "video_paths": video_paths,
+    }
+
+    return dists_dict
 
 
-def compute_dist_metrics(dists: np.ndarray) -> Dict[Text, np.ndarray]:
+def compute_dist_metrics(
+    dists_dict: Dict[str, Union[np.ndarray, List[Instance]]]
+) -> Dict[Text, np.ndarray]:
     """Compute the Euclidean distance error at different percentiles.
 
     Args:
@@ -501,7 +531,10 @@ def compute_dist_metrics(dists: np.ndarray) -> Dict[Text, np.ndarray]:
     Returns:
         A dictionary of distance metrics.
     """
+    dists = dists_dict["dists"]
     results = {
+        "dist.frame_idxs": dists_dict["frame_idxs"],
+        "dist.video_paths": dists_dict["video_paths"],
         "dist.dists": dists,
         "dist.avg": np.nanmean(dists),
         "dist.p50": np.nan,
@@ -623,11 +656,11 @@ def evaluate(
         threshold=match_threshold,
         user_labels_only=user_labels_only,
     )
-    dists = compute_dists(positive_pairs)
+    dists_dict = compute_dists(positive_pairs)
 
     metrics.update(compute_visibility_conf(positive_pairs))
-    metrics.update(compute_dist_metrics(dists))
-    metrics.update(compute_pck_metrics(dists))
+    metrics.update(compute_dist_metrics(dists_dict))
+    metrics.update(compute_pck_metrics(dists_dict["dists"]))
 
     pair_oks = np.array([oks for _, _, oks in positive_pairs])
     pair_pck = metrics["pck.pcks"].mean(axis=-1).mean(axis=-1)
@@ -649,7 +682,7 @@ def evaluate(
 
 def evaluate_model(
     cfg: TrainingJobConfig,
-    labels_reader: LabelsReader,
+    labels_gt: Union[LabelsReader, Labels],
     model: Model,
     save: bool = True,
     split_name: Text = "test",
@@ -658,8 +691,8 @@ def evaluate_model(
 
     Args:
         cfg: The `TrainingJobConfig` associated with the model.
-        labels_reader: A `LabelsReader` pipeline generator that reads the ground truth
-            data to evaluate.
+        labels_gt: A `LabelsReader` pipeline generator that reads the ground truth
+            data to evaluate or a `Labels` object to be used as ground truth.
         model: The `sleap.nn.model.Model` instance to evaluate.
         save: If True, save the predictions and metrics to the model folder.
         split_name: String name to append to the saved filenames.
@@ -708,11 +741,13 @@ def evaluate_model(
         raise ValueError("Unrecognized model type:", head_config)
 
     # Predict.
-    labels_pr = predictor.predict(labels_reader, make_labels=True)
+    labels_pr: Labels = predictor.predict(labels_gt, make_labels=True)
 
     # Compute metrics.
     try:
-        metrics = evaluate(labels_reader.labels, labels_pr)
+        if isinstance(labels_gt, LabelsReader):
+            labels_gt = labels_gt.labels
+        metrics = evaluate(labels_gt, labels_pr)
     except:
         logger.warning("Failed to compute metrics.")
         metrics = None
@@ -763,6 +798,8 @@ def load_metrics(model_path: str, split: str = "val") -> Dict[str, Any]:
         - `"dist.p95"`: Distance for 95th percentile
         - `"dist.p99"`: Distance for 99th percentile
         - `"dist.dists"`: All distances
+        - `"dist.frame_idxs"`: Frame indices corresponding to `"dist.dists"`
+        - `"dist.video_paths"`: Video paths corresponding to `"dist.dists"`
         - `"pck.mPCK"`: Mean Percentage of Correct Keypoints (PCK)
         - `"oks.mOKS"`: Mean Object Keypoint Similarity (OKS)
         - `"oks_voc.mAP"`: VOC with OKS scores - mean Average Precision (mAP)
diff --git a/sleap/nn/inference.py b/sleap/nn/inference.py
index 24c2ce5f5..6d7d24f8c 100644
--- a/sleap/nn/inference.py
+++ b/sleap/nn/inference.py
@@ -68,7 +68,7 @@
 )
 from sleap.nn.utils import reset_input_layer
 from sleap.io.dataset import Labels
-from sleap.util import frame_list
+from sleap.util import frame_list, make_scoped_dictionary
 from sleap.instance import PredictedInstance, LabeledFrame
 
 from tensorflow.python.framework.convert_to_constants import (
@@ -4773,8 +4773,7 @@ def load_model(
             be performed.
         tracker_window: Number of frames of history to use when tracking. No effect when
             `tracker` is `None`.
-        tracker_max_instances: If not `None`, discard instances beyond this count when
-            tracking. No effect when `tracker` is `None`.
+        tracker_max_instances: If not `None`, create at most this many tracks.
         disable_gpu_preallocation: If `True` (the default), initialize the GPU and
             disable preallocation of memory. This is necessary to prevent freezing on
             some systems with low GPU memory and has negligible impact on performance.
@@ -4824,6 +4823,7 @@ def unpack_sleap_model(model_path):
         # Uncompress ZIP packaged models.
         tmp_dirs = []
         for i, model_path in enumerate(model_paths):
+            mp = Path(model_path)
             if model_path.endswith(".zip"):
                 # Create temp dir on demand.
                 tmp_dir = tempfile.TemporaryDirectory()
@@ -4834,7 +4834,12 @@ def unpack_sleap_model(model_path):
 
                 # Extract and replace in the list.
                 shutil.unpack_archive(model_path, extract_dir=tmp_dir.name)
-                model_paths[i] = tmp_dir.name
+                unzipped_mp = Path(tmp_dir.name, mp.name).with_suffix("")
+                if Path(unzipped_mp, "best_model.h5").exists():
+                    unzipped_model_path = str(unzipped_mp)
+                else:
+                    unzipped_model_path = str(unzipped_mp.parent)
+                model_paths[i] = unzipped_model_path
 
         return model_paths, tmp_dirs
 
@@ -4857,11 +4862,18 @@ def unpack_sleap_model(model_path):
     )
     predictor.verbosity = progress_reporting
     if tracker is not None:
+        use_max_tracker = tracker_max_instances is not None
+        if use_max_tracker and not tracker.endswith("maxtracks"):
+            # Append maxtracks to the tracker name to use the right tracker variants.
+            tracker += "maxtracks"
+
         predictor.tracker = Tracker.make_tracker_by_name(
             tracker=tracker,
             track_window=tracker_window,
             post_connect_single_breaks=True,
-            clean_instance_count=tracker_max_instances,
+            max_tracking=use_max_tracker,
+            max_tracks=tracker_max_instances,
+            # clean_instance_count=tracker_max_instances,
         )
 
     # Remove temp dirs.
@@ -5329,7 +5341,7 @@ def _make_tracker_from_cli(args: argparse.Namespace) -> Optional[Tracker]:
     Returns:
         An instance of `Tracker` or `None` if tracking method was not specified.
     """
-    policy_args = sleap.util.make_scoped_dictionary(vars(args), exclude_nones=True)
+    policy_args = make_scoped_dictionary(vars(args), exclude_nones=True)
     if "tracking" in policy_args:
         tracker = Tracker.make_tracker_by_name(**policy_args["tracking"])
         return tracker
diff --git a/sleap/nn/system.py b/sleap/nn/system.py
index 24b4c14b3..eeb3f3ca4 100644
--- a/sleap/nn/system.py
+++ b/sleap/nn/system.py
@@ -195,6 +195,7 @@ def get_gpu_memory() -> List[int]:
         A list of the available memory on each GPU in MiB.
 
     """
+
     if shutil.which("nvidia-smi") is None:
         return []
 
diff --git a/sleap/nn/tracking.py b/sleap/nn/tracking.py
index b861c359f..9865b7db5 100644
--- a/sleap/nn/tracking.py
+++ b/sleap/nn/tracking.py
@@ -88,6 +88,13 @@ class MatchedFrameInstances:
     img_t: Optional[np.ndarray] = None
 
 
+@attr.s(auto_attribs=True, slots=True)
+class MatchedFrameInstance:
+    t: int
+    instance_t: InstanceType
+    img_t: Optional[np.ndarray] = None
+
+
 @attr.s(auto_attribs=True, slots=True)
 class MatchedShiftedFrameInstances:
     ref_t: int
@@ -132,6 +139,66 @@ class FlowCandidateMaker:
     def uses_image(self):
         return True
 
+    def get_shifted_instances_from_earlier_time(
+        self, ref_t: int, ref_img: np.ndarray, ref_instances: List[InstanceType], t: int
+    ) -> (np.ndarray, List[InstanceType]):
+        """Generate shifted instances and corresponding image from earlier time.
+
+        Args:
+            ref_instances: Reference instances in the previous frame.
+            ref_img: Previous frame image as a numpy array.
+            ref_t: Previous frame time instance.
+            t: Current time instance.
+        """
+        for ti in reversed(range(ref_t, t)):
+            if (ref_t, ti) in self.shifted_instances:
+                ref_shifted_instances = self.shifted_instances[(ref_t, ti)]
+                # Use shifted instance as a reference
+                if len(ref_shifted_instances.instances_t) > 0:
+                    ref_img = ref_shifted_instances.img_t
+                    ref_instances = ref_shifted_instances.instances_t
+                    break
+        return [ref_img, ref_instances]
+
+    def get_shifted_instances(
+        self,
+        ref_instances: List[InstanceType],
+        ref_img: np.ndarray,
+        ref_t: int,
+        img: np.ndarray,
+        t: int,
+    ) -> List[ShiftedInstance]:
+        """Returns a list of shifted instances and save shifted instances if needed.
+
+        Args:
+            ref_instances: Reference instances in the previous frame.
+            ref_img: Previous frame image as a numpy array.
+            ref_t: Previous frame time instance.
+            img: Current frame image as a numpy array.
+            t: Current time instance.
+        """
+        # Flow shift reference instances to current frame.
+        shifted_instances = self.flow_shift_instances(
+            ref_instances,
+            ref_img,
+            img,
+            min_shifted_points=self.min_points,
+            scale=self.img_scale,
+            window_size=self.of_window_size,
+            max_levels=self.of_max_levels,
+        )
+
+        # Save shifted instances.
+        if self.save_shifted_instances:
+            self.shifted_instances[(ref_t, t)] = MatchedShiftedFrameInstances(
+                ref_t,
+                t,
+                shifted_instances,
+                img,
+            )
+
+        return shifted_instances
+
     def get_candidates(
         self,
         track_matching_queue: Deque[MatchedFrameInstances],
@@ -152,39 +219,15 @@ def get_candidates(
 
             # Check if shifted instance was computed at earlier time
             if self.save_shifted_instances:
-                for ti in reversed(range(ref_t, t)):
-                    if (ref_t, ti) in self.shifted_instances:
-                        ref_shifted_instances = self.shifted_instances[(ref_t, ti)]
-                        # Use shifted instance as a reference
-                        if len(ref_shifted_instances.instances_t) > 0:
-                            ref_img = ref_shifted_instances.img_t
-                            ref_instances = ref_shifted_instances.instances_t
-                            break
+                ref_img, ref_instances = self.get_shifted_instances_from_earlier_time(
+                    ref_t, ref_img, ref_instances, t
+                )
 
             if len(ref_instances) > 0:
-                # Flow shift reference instances to current frame.
-                shifted_instances = self.flow_shift_instances(
-                    ref_instances,
-                    ref_img,
-                    img,
-                    min_shifted_points=self.min_points,
-                    scale=self.img_scale,
-                    window_size=self.of_window_size,
-                    max_levels=self.of_max_levels,
+                candidate_instances.extend(
+                    self.get_shifted_instances(ref_instances, ref_img, ref_t, img, t)
                 )
 
-                # Add to candidate pool.
-                candidate_instances.extend(shifted_instances)
-
-                # Save shifted instances.
-                if self.save_shifted_instances:
-                    self.shifted_instances[(ref_t, t)] = MatchedShiftedFrameInstances(
-                        ref_t,
-                        t,
-                        shifted_instances,
-                        img,
-                    )
-
         return candidate_instances
 
     def prune_shifted_instances(self, t: int):
@@ -311,6 +354,86 @@ def flow_shift_instances(
         return shifted_instances
 
 
+@attr.s(auto_attribs=True)
+class FlowMaxTracksCandidateMaker(FlowCandidateMaker):
+    """Class for producing optical flow shift matching candidates with maximum tracks.
+
+    Attributes:
+        max_tracks: The maximum number of tracks to avoid redundant tracks.
+
+    """
+
+    max_tracks: int = None
+
+    @staticmethod
+    def get_ref_instances(
+        ref_t: int,
+        ref_img: np.ndarray,
+        track_matching_queue_dict: Dict[Track, Deque[MatchedFrameInstance]],
+    ) -> List[InstanceType]:
+        """Generates a list of instances based on the reference time and image.
+
+        Args:
+            ref_t: Previous frame time instance.
+            ref_img: Previous frame image as a numpy array.
+            track_matching_queue_dict: A dictionary of mapping between the tracks
+                and the corresponding instances associated with the track.
+        """
+        instances = []
+        for track, matched_items in track_matching_queue_dict.items():
+            instances += [
+                item.instance_t
+                for item in matched_items
+                if item.t == ref_t and np.all(item.img_t == ref_img)
+            ]
+        return instances
+
+    def get_candidates(
+        self,
+        track_matching_queue_dict: Dict[Track, Deque[MatchedFrameInstance]],
+        t: int,
+        img: np.ndarray,
+        *args,
+        **kwargs,
+    ) -> List[ShiftedInstance]:
+        candidate_instances = []
+
+        # Prune old shifted instances to save time and memory
+        self.prune_shifted_instances(t)
+        # Storing the tracks from the dictionary for counting purpose.
+        tracks = []
+
+        for track, matched_items in track_matching_queue_dict.items():
+            if len(tracks) <= self.max_tracks:
+                tracks.append(track)
+                for matched_item in matched_items:
+                    ref_t, ref_img = (
+                        matched_item.t,
+                        matched_item.img_t,
+                    )
+                    ref_instances = self.get_ref_instances(
+                        ref_t, ref_img, track_matching_queue_dict
+                    )
+
+                    # Check if shifted instance was computed at earlier time
+                    if self.save_shifted_instances:
+                        (
+                            ref_img,
+                            ref_instances,
+                        ) = self.get_shifted_instances_from_earlier_time(
+                            ref_t, ref_img, ref_instances, t
+                        )
+
+                    if len(ref_instances) > 0:
+                        candidate_instances.extend(
+                            self.get_shifted_instances(
+                                ref_instances, ref_img, ref_t, img, t
+                            )
+                        )
+
+        return candidate_instances
+
+
 @attr.s(auto_attribs=True)
 class SimpleCandidateMaker:
     """Class for producing list of matching candidates from prior frames."""
@@ -334,9 +457,35 @@ def get_candidates(
         return candidate_instances
 
 
+@attr.s(auto_attribs=True)
+class SimpleMaxTracksCandidateMaker(SimpleCandidateMaker):
+    """Class to generate instances with maximum number of tracks from prior frames."""
+
+    max_tracks: int = None
+
+    def get_candidates(
+        self,
+        track_matching_queue_dict: Dict,
+        *args,
+        **kwargs,
+    ) -> List[InstanceType]:
+        # Create set of matchable candidate instances from each track.
+        candidate_instances = []
+        tracks = []
+        for track, matched_instances in track_matching_queue_dict.items():
+            if len(tracks) <= self.max_tracks:
+                tracks.append(track)
+                for ref_instance in matched_instances:
+                    if ref_instance.instance_t.n_visible_points >= self.min_points:
+                        candidate_instances.append(ref_instance.instance_t)
+        return candidate_instances
+
+
 tracker_policies = dict(
     simple=SimpleCandidateMaker,
     flow=FlowCandidateMaker,
+    simplemaxtracks=SimpleMaxTracksCandidateMaker,
+    flowmaxtracks=FlowMaxTracksCandidateMaker,
 )
 
 similarity_policies = dict(
@@ -407,14 +556,17 @@ class Tracker(BaseTracker):
             use a robust quantile similarity score for the track. If the value is 1,
             use the max similarity (non-robust). For selecting a robust score,
             0.95 is a good value.
+        max_tracking: Max tracking is incorporated when this is set to true.
     """
 
+    max_tracks: int = None
     track_window: int = 5
     similarity_function: Optional[Callable] = instance_similarity
     matching_function: Callable = greedy_matching
     candidate_maker: object = attr.ib(factory=FlowCandidateMaker)
+    max_tracking: bool = False  # To enable maximum tracking.
 
-    cleaner: Optional[Callable] = None  # todo: deprecate
+    cleaner: Optional[Callable] = None  # TODO: deprecate
     target_instance_count: int = 0
     pre_cull_function: Optional[Callable] = None
     post_connect_single_breaks: bool = False
@@ -424,6 +576,10 @@ class Tracker(BaseTracker):
 
     track_matching_queue: Deque[MatchedFrameInstances] = attr.ib()
 
+    # Hold track, instances with instances as a deque with length as track_window.
+    track_matching_queue_dict: Dict[Track, Deque[MatchedFrameInstance]] = attr.ib(
+        factory=dict
+    )
     spawned_tracks: List[Track] = attr.ib(factory=list)
 
     save_tracked_instances: bool = False
@@ -443,7 +599,11 @@ def _init_matching_queue(self):
         return deque(maxlen=self.track_window)
 
     def reset_candidates(self):
-        self.track_matching_queue = deque(maxlen=self.track_window)
+        if self.max_tracking:
+            for track in self.track_matching_queue_dict:
+                self.track_matching_queue_dict[track] = deque(maxlen=self.track_window)
+        else:
+            self.track_matching_queue = deque(maxlen=self.track_window)
 
     @property
     def unique_tracks_in_queue(self) -> List[Track]:
@@ -454,6 +614,10 @@ def unique_tracks_in_queue(self) -> List[Track]:
             for instance in match_item.instances_t:
                 unique_tracks.add(instance.track)
 
+        if self.max_tracking:
+            for track in self.track_matching_queue_dict.keys():
+                unique_tracks.add(track)
+
         return list(unique_tracks)
 
     @property
@@ -482,13 +646,30 @@ def track(
 
         # Infer timestep if not provided.
         if t is None:
-            if len(self.track_matching_queue) > 0:
-
-                # Default to last timestep + 1 if available.
-                t = self.track_matching_queue[-1].t + 1
+            if self.max_tracking:
+                if len(self.track_matching_queue_dict) > 0:
+
+                    # Default to last timestep + 1 if available.
+                    # Here we find the track that has the most instances.
+                    track_with_max_instances = max(
+                        self.track_matching_queue_dict,
+                        key=lambda track: len(self.track_matching_queue_dict[track]),
+                    )
+                    t = (
+                        self.track_matching_queue_dict[track_with_max_instances][-1].t
+                        + 1
+                    )
 
+                else:
+                    t = 0
             else:
-                t = 0
+                if len(self.track_matching_queue) > 0:
+
+                    # Default to last timestep + 1 if available.
+                    t = self.track_matching_queue[-1].t + 1
+
+                else:
+                    t = 0
 
         # Initialize containers for tracked instances at the current timestep.
         tracked_instances = []
@@ -503,11 +684,19 @@ def track(
                 self.pre_cull_function(untracked_instances)
 
             # Build a pool of matchable candidate instances.
-            candidate_instances = self.candidate_maker.get_candidates(
-                track_matching_queue=self.track_matching_queue,
-                t=t,
-                img=img,
-            )
+            if self.max_tracking:
+                candidate_instances = self.candidate_maker.get_candidates(
+                    track_matching_queue_dict=self.track_matching_queue_dict,
+                    max_tracks=self.max_tracks,
+                    t=t,
+                    img=img,
+                )
+            else:
+                candidate_instances = self.candidate_maker.get_candidates(
+                    track_matching_queue=self.track_matching_queue,
+                    t=t,
+                    img=img,
+                )
 
             # Determine matches for untracked instances in current frame.
             frame_matches = FrameMatches.from_candidate_instances(
@@ -531,10 +720,26 @@ def track(
                 self.spawn_for_untracked_instances(frame_matches.unmatched_instances, t)
             )
 
-        # Add the tracked instances to the matching buffer.
-        self.track_matching_queue.append(
-            MatchedFrameInstances(t, tracked_instances, img)
-        )
+        # Add the tracked instances to the dictionary of matched instances.
+        if self.max_tracking:
+            for tracked_instance in tracked_instances:
+                if tracked_instance.track in self.track_matching_queue_dict:
+                    self.track_matching_queue_dict[tracked_instance.track].append(
+                        MatchedFrameInstance(t, tracked_instance, img)
+                    )
+                elif len(self.track_matching_queue_dict) < self.max_tracks:
+                    self.track_matching_queue_dict[tracked_instance.track] = deque(
+                        maxlen=self.track_window
+                    )
+                    self.track_matching_queue_dict[tracked_instance.track].append(
+                        MatchedFrameInstance(t, tracked_instance, img)
+                    )
+
+        else:
+            # Add the tracked instances to the matching buffer.
+            self.track_matching_queue.append(
+                MatchedFrameInstances(t, tracked_instances, img)
+            )
 
         # Save tracked instances internally.
         if self.save_tracked_instances:
@@ -566,6 +771,13 @@ def spawn_for_untracked_instances(
             if inst.n_visible_points < self.min_new_track_points:
                 continue
 
+            # Skip if we've reached the maximum number of tracks.
+            if (
+                self.max_tracking
+                and len(self.track_matching_queue_dict) >= self.max_tracks
+            ):
+                break
+
             # Spawn new track.
             new_track = Track(spawned_on=t, name=f"track_{len(self.spawned_tracks)}")
             self.spawned_tracks.append(new_track)
@@ -598,6 +810,7 @@ def get_name(self):
     @classmethod
     def make_tracker_by_name(
         cls,
+        # Tracker options
         tracker: str = "flow",
         similarity: str = "instance",
         match: str = "greedy",
@@ -622,6 +835,9 @@ def make_tracker_by_name(
         # Kalman filter options
         kf_init_frame_count: int = 0,
         kf_node_indices: Optional[list] = None,
+        # Max tracking options
+        max_tracks: Optional[int] = None,
+        max_tracking: bool = False,
         **kwargs,
     ) -> BaseTracker:
 
@@ -652,6 +868,9 @@ def make_tracker_by_name(
             candidate_maker.save_shifted_instances = save_shifted_instances
             candidate_maker.track_window = track_window
 
+        if tracker == "simplemaxtracks" or tracker == "flowmaxtracks":
+            candidate_maker.max_tracks = max_tracks
+
         cleaner = None
         if clean_instance_count:
             cleaner = TrackCleaner(
@@ -677,6 +896,8 @@ def pre_cull_function(inst_list):
             candidate_maker=candidate_maker,
             cleaner=cleaner,
             pre_cull_function=pre_cull_function,
+            max_tracking=max_tracking,
+            max_tracks=max_tracks,
             target_instance_count=target_instance_count,
             post_connect_single_breaks=post_connect_single_breaks,
         )
@@ -708,6 +929,16 @@ def get_by_name_factory_options(cls):
         ]
         options.append(option)
 
+        option = dict(name="max_tracking", default=False)
+        option["type"] = bool
+        option["help"] = "If true then the tracker will cap the max number of tracks."
+        options.append(option)
+
+        option = dict(name="max_tracks", default=None)
+        option["type"] = int
+        option["help"] = "Maximum number of tracks to be tracked by the tracker."
+        options.append(option)
+
         option = dict(name="target_instance_count", default=0)
         option["type"] = int
         option["help"] = "Target number of instances to track per frame."
@@ -854,6 +1085,19 @@ class FlowTracker(Tracker):
     candidate_maker: object = attr.ib(factory=FlowCandidateMaker)
 
 
+attr.s(auto_attribs=True)
+
+
+class FlowMaxTracker(Tracker):
+    """Pre-configured tracker to use optical flow shifted candidates with max tracks."""
+
+    max_tracks: int = attr.ib(kw_only=True)
+    similarity_function: Callable = instance_similarity
+    matching_function: Callable = greedy_matching
+    candidate_maker: object = attr.ib(factory=FlowMaxTracksCandidateMaker)
+    max_tracking: bool = True
+
+
 @attr.s(auto_attribs=True)
 class SimpleTracker(Tracker):
     """A Tracker pre-configured to use simple, non-image-based candidates."""
@@ -863,6 +1107,17 @@ class SimpleTracker(Tracker):
     candidate_maker: object = attr.ib(factory=SimpleCandidateMaker)
 
 
+@attr.s(auto_attribs=True)
+class SimpleMaxTracker(Tracker):
+    """Pre-configured tracker to use simple, non-image-based candidates with max tracks."""
+
+    max_tracks: int = attr.ib(kw_only=True)
+    similarity_function: Callable = instance_iou
+    matching_function: Callable = hungarian_matching
+    candidate_maker: object = attr.ib(factory=SimpleMaxTracksCandidateMaker)
+    max_tracking: bool = True
+
+
 @attr.s(auto_attribs=True)
 class KalmanInitSet:
     init_frame_count: int
diff --git a/sleap/nn/training.py b/sleap/nn/training.py
index 21beb802b..16f027175 100644
--- a/sleap/nn/training.py
+++ b/sleap/nn/training.py
@@ -1,85 +1,83 @@
 """Training functionality and high level APIs."""
 
+import copy
+import json
+import logging
 import os
+import platform
 import re
+import shutil
+from abc import ABC, abstractmethod
 from datetime import datetime
 from time import time
-import logging
-import shutil
-import platform
-
-import tensorflow as tf
-import numpy as np
+from typing import Callable, List, Optional, Text, TypeVar, Union
 
 import attr
-from typing import Optional, Callable, List, Union, Text, TypeVar
-from abc import ABC, abstractmethod
-
 import cattr
-import json
-import copy
+
+# Visualization
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.callbacks import (
+    CSVLogger,
+    EarlyStopping,
+    ModelCheckpoint,
+    ReduceLROnPlateau,
+    TensorBoard,
+)
 
 import sleap
 from sleap import Labels
-from sleap.util import get_package_file
+from sleap.nn.callbacks import (
+    MatplotlibSaver,
+    ModelCheckpointOnEvent,
+    ProgressReporterZMQ,
+    TensorBoardMatplotlibWriter,
+    TrainingControllerZMQ,
+)
 
+# Outputs
+# Optimization
+# Data
 # Config
 from sleap.nn.config import (
-    TrainingJobConfig,
-    SingleInstanceConfmapsHeadConfig,
-    CentroidsHeadConfig,
     CenteredInstanceConfmapsHeadConfig,
-    MultiInstanceConfig,
+    CentroidsHeadConfig,
+    CheckpointingConfig,
+    LabelsConfig,
     MultiClassBottomUpConfig,
     MultiClassTopDownConfig,
+    MultiInstanceConfig,
+    OptimizationConfig,
+    OutputsConfig,
+    SingleInstanceConfmapsHeadConfig,
+    TensorBoardConfig,
+    TrainingJobConfig,
+    ZMQConfig,
 )
-
-# Model
-from sleap.nn.model import Model
-
-# Data
-from sleap.nn.config import LabelsConfig
-from sleap.nn.data.pipelines import LabelsReader
 from sleap.nn.data.pipelines import (
+    BottomUpMultiClassPipeline,
+    BottomUpPipeline,
+    CentroidConfmapsPipeline,
+    KeyMapper,
+    LabelsReader,
     Pipeline,
     SingleInstanceConfmapsPipeline,
-    CentroidConfmapsPipeline,
     TopdownConfmapsPipeline,
-    BottomUpPipeline,
-    BottomUpMultiClassPipeline,
     TopDownMultiClassPipeline,
-    KeyMapper,
 )
 from sleap.nn.data.training import split_labels_train_val
 
-# Optimization
-from sleap.nn.config import OptimizationConfig
-from sleap.nn.losses import OHKMLoss, PartLoss
-from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
-
-# Outputs
-from sleap.nn.config import (
-    OutputsConfig,
-    ZMQConfig,
-    TensorBoardConfig,
-    CheckpointingConfig,
-)
-from sleap.nn.callbacks import (
-    TrainingControllerZMQ,
-    ProgressReporterZMQ,
-    ModelCheckpointOnEvent,
-)
-from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint, CSVLogger
-
 # Inference
 from sleap.nn.inference import FindInstancePeaks, SingleInstanceInferenceLayer
+from sleap.nn.losses import OHKMLoss, PartLoss
 
-# Visualization
-import matplotlib
-import matplotlib.pyplot as plt
-from sleap.nn.callbacks import TensorBoardMatplotlibWriter, MatplotlibSaver
-from sleap.nn.viz import plot_img, plot_confmaps, plot_peaks, plot_pafs
-
+# Model
+from sleap.nn.model import Model
+from sleap.nn.viz import plot_confmaps, plot_img, plot_pafs, plot_peaks
+from sleap.util import get_package_file
 
 logger = logging.getLogger(__name__)
 
@@ -962,14 +960,14 @@ def evaluate(self):
         logger.info("Saving evaluation metrics to model folder...")
         sleap.nn.evals.evaluate_model(
             cfg=self.config,
-            labels_reader=self.data_readers.training_labels_reader,
+            labels_gt=self.data_readers.training_labels_reader,
             model=self.model,
             save=True,
             split_name="train",
         )
         sleap.nn.evals.evaluate_model(
             cfg=self.config,
-            labels_reader=self.data_readers.validation_labels_reader,
+            labels_gt=self.data_readers.validation_labels_reader,
             model=self.model,
             save=True,
             split_name="val",
@@ -977,7 +975,7 @@ def evaluate(self):
         if self.data_readers.test_labels_reader is not None:
             sleap.nn.evals.evaluate_model(
                 cfg=self.config,
-                labels_reader=self.data_readers.test_labels_reader,
+                labels_gt=self.data_readers.test_labels_reader,
                 model=self.model,
                 save=True,
                 split_name="test",
@@ -1913,7 +1911,7 @@ def create_trainer_using_cli(args: Optional[List] = None):
     # Find job configuration file.
     job_filename = args.training_job_path
     if not os.path.exists(job_filename):
-        profile_dir = get_package_file("sleap/training_profiles")
+        profile_dir = get_package_file("training_profiles")
 
         if os.path.exists(os.path.join(profile_dir, job_filename)):
             job_filename = os.path.join(profile_dir, job_filename)
diff --git a/sleap/util.py b/sleap/util.py
index d3a3073c2..5edbf164b 100644
--- a/sleap/util.py
+++ b/sleap/util.py
@@ -4,26 +4,30 @@
 """
 
 import base64
-from collections import defaultdict
-from io import BytesIO
 import json
 import os
-from pathlib import Path
 import re
 import shutil
+from collections import defaultdict
+from io import BytesIO
+from pathlib import Path
 from typing import Any, Dict, Hashable, Iterable, List, Optional
-from urllib.request import url2pathname
 from urllib.parse import unquote, urlparse
+from urllib.request import url2pathname
 
 import attr
 import h5py as h5
 import numpy as np
-from PIL import Image
-from pkg_resources import Requirement, resource_filename
 import psutil
 import rapidjson
 import yaml
 
+try:
+    from importlib.resources import files  # New in 3.9+
+except ImportError:
+    from importlib_resources import files  # TODO(LM): Upgrade to importlib.resources.
+from PIL import Image
+
 import sleap.version as sleap_version
 
 
@@ -237,9 +241,9 @@ def dict_cut(d: Dict, a: int, b: int) -> Dict:
 
 def get_package_file(filename: str) -> str:
     """Returns full path to specified file within sleap package."""
-    package_path = Requirement.parse("sleap")
-    result = resource_filename(package_path, filename)
-    return result
+
+    data_path: Path = files("sleap").joinpath(filename)
+    return data_path.as_posix()
 
 
 def get_config_file(
@@ -266,6 +270,8 @@ def get_config_file(
         The full path to the specified config file.
     """
 
+    desired_path = None  # Handle case where get_defaults, but cannot find package_path
+
     if not get_defaults:
         desired_path = os.path.expanduser(
             f"~/.sleap/{sleap_version.__version__}/{shortname}"
@@ -286,7 +292,7 @@ def get_config_file(
     # config file if we can't find the user version.
 
     if get_defaults or not os.path.exists(desired_path):
-        package_path = get_package_file(f"sleap/config/{shortname}")
+        package_path = get_package_file(f"config/{shortname}")
         if not os.path.exists(package_path):
             raise FileNotFoundError(
                 f"Cannot locate {shortname} config file at {desired_path} or {package_path}."
diff --git a/sleap/version.py b/sleap/version.py
index a4e2cec7d..ffa7b55b9 100644
--- a/sleap/version.py
+++ b/sleap/version.py
@@ -12,7 +12,7 @@
 """
 
 
-__version__ = "1.3.1"
+__version__ = "1.3.2"
 
 
 def versions():
diff --git a/tests/data/csv_format/minimal_instance.000_centered_pair_low_quality.analysis.csv b/tests/data/csv_format/minimal_instance.000_centered_pair_low_quality.analysis.csv
new file mode 100644
index 000000000..83d3259be
--- /dev/null
+++ b/tests/data/csv_format/minimal_instance.000_centered_pair_low_quality.analysis.csv
@@ -0,0 +1,2 @@
+track,frame_idx,instance.score,A.x,A.y,A.score,B.x,B.y,B.score
+,0,nan,205.9300539013689,187.88964024221963,,278.63521449272383,203.3658657346604,
diff --git a/tests/fixtures/datasets.py b/tests/fixtures/datasets.py
index b8d438fb6..801fcc092 100644
--- a/tests/fixtures/datasets.py
+++ b/tests/fixtures/datasets.py
@@ -26,6 +26,9 @@
 TEST_HDF5_PREDICTIONS = "tests/data/hdf5_format_v1/centered_pair_predictions.h5"
 TEST_SLP_PREDICTIONS = "tests/data/hdf5_format_v1/centered_pair_predictions.slp"
 TEST_MIN_DANCE_LABELS = "tests/data/slp_hdf5/dance.mp4.labels.slp"
+TEST_CSV_PREDICTIONS = (
+    "tests/data/csv_format/minimal_instance.000_centered_pair_low_quality.analysis.csv"
+)
 
 
 @pytest.fixture
@@ -247,6 +250,11 @@ def centered_pair_predictions_hdf5_path():
     return TEST_HDF5_PREDICTIONS
 
 
+@pytest.fixture
+def minimal_instance_predictions_csv_path():
+    return TEST_CSV_PREDICTIONS
+
+
 @pytest.fixture
 def centered_pair_predictions_slp_path():
     return TEST_SLP_PREDICTIONS
diff --git a/tests/fixtures/instances.py b/tests/fixtures/instances.py
index 862577457..78e8f35b8 100644
--- a/tests/fixtures/instances.py
+++ b/tests/fixtures/instances.py
@@ -1,16 +1,18 @@
 import pytest
 
-from sleap.instance import Instance, Point, PredictedInstance
+from sleap.instance import Instance, LabeledFrame, Point, PredictedInstance
 
 
 @pytest.fixture
-def instances(skeleton):
+def instances(skeleton, centered_pair_vid):
 
     # Generate some instances
     NUM_INSTANCES = 500
 
+    video = centered_pair_vid
     instances = []
     for i in range(NUM_INSTANCES):
+
         instance = Instance(skeleton=skeleton)
         instance["head"] = Point(i * 1, i * 2)
         instance["left-wing"] = Point(10 + i * 1, 10 + i * 2)
@@ -19,6 +21,10 @@ def instances(skeleton):
         # Lets make an NaN entry to test skip_nan as well
         instance["thorax"]
 
+        # Add a LabeledFrame
+        labeled_frame = LabeledFrame(video=video, frame_idx=i, instances=[instance])
+        instance.frame = labeled_frame
+
         instances.append(instance)
 
     return instances
diff --git a/tests/gui/test_app.py b/tests/gui/test_app.py
index 66b0dafbb..bacda4ae3 100644
--- a/tests/gui/test_app.py
+++ b/tests/gui/test_app.py
@@ -240,9 +240,12 @@ def assert_frame_chunk_suggestion_ui_updated(
 
     # Set up to test labeled frames data cache
     app.labels = min_tracks_2node_labels
-    video = app.labels.video
+    video_clip = app.labels.video
+    app.state["labels"] = app.labels
+    app.state["video"] = video_clip
+    app.on_data_update([UpdateTopic.all])
     num_samples = 5
-    frame_delta = video.num_frames // num_samples
+    frame_delta = video_clip.num_frames // num_samples
 
     # Add suggestions
     app.labels.suggestions = VideoFrameSuggestions.suggest(
@@ -274,7 +277,7 @@ def assert_frame_chunk_suggestion_ui_updated(
             (l_suggestion.video, l_suggestion.frame_idx), use_cache=True
         )
         assert type(lf) == LabeledFrame
-        assert lf.video == video
+        assert lf.video == video_clip
         assert lf.frame_idx == prev_idx + frame_delta
         prev_idx = l_suggestion.frame_idx
 
@@ -284,8 +287,6 @@ def assert_frame_chunk_suggestion_ui_updated(
 
     assert len(app.labels.videos) == 2
 
-    app.state["video"] = centered_pair_vid
-
     # Generate suggested frames in both videos
     app.labels.clear_suggestions()
     num_samples = 3
@@ -311,11 +312,11 @@ def assert_frame_chunk_suggestion_ui_updated(
     assert app.state["selected_video"] == small_robot_mp4_vid
     app.commands.removeVideo()
     assert len(app.labels.videos) == 1
-    assert app.state["video"] == centered_pair_vid
+    assert app.state["video"] == video_clip
 
     # Verify frame suggestions from video 1 are removed
     for sugg in app.labels.suggestions:
-        assert sugg.video == app.labels.videos[0]
+        assert sugg.video == video_clip
 
 
 def test_app_new_window(qtbot):
diff --git a/tests/gui/test_commands.py b/tests/gui/test_commands.py
index bfa92ea1a..13aa60e6b 100644
--- a/tests/gui/test_commands.py
+++ b/tests/gui/test_commands.py
@@ -1,16 +1,18 @@
-from pathlib import PurePath, Path
+import pytest
 import shutil
 import sys
-from typing import List
+import time
 
-import pytest
-from qtpy.QtWidgets import QComboBox
+from pathlib import PurePath, Path
+from typing import List
 
-from sleap import Skeleton, Track
+from sleap import Skeleton, Track, PredictedInstance
 from sleap.gui.commands import (
     CommandContext,
-    ImportDeepLabCutFolder,
     ExportAnalysisFile,
+    ExportDatasetWithImages,
+    ImportDeepLabCutFolder,
+    RemoveVideo,
     ReplaceVideo,
     OpenSkeleton,
     SaveProjectAs,
@@ -90,13 +92,49 @@ def test_get_new_version_filename():
     )
 
 
-@pytest.mark.parametrize("out_suffix", ["h5", "nix"])
+def test_RemoveVideo(
+    centered_pair_predictions: Labels,
+    small_robot_mp4_vid: Video,
+    centered_pair_vid: Video,
+):
+    def ask(obj: RemoveVideo, context: CommandContext, params: dict) -> bool:
+        return True
+
+    RemoveVideo.ask = ask
+
+    labels = centered_pair_predictions.copy()
+    labels.add_video(small_robot_mp4_vid)
+    labels.add_video(centered_pair_vid)
+
+    all_videos = labels.videos
+    assert len(all_videos) == 3
+
+    video_idxs = [1, 2]
+    videos_to_remove = [labels.videos[i] for i in video_idxs]
+
+    context = CommandContext.from_labels(labels)
+    context.state["selected_batch_video"] = video_idxs
+    context.state["video"] = labels.videos[1]
+
+    context.removeVideo()
+
+    assert len(labels.videos) == 1
+    assert context.state["video"] not in videos_to_remove
+
+
+@pytest.mark.parametrize("out_suffix", ["h5", "nix", "csv"])
 def test_ExportAnalysisFile(
     centered_pair_predictions: Labels,
+    centered_pair_predictions_hdf5_path: str,
     small_robot_mp4_vid: Video,
     out_suffix: str,
     tmpdir,
 ):
+    if out_suffix == "csv":
+        csv = True
+    else:
+        csv = False
+
     def ExportAnalysisFile_ask(context: CommandContext, params: dict):
         """Taken from ExportAnalysisFile.ask()"""
 
@@ -119,7 +157,7 @@ def ask_for_filename(default_name: str) -> str:
         if len(videos) == 0:
             raise ValueError("No labeled frames in video(s). Nothing to export.")
 
-        default_name = context.state["filename"] or "labels"
+        default_name = "labels"
         fn = PurePath(tmpdir, default_name)
         if len(videos) == 1:
             # Allow user to specify the filename
@@ -162,7 +200,7 @@ def assert_videos_written(num_videos: int, labels_path: str = None):
             assert Path(output_path).exists()
             output_paths.append(output_path)
 
-            if labels_path is not None:
+            if labels_path is not None and not params["csv"]:
                 meta_reader = extract_meta_hdf5 if out_suffix == "h5" else read_nix_meta
                 labels_key = "labels_path" if out_suffix == "h5" else "project"
                 read_meta = meta_reader(output_path, dset_names_in=["labels_path"])
@@ -177,8 +215,20 @@ def assert_videos_written(num_videos: int, labels_path: str = None):
     context = CommandContext.from_labels(labels)
     context.state["filename"] = None
 
+    if csv:
+
+        context.state["filename"] = centered_pair_predictions_hdf5_path
+
+        params = {"all_videos": True, "csv": csv}
+        okay = ExportAnalysisFile_ask(context=context, params=params)
+        assert okay == True
+        ExportAnalysisFile.do_action(context=context, params=params)
+        assert_videos_written(num_videos=1, labels_path=context.state["filename"])
+
+        return
+
     # Test with all_videos False (single video)
-    params = {"all_videos": False}
+    params = {"all_videos": False, "csv": csv}
     okay = ExportAnalysisFile_ask(context=context, params=params)
     assert okay == True
     ExportAnalysisFile.do_action(context=context, params=params)
@@ -186,7 +236,7 @@ def assert_videos_written(num_videos: int, labels_path: str = None):
 
     # Add labels path and test with all_videos True (single video)
     context.state["filename"] = str(tmpdir.with_name("path.to.labels"))
-    params = {"all_videos": True}
+    params = {"all_videos": True, "csv": csv}
     okay = ExportAnalysisFile_ask(context=context, params=params)
     assert okay == True
     ExportAnalysisFile.do_action(context=context, params=params)
@@ -195,7 +245,7 @@ def assert_videos_written(num_videos: int, labels_path: str = None):
     # Add a video (no labels) and test with all_videos True
     labels.add_video(small_robot_mp4_vid)
 
-    params = {"all_videos": True}
+    params = {"all_videos": True, "csv": csv}
     okay = ExportAnalysisFile_ask(context=context, params=params)
     assert okay == True
     ExportAnalysisFile.do_action(context=context, params=params)
@@ -207,7 +257,7 @@ def assert_videos_written(num_videos: int, labels_path: str = None):
     labels.add_instance(frame=labeled_frame, instance=instance)
     labels.append(labeled_frame)
 
-    params = {"all_videos": False}
+    params = {"all_videos": False, "csv": csv}
     okay = ExportAnalysisFile_ask(context=context, params=params)
     assert okay == True
     ExportAnalysisFile.do_action(context=context, params=params)
@@ -216,14 +266,14 @@ def assert_videos_written(num_videos: int, labels_path: str = None):
     # Add specific video and test with all_videos False
     context.state["videos"] = labels.videos[1]
 
-    params = {"all_videos": False}
+    params = {"all_videos": False, "csv": csv}
     okay = ExportAnalysisFile_ask(context=context, params=params)
     assert okay == True
     ExportAnalysisFile.do_action(context=context, params=params)
     assert_videos_written(num_videos=1, labels_path=context.state["filename"])
 
     # Test with all videos True
-    params = {"all_videos": True}
+    params = {"all_videos": True, "csv": csv}
     okay = ExportAnalysisFile_ask(context=context, params=params)
     assert okay == True
     ExportAnalysisFile.do_action(context=context, params=params)
@@ -241,7 +291,7 @@ def assert_videos_written(num_videos: int, labels_path: str = None):
     labels.videos[0].backend.filename = str(tmpdir / "session1" / "video.mp4")
     labels.videos[1].backend.filename = str(tmpdir / "session2" / "video.mp4")
 
-    params = {"all_videos": True}
+    params = {"all_videos": True, "csv": csv}
     okay = ExportAnalysisFile_ask(context=context, params=params)
     assert okay == True
     ExportAnalysisFile.do_action(context=context, params=params)
@@ -252,7 +302,7 @@ def assert_videos_written(num_videos: int, labels_path: str = None):
     for video in all_videos:
         labels.remove_video(labels.videos[-1])
 
-    params = {"all_videos": True}
+    params = {"all_videos": True, "csv": csv}
     with pytest.raises(ValueError):
         okay = ExportAnalysisFile_ask(context=context, params=params)
 
@@ -406,7 +456,7 @@ def OpenSkeleton_ask(context: CommandContext, params: dict) -> bool:
             # Original function opens FileDialog here
             filename = params["filename_in"]
         else:
-            filename = get_package_file(f"sleap/skeletons/{template}.json")
+            filename = get_package_file(f"skeletons/{template}.json")
         if len(filename) == 0:
             return False
 
@@ -472,7 +522,7 @@ def OpenSkeleton_ask(context: CommandContext, params: dict) -> bool:
 
     # Run again with template set
     context.app.currentText = "fly32"
-    fly32_json = get_package_file(f"sleap/skeletons/fly32.json")
+    fly32_json = get_package_file(f"skeletons/fly32.json")
     OpenSkeleton_ask(context, params)
     assert params["filename"] == fly32_json
     fly32_skeleton = Skeleton.load_json(fly32_json)
@@ -795,3 +845,80 @@ def load_and_assert_changes(new_video_path: Path):
         load_and_assert_changes(search_path)
     finally:  # Move video back to original location - for ease of re-testing
         shutil.move(new_video_path, expected_video_path)
+
+
+@pytest.mark.parametrize("export_extension", [".json.zip", ".slp"])
+def test_exportLabelsPackage(export_extension, centered_pair_labels: Labels, tmpdir):
+    def assert_loaded_package_similar(path_to_pkg: Path, sugg=False, pred=False):
+        """Assert that the loaded labels are similar to the original."""
+
+        # Load the labels, but first copy file to a location (which pytest can and will
+        # keep in memory, but won't affect our re-use of the original file name)
+        filename_for_pytest_to_hoard: Path = path_to_pkg.with_name(
+            f"pytest_labels_{time.perf_counter_ns()}{export_extension}"
+        )
+        shutil.copyfile(path_to_pkg.as_posix(), filename_for_pytest_to_hoard.as_posix())
+        labels_reload: Labels = Labels.load_file(
+            filename_for_pytest_to_hoard.as_posix()
+        )
+
+        assert len(labels_reload.labeled_frames) == len(centered_pair_labels)
+        assert len(labels_reload.videos) == len(centered_pair_labels.videos)
+        assert len(labels_reload.suggestions) == len(centered_pair_labels.suggestions)
+        assert len(labels_reload.tracks) == len(centered_pair_labels.tracks)
+        assert len(labels_reload.skeletons) == len(centered_pair_labels.skeletons)
+        assert (
+            len(
+                set(labels_reload.skeleton.node_names)
+                - set(centered_pair_labels.skeleton.node_names)
+            )
+            == 0
+        )
+        num_images = len(labels_reload)
+        if sugg:
+            num_images += len(lfs_sugg)
+        if not pred:
+            num_images -= len(lfs_pred)
+        assert labels_reload.video.num_frames == num_images
+
+    # Set-up CommandContext
+    path_to_pkg = Path(tmpdir, "test_exportLabelsPackage.ext")
+    path_to_pkg = path_to_pkg.with_suffix(export_extension)
+
+    def no_gui_ask(cls, context, params):
+        """No GUI version of `ExportDatasetWithImages.ask`."""
+        params["filename"] = path_to_pkg.as_posix()
+        params["verbose"] = False
+        return True
+
+    ExportDatasetWithImages.ask = no_gui_ask
+
+    # Remove frames we want to use for suggestions and predictions
+    lfs_sugg = [centered_pair_labels[idx] for idx in [-1, -2]]
+    lfs_pred = [centered_pair_labels[idx] for idx in [-3, -4]]
+    centered_pair_labels.remove_frames(lfs_sugg)
+
+    # Add suggestions
+    for lf in lfs_sugg:
+        centered_pair_labels.add_suggestion(centered_pair_labels.video, lf.frame_idx)
+
+    # Add predictions and remove user instances from those frames
+    for lf in lfs_pred:
+        predicted_inst = PredictedInstance.from_instance(lf.instances[0], score=0.5)
+        centered_pair_labels.add_instance(lf, predicted_inst)
+        for inst in lf.user_instances:
+            centered_pair_labels.remove_instance(lf, inst)
+    context = CommandContext.from_labels(centered_pair_labels)
+
+    # Case 1: Export user-labeled frames with image data into a single SLP file.
+    context.exportUserLabelsPackage()
+    assert path_to_pkg.exists()
+    assert_loaded_package_similar(path_to_pkg)
+
+    # Case 2: Export user-labeled frames and suggested frames with image data.
+    context.exportTrainingPackage()
+    assert_loaded_package_similar(path_to_pkg, sugg=True)
+
+    # Case 3: Export all frames and suggested frames with image data.
+    context.exportFullPackage()
+    assert_loaded_package_similar(path_to_pkg, sugg=True, pred=True)
diff --git a/tests/gui/test_dataviews.py b/tests/gui/test_dataviews.py
index 7a89b1ab2..9c62daf88 100644
--- a/tests/gui/test_dataviews.py
+++ b/tests/gui/test_dataviews.py
@@ -20,7 +20,9 @@ def test_skeleton_nodes(qtbot, centered_pair_predictions):
     assert table.model().data(table.currentIndex()) == "thorax"
 
     table = GenericTableView(
-        row_name="video", model=VideosTableModel(items=centered_pair_predictions.videos)
+        row_name="video",
+        model=VideosTableModel(items=centered_pair_predictions.videos),
+        multiple_selection=True,
     )
     table.selectRow(0)
     assert (
diff --git a/tests/gui/test_filedialog.py b/tests/gui/test_filedialog.py
index d70a413db..8d90ff817 100644
--- a/tests/gui/test_filedialog.py
+++ b/tests/gui/test_filedialog.py
@@ -3,26 +3,38 @@
 
 from qtpy import QtWidgets
 
-from sleap.gui.dialogs.filedialog import FileDialog
+from sleap.gui.dialogs.filedialog import os_specific_method, FileDialog
 
 
 def test_non_native_dialog():
-    save_env_non_native = os.environ.get("USE_NON_NATIVE_FILE", None)
+    @os_specific_method
+    def dummy_function(cls, *args, **kwargs):
+        """This function returns the `kwargs` modified by the wrapper.
 
-    os.environ["USE_NON_NATIVE_FILE"] = ""
+        Args:
+            cls: The `FileDialog` class.
 
+        Returns:
+            kwargs: Modified by the wrapper.
+        """
+        return kwargs
+
+    FileDialog.dummy_function = dummy_function
+    save_env_non_native = os.environ.get("USE_NON_NATIVE_FILE", None)
+    os.environ["USE_NON_NATIVE_FILE"] = ""
     d = dict()
-    FileDialog._non_native_if_set(d)
+
+    # Wrapper doesn't mutate `d` outside of scope, so need to return `modified_d`
+    modified_d = FileDialog.dummy_function(FileDialog, d)
     is_linux = sys.platform.startswith("linux")
     if is_linux:
-        assert d["options"] == QtWidgets.QFileDialog.DontUseNativeDialog
+        assert modified_d["options"] == QtWidgets.QFileDialog.DontUseNativeDialog
     else:
-        assert "options" not in d
+        assert "options" not in modified_d
 
     os.environ["USE_NON_NATIVE_FILE"] = "1"
-    d = dict()
-    FileDialog._non_native_if_set(d)
-    assert d["options"] == QtWidgets.QFileDialog.DontUseNativeDialog
+    modified_d = FileDialog.dummy_function(FileDialog, d)
+    assert modified_d["options"] == QtWidgets.QFileDialog.DontUseNativeDialog
 
     if save_env_non_native is not None:
         os.environ["USE_NON_NATIVE_FILE"] = save_env_non_native
diff --git a/tests/gui/widgets/test_docks.py b/tests/gui/widgets/test_docks.py
index 0bc8f98b2..69fe56a56 100644
--- a/tests/gui/widgets/test_docks.py
+++ b/tests/gui/widgets/test_docks.py
@@ -1,8 +1,10 @@
 """Module for testing dock widgets for the `MainWindow`."""
 
+from pathlib import Path
 import pytest
-
+from sleap import Labels, Video
 from sleap.gui.app import MainWindow
+from sleap.gui.commands import OpenSkeleton
 from sleap.gui.widgets.docks import (
     InstancesDock,
     SuggestionsDock,
@@ -11,15 +13,64 @@
 )
 
 
-def test_videos_dock(qtbot):
+def test_videos_dock(
+    qtbot,
+    centered_pair_predictions: Labels,
+    small_robot_mp4_vid: Video,
+    centered_pair_vid: Video,
+    small_robot_3_frame_vid: Video,
+):
     """Test the `DockWidget` class."""
+
+    # Add some extra videos to the labels
+    labels = centered_pair_predictions
+    labels.add_video(small_robot_3_frame_vid)
+    labels.add_video(centered_pair_vid)
+    labels.add_video(small_robot_mp4_vid)
+    assert len(labels.videos) == 4
+
+    # Create the dock
     main_window = MainWindow()
+
+    # Use commands to set the labels instead of setting it directly
+    # To make sure other dependent instances like color_manager are also set
+    main_window.commands.loadLabelsObject(labels)
+
+    video_state = labels.videos[-1]
+    main_window.state["video"] = video_state
     dock = VideosDock(main_window)
 
+    # Test that the dock was created correctly
     assert dock.name == "Videos"
     assert dock.main_window is main_window
     assert dock.wgt_layout is dock.widget().layout()
 
+    # Test that videos can be removed
+
+    # No videos selected, won't remove anything
+    dock.main_window._buttons["remove video"].click()
+    assert len(labels.videos) == 4
+
+    # Select the last video, should remove that one and update state
+
+    dock.main_window.videos_dock.table.selectRowItem(small_robot_mp4_vid)
+    dock.main_window._buttons["remove video"].click()
+    assert len(labels.videos) == 3
+    assert video_state not in labels.videos
+    assert main_window.state["video"] == labels.videos[-1]
+
+    # Select the last two videos, should remove those two and update state
+    idxs = [1, 2]
+    videos_to_be_removed = [labels.videos[i] for i in idxs]
+    main_window.state["selected_batch_video"] = idxs
+    dock.main_window._buttons["remove video"].click()
+    assert len(labels.videos) == 1
+    assert (
+        videos_to_be_removed[0] not in labels.videos
+        and videos_to_be_removed[1] not in labels.videos
+    )
+    assert main_window.state["video"] == labels.videos[-1]
+
 
 def test_skeleton_dock(qtbot):
     """Test the `DockWidget` class."""
@@ -30,6 +81,13 @@ def test_skeleton_dock(qtbot):
     assert dock.main_window is main_window
     assert dock.wgt_layout is dock.widget().layout()
 
+    # This method should get called when we click the load button, but let's just call
+    # the non-gui parts directly
+    fn = Path(
+        OpenSkeleton.get_template_skeleton_filename(context=dock.main_window.commands)
+    )
+    assert fn.name == f"{dock.skeleton_templates.currentText()}.json"
+
 
 def test_suggestions_dock(qtbot):
     """Test the `DockWidget` class."""
@@ -49,7 +107,3 @@ def test_instances_dock(qtbot):
     assert dock.name == "Instances"
     assert dock.main_window is main_window
     assert dock.wgt_layout is dock.widget().layout()
-
-
-if __name__ == "__main__":
-    pytest.main([f"{__file__}::test_instances_dock"])
diff --git a/tests/io/test_dataset.py b/tests/io/test_dataset.py
index 6cc6485dc..5592ae437 100644
--- a/tests/io/test_dataset.py
+++ b/tests/io/test_dataset.py
@@ -1384,6 +1384,30 @@ def test_labels_numpy(centered_pair_predictions: Labels):
     np.testing.assert_array_equal(labels_np[lf.frame_idx, 0, :, :-1], user_inst.numpy())
 
 
+def test_add_track(centered_pair_labels: Labels, small_robot_mp4_vid: Video):
+    labels = centered_pair_labels
+    new_video = small_robot_mp4_vid
+
+    track = Track()
+    labels.add_track(new_video, track)
+    assert track in labels.tracks
+    assert new_video in labels._cache._track_occupancy
+    assert track in labels._cache._track_occupancy[new_video]
+
+
+def test_add_instance(centered_pair_labels: Labels):
+    labels = centered_pair_labels
+    lf = labels[0]
+    track = Track()
+    inst = Instance(skeleton=labels.skeleton, track=track, frame=lf)
+
+    labels.add_instance(lf, inst)
+    assert inst in labels.instances()
+    assert inst in lf.instances
+    assert track in labels.tracks
+    assert track in labels._cache._track_occupancy[lf.video]
+
+
 def test_remove_track(centered_pair_predictions):
     labels = centered_pair_predictions
 
diff --git a/tests/io/test_formats.py b/tests/io/test_formats.py
index b28de176e..a89bf60d7 100644
--- a/tests/io/test_formats.py
+++ b/tests/io/test_formats.py
@@ -2,6 +2,7 @@
 from pathlib import Path, PurePath
 
 import numpy as np
+import pandas as pd
 from numpy.testing import assert_array_equal
 import pytest
 import nixio
@@ -17,6 +18,7 @@
 from sleap.gui.commands import ImportAlphaTracker
 from sleap.gui.app import MainWindow
 from sleap.gui.state import GuiState
+from sleap.info.write_tracking_h5 import get_nodes_as_np_strings
 
 
 def test_text_adaptor(tmpdir):
@@ -126,6 +128,24 @@ def test_hdf5_v1_filehandle(centered_pair_predictions_hdf5_path):
     )
 
 
+def test_csv(tmpdir, min_labels_slp, minimal_instance_predictions_csv_path):
+    from sleap.info.write_tracking_h5 import main as write_analysis
+
+    filename_csv = str(tmpdir + "\\analysis.csv")
+    write_analysis(min_labels_slp, output_path=filename_csv, all_frames=True, csv=True)
+
+    labels_csv = pd.read_csv(filename_csv)
+
+    csv_predictions = pd.read_csv(minimal_instance_predictions_csv_path)
+
+    assert labels_csv.equals(csv_predictions)
+
+    labels = min_labels_slp
+
+    # check number of cols
+    assert len(labels_csv.columns) - 3 == len(get_nodes_as_np_strings(labels)) * 3
+
+
 def test_analysis_hdf5(tmpdir, centered_pair_predictions):
     from sleap.info.write_tracking_h5 import main as write_analysis
 
diff --git a/tests/io/test_video.py b/tests/io/test_video.py
index 9361f393b..4c3f8a5e9 100644
--- a/tests/io/test_video.py
+++ b/tests/io/test_video.py
@@ -37,6 +37,9 @@ def test_from_filename(hdf5_file_path, small_robot_mp4_path):
         == SingleImageVideo
     )
 
+    with pytest.raises(ValueError):
+        Video.from_filename("this_has_no_video_extension")
+
 
 def test_backend_extra_kwargs(hdf5_file_path, small_robot_mp4_path):
     Video.from_filename(hdf5_file_path, grayscale=True, another_kwarg=False)
diff --git a/tests/nn/test_evals.py b/tests/nn/test_evals.py
index 0e6a04dfe..265994056 100644
--- a/tests/nn/test_evals.py
+++ b/tests/nn/test_evals.py
@@ -1,12 +1,30 @@
+from pathlib import Path
 import numpy as np
+import tensorflow as tf
+
+from typing import List, Tuple
+
 import sleap
-from sleap.nn.evals import load_metrics, compute_oks
+
+from sleap import Instance, PredictedInstance
+from sleap.instance import Point
+from sleap.nn.config.training_job import TrainingJobConfig
+from sleap.nn.data.providers import LabelsReader
+from sleap.nn.evals import (
+    compute_dists,
+    compute_dist_metrics,
+    compute_oks,
+    load_metrics,
+    evaluate_model,
+)
+from sleap.nn.model import Model
 
 
 sleap.use_cpu_only()
 
 
 def test_compute_oks():
+    # Test compute_oks function with the cocoutils implementation
     inst_gt = np.array([[0, 0], [1, 1], [2, 2]]).astype("float32")
     inst_pr = np.array([[0, 0], [1, 1], [2, 2]]).astype("float32")
     oks = compute_oks(inst_gt, inst_pr)
@@ -26,6 +44,106 @@ def test_compute_oks():
     oks = compute_oks(inst_gt, inst_pr)
     np.testing.assert_allclose(oks, 1)
 
+    # Test compute_oks function with the implementation from the paper
+    inst_gt = np.array([[0, 0], [1, 1], [2, 2]]).astype("float32")
+    inst_pr = np.array([[0, 0], [1, 1], [2, 2]]).astype("float32")
+    oks = compute_oks(inst_gt, inst_pr, False)
+    np.testing.assert_allclose(oks, 1)
+
+    inst_pr = np.array([[0, 0], [1, 1], [np.nan, np.nan]]).astype("float32")
+    oks = compute_oks(inst_gt, inst_pr, False)
+    np.testing.assert_allclose(oks, 2 / 3)
+
+    inst_gt = np.array([[0, 0], [1, 1], [np.nan, np.nan]]).astype("float32")
+    inst_pr = np.array([[0, 0], [1, 1], [2, 2]]).astype("float32")
+    oks = compute_oks(inst_gt, inst_pr, False)
+    np.testing.assert_allclose(oks, 1)
+
+    inst_gt = np.array([[0, 0], [1, 1], [np.nan, np.nan]]).astype("float32")
+    inst_pr = np.array([[0, 0], [1, 1], [np.nan, np.nan]]).astype("float32")
+    oks = compute_oks(inst_gt, inst_pr, False)
+    np.testing.assert_allclose(oks, 1)
+
+
+def test_compute_dists(instances, predicted_instances):
+    # Make some changes to the instances
+    error_start = 10
+    error_end = 20
+    expected_dists = []
+    for offset, zipped_insts in enumerate(
+        zip(
+            instances[error_start:error_end], predicted_instances[error_start:error_end]
+        )
+    ):
+
+        inst, pred_inst = zipped_insts
+        for node_name in inst.skeleton.node_names:
+            pred_point = pred_inst[node_name]
+            if pred_point != np.NaN:
+                inst[node_name] = Point(
+                    pred_point.x + offset, pred_point.y + offset + 1
+                )
+
+        error = ((offset ** 2) + (offset + 1) ** 2) ** (1 / 2)
+        expected_dists.append(error)
+
+    best_match_oks = np.NaN
+    positive_pairs: List[Tuple[Instance, PredictedInstance]] = [
+        (inst, pred_inst, best_match_oks)
+        for inst, pred_inst in zip(instances, predicted_instances)
+    ]
+
+    dists_dict = compute_dists(positive_pairs=positive_pairs)
+    dists = dists_dict["dists"]
+
+    # Replace nan to 0
+    dists_no_nan = np.nan_to_num(dists, nan=0)
+    np.testing.assert_allclose(dists_no_nan[0:10], 0)
+
+    # Replace nan to negative (which we never see in a norm)
+    dists_no_nan = np.nan_to_num(dists, nan=-1)
+
+    # Check distances are as expected
+    for idx, error in enumerate(expected_dists):
+        idx += error_start
+        dists_idx = dists_no_nan[idx]
+        dists_idx = dists_idx[dists_idx >= 0]
+        np.testing.assert_allclose(dists_idx, error)
+
+    # Check instances are as expected
+    dists_metric = compute_dist_metrics(dists_dict)
+    for idx, zipped_metrics in enumerate(
+        zip(dists_metric["dist.frame_idxs"], dists_metric["dist.video_paths"])
+    ):
+        frame_idx, video_path = zipped_metrics
+        assert frame_idx == instances[idx].frame.frame_idx
+        assert video_path == instances[idx].frame.video.backend.filename
+
+
+def test_evaluate_model(min_labels_slp, min_bottomup_model_path):
+
+    labels_reader = LabelsReader(labels=min_labels_slp, user_instances_only=True)
+    model_dir: str = min_bottomup_model_path
+    cfg = TrainingJobConfig.load_json(str(Path(model_dir, "training_config.json")))
+    model = Model.from_config(
+        config=cfg.model,
+        skeleton=labels_reader.labels.skeletons[0],
+        tracks=labels_reader.labels.tracks,
+        update_config=True,
+    )
+    model.keras_model = tf.keras.models.load_model(
+        Path(model_dir) / "best_model.h5", compile=False
+    )
+
+    labels_pr, metrics = evaluate_model(
+        cfg=cfg,
+        labels_gt=labels_reader,
+        model=model,
+        save=True,
+        split_name="test",
+    )
+    assert metrics is not None  # If metrics is None, then the metrics were not saved
+
 
 def test_load_metrics(min_centered_instance_model_path):
     model_path = min_centered_instance_model_path
diff --git a/tests/nn/test_inference.py b/tests/nn/test_inference.py
index 9e07b07f8..fe848bb1c 100644
--- a/tests/nn/test_inference.py
+++ b/tests/nn/test_inference.py
@@ -1,21 +1,23 @@
 import ast
+import json
+import zipfile
+from pathlib import Path
 from typing import cast
-import pytest
+
 import numpy as np
-import json
-from sleap.io.dataset import Labels
-from sleap.nn.tracking import FlowCandidateMaker, Tracker
+import pytest
 import tensorflow as tf
-import sleap
-from numpy.testing import assert_array_equal, assert_allclose
-from pathlib import Path
 import tensorflow_hub as hub
+from numpy.testing import assert_array_equal, assert_allclose
+
+import sleap
+from sleap.gui.learning import runners
+from sleap.io.dataset import Labels
 from sleap.nn.data.confidence_maps import (
     make_confmaps,
     make_grid_vectors,
     make_multi_confmaps,
 )
-
 from sleap.nn.inference import (
     InferenceLayer,
     InferenceModel,
@@ -49,10 +51,15 @@
     main as sleap_track,
     export_cli as sleap_export,
 )
+from sleap.nn.tracking import (
+    MatchedFrameInstance,
+    FlowCandidateMaker,
+    FlowMaxTracksCandidateMaker,
+    Tracker,
+)
+from sleap.instance import Track
 
 
-from sleap.gui.learning import runners
-
 sleap.nn.system.use_cpu_only()
 
 
@@ -832,6 +839,47 @@ def test_topdown_multiclass_predictor_high_threshold(
     assert len(labels_pr[0].instances) == 0
 
 
+def zip_directory_with_itself(src_dir, output_path):
+    """Zip a directory, including the directory itself.
+
+    Args:
+        src_dir: Path to directory to zip.
+        output_path: Path to output zip file.
+    """
+
+    src_path = Path(src_dir)
+    with zipfile.ZipFile(output_path, "w", zipfile.ZIP_DEFLATED) as zipf:
+        for file_path in src_path.rglob("*"):
+            arcname = src_path.name / file_path.relative_to(src_path)
+            zipf.write(file_path, arcname)
+
+
+def zip_directory_contents(src_dir, output_path):
+    """Zip the contents of a directory, not the directory itself.
+
+    Args:
+        src_dir: Path to directory to zip.
+        output_path: Path to output zip file.
+    """
+
+    src_path = Path(src_dir)
+    with zipfile.ZipFile(output_path, "w", zipfile.ZIP_DEFLATED) as zipf:
+        for file_path in src_path.rglob("*"):
+            arcname = file_path.relative_to(src_path)
+            zipf.write(file_path, arcname)
+
+
+@pytest.mark.parametrize(
+    "zip_func", [zip_directory_with_itself, zip_directory_contents]
+)
+def test_load_model_zipped(tmpdir, min_centroid_model_path, zip_func):
+    mp = Path(min_centroid_model_path)
+    zip_dir = Path(tmpdir, mp.name).with_name(mp.name + ".zip")
+    zip_func(mp, zip_dir)
+
+    predictor = load_model(str(zip_dir))
+
+
 @pytest.mark.parametrize("resize_input_shape", [True, False])
 @pytest.mark.parametrize(
     "model_fixture_name",
@@ -1293,7 +1341,13 @@ def test_topdown_id_predictor_save(
 
 
 @pytest.mark.parametrize(
-    "output_path,tracker_method", [("not_default", "flow"), (None, "simple")]
+    "output_path,tracker_method",
+    [
+        ("not_default", "flow"),
+        ("not_default", "flowmaxtracks"),
+        (None, "simple"),
+        (None, "simplemaxtracks"),
+    ],
 )
 def test_retracking(
     centered_pair_predictions: Labels, tmpdir, output_path, tracker_method
@@ -1308,6 +1362,9 @@ def test_retracking(
     )
     if tracker_method == "flow":
         cmd += " --tracking.save_shifted_instances 1"
+    elif tracker_method == "simplemaxtracks" or tracker_method == "flowmaxtracks":
+        cmd += " --tracking.max_tracking 1"
+        cmd += " --tracking.max_tracks 2"
     if output_path == "not_default":
         output_path = Path(tmpdir, "tracked_slp.slp")
         cmd += f" --output {output_path}"
@@ -1435,6 +1492,58 @@ def test_flow_tracker(centered_pair_predictions: Labels, tmpdir):
             assert abs(key[0] - key[1]) <= track_window  # References within window
 
 
+@pytest.mark.parametrize(
+    "max_tracks, trackername",
+    [
+        (2, "flowmaxtracks"),
+        (2, "simplemaxtracks"),
+    ],
+)
+def test_max_tracks_matching_queue(
+    centered_pair_predictions: Labels, max_tracks, trackername
+):
+    """Test flow max tracks instance generation."""
+    labels: Labels = centered_pair_predictions
+    max_tracking = True
+    track_window = 5
+
+    # Setup flow max tracker
+    tracker: Tracker = Tracker.make_tracker_by_name(
+        tracker=trackername,
+        track_window=track_window,
+        save_shifted_instances=True,
+        max_tracking=max_tracking,
+        max_tracks=max_tracks,
+    )
+
+    tracker.candidate_maker = cast(FlowMaxTracksCandidateMaker, tracker.candidate_maker)
+
+    # Run tracking
+    frames = sorted(labels.labeled_frames, key=lambda lf: lf.frame_idx)
+
+    for lf in frames[:20]:
+
+        # Clear the tracks
+        for inst in lf.instances:
+            inst.track = None
+
+        track_args = dict(untracked_instances=lf.instances, img=lf.video[lf.frame_idx])
+        tracker.track(**track_args)
+
+        if trackername == "flowmaxtracks":
+            # Check that saved instances are pruned to track window
+            for key in tracker.candidate_maker.shifted_instances.keys():
+                assert lf.frame_idx - key[0] <= track_window  # Keys are pruned
+                assert abs(key[0] - key[1]) <= track_window
+
+        # Check if the length of each of the tracks is not more than the track window
+        for track in tracker.track_matching_queue_dict.keys():
+            assert len(tracker.track_matching_queue_dict[track]) <= track_window
+
+        # Check if number of tracks that are generated are not more than the maximum tracks
+        assert len(tracker.track_matching_queue_dict) <= max_tracks
+
+
 def test_movenet_inference(movenet_video):
     inference_layer = MoveNetInferenceLayer(model_name="lightning")
     inference_model = MoveNetInferenceModel(inference_layer)
diff --git a/tests/nn/test_system.py b/tests/nn/test_system.py
index ea835e3c3..fc95bb0ea 100644
--- a/tests/nn/test_system.py
+++ b/tests/nn/test_system.py
@@ -87,3 +87,9 @@ def test_gpu_order_and_length():
 
     # Assert that the order and length of GPU indices match
     assert sleap_indices == nvidia_indices
+
+
+def test_gpu_device_order():
+    """Indirectly tests GPU device order by ensuring environment variable is set."""
+
+    assert os.environ["CUDA_DEVICE_ORDER"] == "PCI_BUS_ID"
diff --git a/tests/nn/test_tracker_components.py b/tests/nn/test_tracker_components.py
index 869ebc85c..f861241ee 100644
--- a/tests/nn/test_tracker_components.py
+++ b/tests/nn/test_tracker_components.py
@@ -14,7 +14,9 @@
 from sleap.skeleton import Skeleton
 
 
-@pytest.mark.parametrize("tracker", ["simple", "flow"])
+@pytest.mark.parametrize(
+    "tracker", ["simple", "flow", "simplemaxtracks", "flowmaxtracks"]
+)
 @pytest.mark.parametrize("similarity", ["instance", "iou", "centroid"])
 @pytest.mark.parametrize("match", ["greedy", "hungarian"])
 @pytest.mark.parametrize("count", [0, 2])
@@ -166,3 +168,222 @@ def test_frame_match_object():
 
     assert matches[1].track == "track b"
     assert matches[1].instance == "instance b"
+
+
+def make_insts(trx):
+    skel = Skeleton.from_names_and_edge_inds(
+        ["A", "B", "C"], edge_inds=[[0, 1], [1, 2]]
+    )
+
+    def make_inst(x, y):
+        pts = np.array([[-0.1, -0.1], [0.0, 0.0], [0.1, 0.1]]) + np.array([[x, y]])
+        return PredictedInstance.from_numpy(pts, [1, 1, 1], 1, skel)
+
+    insts = []
+    for frame in trx:
+        insts_frame = []
+        for x, y in frame:
+            insts_frame.append(make_inst(x, y))
+        insts.append(insts_frame)
+    return insts
+
+
+def test_max_tracking_large_gap_single_track():
+    # Track 2 instances with gap > window size
+    preds = make_insts(
+        [
+            [
+                (0, 0),
+                (0, 1),
+            ],
+            [
+                (0.1, 0),
+                (0.1, 1),
+            ],
+            [
+                (0.2, 0),
+                (0.2, 1),
+            ],
+            [
+                (0.3, 0),
+            ],
+            [
+                (0.4, 0),
+            ],
+            [
+                (0.5, 0),
+                (0.5, 1),
+            ],
+            [
+                (0.6, 0),
+                (0.6, 1),
+            ],
+        ]
+    )
+
+    tracker = Tracker.make_tracker_by_name(
+        tracker="simple",
+        # tracker="simplemaxtracks",
+        match="hungarian",
+        track_window=2,
+        # max_tracks=2,
+        # max_tracking=True,
+    )
+
+    tracked = []
+    for insts in preds:
+        tracked_insts = tracker.track(insts)
+        tracked.append(tracked_insts)
+    all_tracks = list(set([inst.track for frame in tracked for inst in frame]))
+
+    assert len(all_tracks) == 3
+
+    tracker = Tracker.make_tracker_by_name(
+        # tracker="simple",
+        tracker="simplemaxtracks",
+        match="hungarian",
+        track_window=2,
+        max_tracks=2,
+        max_tracking=True,
+    )
+
+    tracked = []
+    for insts in preds:
+        tracked_insts = tracker.track(insts)
+        tracked.append(tracked_insts)
+    all_tracks = list(set([inst.track for frame in tracked for inst in frame]))
+
+    assert len(all_tracks) == 2
+
+
+def test_max_tracking_small_gap_on_both_tracks():
+    # Test 2 instances with both tracks with gap > window size
+    preds = make_insts(
+        [
+            [
+                (0, 0),
+                (0, 1),
+            ],
+            [
+                (0.1, 0),
+                (0.1, 1),
+            ],
+            [
+                (0.2, 0),
+                (0.2, 1),
+            ],
+            [],
+            [],
+            [
+                (0.5, 0),
+                (0.5, 1),
+            ],
+            [
+                (0.6, 0),
+                (0.6, 1),
+            ],
+        ]
+    )
+
+    tracker = Tracker.make_tracker_by_name(
+        tracker="simple",
+        # tracker="simplemaxtracks",
+        match="hungarian",
+        track_window=2,
+        # max_tracks=2,
+        # max_tracking=True,
+    )
+
+    tracked = []
+    for insts in preds:
+        tracked_insts = tracker.track(insts)
+        tracked.append(tracked_insts)
+    all_tracks = list(set([inst.track for frame in tracked for inst in frame]))
+
+    assert len(all_tracks) == 4
+
+    tracker = Tracker.make_tracker_by_name(
+        # tracker="simple",
+        tracker="simplemaxtracks",
+        match="hungarian",
+        track_window=2,
+        max_tracks=2,
+        max_tracking=True,
+    )
+
+    tracked = []
+    for insts in preds:
+        tracked_insts = tracker.track(insts)
+        tracked.append(tracked_insts)
+    all_tracks = list(set([inst.track for frame in tracked for inst in frame]))
+
+    assert len(all_tracks) == 2
+
+
+def test_max_tracking_extra_detections():
+    # Test having more than 2 detected instances in a frame
+    preds = make_insts(
+        [
+            [
+                (0, 0),
+                (0, 1),
+            ],
+            [
+                (0.1, 0),
+                (0.1, 1),
+            ],
+            [
+                (0.2, 0),
+                (0.2, 1),
+            ],
+            [
+                (0.3, 0),
+            ],
+            [
+                (0.4, 0),
+            ],
+            [
+                (0.5, 0),
+                (0.5, 1),
+            ],
+            [
+                (0.6, 0),
+                (0.6, 1),
+                (0.6, 0.5),
+            ],
+        ]
+    )
+
+    tracker = Tracker.make_tracker_by_name(
+        tracker="simple",
+        # tracker="simplemaxtracks",
+        match="hungarian",
+        track_window=2,
+        # max_tracks=2,
+        # max_tracking=True,
+    )
+
+    tracked = []
+    for insts in preds:
+        tracked_insts = tracker.track(insts)
+        tracked.append(tracked_insts)
+    all_tracks = list(set([inst.track for frame in tracked for inst in frame]))
+
+    assert len(all_tracks) == 4
+
+    tracker = Tracker.make_tracker_by_name(
+        # tracker="simple",
+        tracker="simplemaxtracks",
+        match="hungarian",
+        track_window=2,
+        max_tracks=2,
+        max_tracking=True,
+    )
+
+    tracked = []
+    for insts in preds:
+        tracked_insts = tracker.track(insts)
+        tracked.append(tracked_insts)
+    all_tracks = list(set([inst.track for frame in tracked for inst in frame]))
+
+    assert len(all_tracks) == 2
diff --git a/tests/nn/test_tracking_integration.py b/tests/nn/test_tracking_integration.py
index 829b7c3cb..a6592dc4d 100644
--- a/tests/nn/test_tracking_integration.py
+++ b/tests/nn/test_tracking_integration.py
@@ -3,10 +3,42 @@
 import os
 import time
 
+import sleap
+from sleap.nn.inference import main as inference_cli
 import sleap.nn.tracker.components
 from sleap.io.dataset import Labels, LabeledFrame
 
 
+def test_simple_tracker(tmpdir, centered_pair_predictions_slp_path):
+    cli = (
+        "--tracking.tracker simple "
+        "--frames 200-300 "
+        f"-o {tmpdir}/simpletracks.slp "
+        f"{centered_pair_predictions_slp_path}"
+    )
+    inference_cli(cli.split(" "))
+
+    labels = sleap.load_file(f"{tmpdir}/simpletracks.slp")
+    assert len(labels.tracks) == 27
+
+
+def test_simplemax_tracker(tmpdir, centered_pair_predictions_slp_path):
+    cli = (
+        "--tracking.tracker simplemaxtracks "
+        "--tracking.max_tracking 1 --tracking.max_tracks 2 "
+        "--frames 200-300 "
+        f"-o {tmpdir}/simplemaxtracks.slp "
+        f"{centered_pair_predictions_slp_path}"
+    )
+    inference_cli(cli.split(" "))
+
+    labels = sleap.load_file(f"{tmpdir}/simplemaxtracks.slp")
+    assert len(labels.tracks) == 2
+
+
+# TODO: Refactor the below things into a real test suite.
+
+
 def make_ground_truth(frames, tracker, gt_filename):
     t0 = time.time()
     new_labels = run_tracker(frames, tracker)
@@ -95,6 +127,8 @@ def main(f, dir):
     trackers = dict(
         simple=sleap.nn.tracker.simple.SimpleTracker,
         flow=sleap.nn.tracker.flow.FlowTracker,
+        simplemaxtracks=sleap.nn.tracker.SimpleMaxTracker,
+        flowmaxtracks=sleap.nn.tracker.FlowMaxTracker,
     )
     matchers = dict(
         hungarian=sleap.nn.tracker.components.hungarian_matching,
@@ -110,11 +144,21 @@ def main(f, dir):
         0.25,
     )
 
-    def make_tracker(tracker_name, matcher_name, sim_name, scale=0):
-        tracker = trackers[tracker_name](
-            matching_function=matchers[matcher_name],
-            similarity_function=similarities[sim_name],
-        )
+    def make_tracker(
+        tracker_name, matcher_name, sim_name, max_tracks, max_tracking=False, scale=0
+    ):
+        if tracker_name == "simplemaxtracks" or tracker_name == "flowmaxtracks":
+            tracker = trackers[tracker_name](
+                matching_function=matchers[matcher_name],
+                similarity_function=similarities[sim_name],
+                max_tracks=max_tracks,
+                max_tracking=max_tracking,
+            )
+        else:
+            tracker = trackers[tracker_name](
+                matching_function=matchers[matcher_name],
+                similarity_function=similarities[sim_name],
+            )
         if scale:
             tracker.candidate_maker.img_scale = scale
         return tracker
@@ -145,6 +189,28 @@ def make_tracker_and_filename(*args, **kwargs):
                             scale=scale,
                         )
                         f(frames, tracker, gt_filename)
+                elif tracker_name == "flowmaxtracks":
+                    # If this tracker supports scale, try multiple scales
+                    for scale in scales:
+                        tracker, gt_filename = make_tracker_and_filename(
+                            tracker_name=tracker_name,
+                            matcher_name=matcher_name,
+                            sim_name=sim_name,
+                            max_tracks=2,
+                            max_tracking=True,
+                            scale=scale,
+                        )
+                        f(frames, tracker, gt_filename)
+                elif tracker_name == "simplemaxtracks":
+                    tracker, gt_filename = make_tracker_and_filename(
+                        tracker_name=tracker_name,
+                        matcher_name=matcher_name,
+                        sim_name=sim_name,
+                        max_tracks=2,
+                        max_tracking=True,
+                        scale=0,
+                    )
+                    f(frames, tracker, gt_filename)
                 else:
                     tracker, gt_filename = make_tracker_and_filename(
                         tracker_name=tracker_name,