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CarND-TrafficLight-Detection

Step 1 - Get the Data

  1. Gather the data - We have 2 sets of data - simulator and real-world data.
  2. Label and annotate the data

We will come to download the dataset in step 4 - create the TFRecord.

Step 2 - Setup the tensorflow models

  1. Do git clone https://github.com/tensorflow/models.git inside the tensorflow directory.

  2. We will work with python 2, so activate respective virtual environment.

  3. Follow the instructions at this page for installing some simple dependencies. See rest of points as summary of commands.

  4. Go to research directory - cd tensorflow/models/research/ and run following commands:

    python setup.py build
python setup.py install
python slim/setup.py build
python slim/setup.py install
protoc object_detection/protos/*.proto --python_out=.
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

  5. Now that installation is done, test it:

    python object_detection/builders/model_builder_test.py

Step 3 - Download the Model

Here is the list of pre-trained models zoo

Download the required model tar.gz files and untar them into /tensorflow/models/research/ directory with tar -xvzf name_of_tar_file.

Next we need to setup an object detection pipeline. TensorFlow team also provides sample config files on their repo. For the training,we can use any of three models, ssd_mobilenet_v1_coco, ssd_inception_v2_coco and faster_rcnn_resnet101_coco. These models can be downloaded from here. Here are the download link:

wget http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz
wget http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz
wget http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_coco_2018_01_28.tar.gz

Select the model

We can go ahead with the ssd_inception_v2_coco model.

Step 4 - Create TFRecord files

python data_conversion_udacity_sim.py --output_path sim_data.record

python data_conversion_udacity_real.py --output_path real_data.record

Step 1 and Step 4 has been taken care off: https://github.com/kinshuk4/CarND-TrafficLight-Detection-Dataset. Just clone/download the repo, and use the data folder in it.

Step 5 - Train the Model

Commands for training the models and saving the weights for inference.

Use the label map

We need to use labelmap configured in label_map.pbtxt which contains 4 classes:

item {
  id: 1
  name: 'Green'
}

item {
  id: 2
  name: 'Red'
}

item {
  id: 3
  name: 'Yellow'
}

item {
  id: 4
  name: 'off'
}

Use the config files

TensorFlow team also provides sample config files on their repo. We have to setup the config files for real and simulator data.

Training Steps

Now for all the models, we do following steps:

  1. COCO pre-trained network models
  2. The TFRecord files we created earlier
  3. The label_map file with our classes
  4. The image data-set
  5. The TensorFlow model API

Use Inception SSD v2

For Simulator Data - Training

python object_detection/train.py --pipeline_config_path=config/ssd_inception-traffic-udacity_sim.config --train_dir=data/sim_training_data/sim_data_capture

For Simulator Data - Saving for Inference

python object_detection/export_inference_graph.py --pipeline_config_path=config/ssd_inception-traffic-udacity_sim.config --trained_checkpoint_prefix=data/sim_training_data/sim_data_capture/model.ckpt-5000 --output_directory=frozen_models/frozen_sim_inception/

For Real Data - Training

python object_detection/train.py --pipeline_config_path=config/ssd_inception-traffic_udacity_real.config --train_dir=data/real_training_data

For Real Data - Saving for Inference

python object_detection/export_inference_graph.py --pipeline_config_path=config/ssd_inception-traffic_udacity_real.config --trained_checkpoint_prefix=data/real_training_data/model.ckpt-10000 --output_directory=frozen_models/frozen_real_inception/

Use Faster-RCNN model

For Simulator Data - Training

python object_detection/train.py --pipeline_config_path=config/faster_rcnn-traffic-udacity_sim.config --train_dir=data/sim_training_data/sim_data_capture

For Simulator Data - Saving for Inference

python object_detection/export_inference_graph.py --pipeline_config_path=config/faster_rcnn-traffic-udacity_sim.config --trained_checkpoint_prefix=data/sim_training_data/sim_data_capture/model.ckpt-5000 --output_directory=frozen_sim/

For Real Data - Training

python object_detection/train.py --pipeline_config_path=config/faster_rcnn-traffic_udacity_real.config --train_dir=data/real_training_data

For Real Data - Saving for Inference

python object_detection/export_inference_graph.py --pipeline_config_path=config/faster_rcnn-traffic_udacity_real.config --trained_checkpoint_prefix=data/real_training_data/model.ckpt-10000 --output_directory=frozen_real/

Use MobileNet SSD v1

(Due to some unknown reasons the model gets trained but does not save for inference. Ignoring this for now.)

For Simulator Data - Training

python object_detection/train.py --pipeline_config_path=config/ssd_mobilenet-traffic-udacity_sim.config --train_dir=data/sim_training_data/sim_data_capture

For Simulator Data - Saving for Inference

python object_detection/export_inference_graph.py --pipeline_config_path=config/ssd_mobilenet-traffic-udacity_sim.config --trained_checkpoint_prefix=data/sim_training_data/sim_data_capture/model.ckpt-5000 --output_directory=frozen_models/frozen_sim_mobile/

For Real Data - Training

python object_detection/train.py --pipeline_config_path=config/ssd_mobilenet-traffic_udacity_real.config --train_dir=data/real_training_data

For Real Data - Saving for Inference

python object_detection/export_inference_graph.py --pipeline_config_path=config/ssd_mobilenet-traffic_udacity_real.config --trained_checkpoint_prefix=data/real_training_data/model.ckpt-10000 --output_directory=frozen_models/frozen_real_mobile/

Test the Model

Working on it.

Credits

To save the time and effort, I followed the post from https://becominghuman.ai/@Vatsal410 and https://medium.com/@anthony_sarkis, where they have shared shared his annotated data-set openly available for all to use. Also, I got the config files for the project as well, that saved lot of time. Thanks for sharing such a nice post and the dataset.