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vision datasets stats for common vision tasks and datasets.

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Vision Datasets Statistics

showcase

In computer vision, we have a lot of datasets that are used for different tasks (i.e, classification, detection, segmentation, tracking...). These datasets in a form of images and/or videos are used to train and test models.

Here we take a look at the statistics for datasets used for the 3 main tasks in computer vision (Detection, Segmentation, and Tracking). While attempting to do so we provide converters to standardized formats and PyTorch data loader implementations for specific datasets.

We are going to look at the statistics of each dataset and perform a comparison in the end. To do this we will need to load each dataset and extract it's statistics programmatically. We will also need to visualize the statistics in a way that is easy to understand. Last but not least the statistics will be compared.


Task based datasets lookup table

Task Detection Based Instance Segmentation Multi Object Tracking Video Instance Segmentation
Dataset
  • ✓ COCO
  • ✓ SkyData
  • ✓ VisDrone
  • ✓ KAIST
  • ✓ VHR-10
  • ✓ DOTA
  • ✓ VEDAI
  • ✓ KITTI
  • ✓ COCO
  • ✓ SkyData
  • ✓ VHR-10
  • ✓ SkyData
  • ✓ VisDrone-MOT
  • ✓ MOT-17
  • ✓ MOT-20
  • ✓ DanceTrack
  • ✓ TAO
  • ✓ SkyData
  • ✓ Youtube-VIS 2019
  • ✓ Youtube-VIS 2021

  • Getting Started

    For more details on how to use the repo, please refer to the docs

    # Get REPO
    #1. clone and setup up the repo
    !git clone https://github.com/ozerlabs-proxy/vision-datasets-
    stats.git
    #2. cd into the repo
    cd vision-datasets-stats
    #3.
    #we require conda to be installed
    #alternatively you can any other env
    conda env create -f environment.yaml
    conda activate VisionStats
    #4. follow along the notebooks

    Stats

    There is a number of stats about datasets that can be generated. These may vary depending on the task, for most we will derive the following:

    - Detection Tracking
    Stats
    1. ✓ Number of images
    2. ✓ Number of objects
    3. ✓ Number of classes
    4. ✓ Number of instances per class
    5. ✓ Average number of instances per image
    6. ✓ Average number of instances per class
    1. ✓ Number of videos
    2. ✓ Number of tracks
    3. ✓ Number of categories
    4. ✓ average track length
    5. ✓ average number of tracks per video
    6. ✓ average number of tracks per category
    7. ✓ video lengths
    8. ✓ min-max resolutions
    9. ✓ areas stats ...

    Contribution

    We are open to contributions, if you have a dataset that you would like to add to the list, please do so by following the steps in the contribution guide.