datumaro 1.13.2

Dataset Management Framework (Datumaro)

Dataset Management Framework (Datumaro)

A framework and CLI tool to build, transform, and analyze datasets.

VOC dataset                                  ---> Annotation tool
     +                                     /
COCO dataset -----> Datumaro ---> dataset ------> Model training
     +                                     \
CVAT annotations                             ---> Publication, statistics etc.

• Getting started
• Level Up
• Features
• User manual
• Developer manual
• Contributing

Features

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• Dataset reading, writing, conversion in any direction.

  • CIFAR-10/100 (classification)
  • Cityscapes
  • COCO (image_info, instances, person_keypoints, captions, labels, panoptic, stuff)
  • CVAT
  • ImageNet
  • Kitti (segmentation, detection, 3D raw / velodyne points)
  • LabelMe
  • LFW (classification, person re-identification, landmarks)
  • MNIST (classification)
  • Open Images
  • PASCAL VOC (classification, detection, segmentation, action_classification, person_layout)
  • TF Detection API (bboxes, masks)
  • YOLO (bboxes)

  Other formats and documentation for them can be found here.

• Dataset building

  • Merging multiple datasets into one
  • Dataset filtering by a custom criteria:
    • remove polygons of a certain class
    • remove images without annotations of a specific class
    • remove occluded annotations from images
    • keep only vertically-oriented images
    • remove small area bounding boxes from annotations
  • Annotation conversions, for instance:
    • polygons to instance masks and vice-versa
    • apply a custom colormap for mask annotations
    • rename or remove dataset labels
  • Splitting a dataset into multiple subsets like train, val, and test:
    • random split
    • task-specific splits based on annotations, which keep initial label and attribute distributions
      • for classification task, based on labels
      • for detection task, based on bboxes
      • for re-identification task, based on labels, avoiding having same IDs in training and test splits

• Dataset quality checking

  • Simple checking for errors
  • Comparison with model inference
  • Merging and comparison of multiple datasets
  • Annotation validation based on the task type(classification, etc)

• Dataset comparison

• Dataset statistics (image mean and std, annotation statistics)

Check the design document for a full list of features. Check the user manual for usage instructions.

Contributing

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Feel free to open an Issue, if you think something needs to be changed. You are welcome to participate in development, instructions are available in our contribution guide.