zhengbt / datumaro

Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.

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
• Examples
• Features
• User manual
• Developer manual
• Python API
• 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
  • Sampling a dataset
    • analyzes inference result from the given dataset and selects the ‘best’ and the ‘least amount of’ samples for annotation.
    • Select the sample that best suits model training.
      • sampling with Entropy based algorithm

• 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)

• Model integration

  • Inference (OpenVINO, Caffe, PyTorch, TensorFlow, MxNet, etc.)
  • Explainable AI (RISE algorithm)
    • RISE for classification
    • RISE for object detection

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.

Telemetry data collection note

The OpenVINO™ telemetry library is used to collect basic information about Datumaro usage.

To enable/disable telemetry data collection please see the guide.