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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
• Contributing

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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 foundhere.

• 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
    • removeoccluded 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 liketrain,val, andtest:
    • 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 datasetand 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

Checkthe design documentfor a full list of features.Checkthe user manualfor usage instructions.

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Feel free toopen an Issue, if youthink something needs to be changed. You are welcome to participate indevelopment, instructions are available in ourcontribution guide.

TheOpenVINO™ telemetry libraryis used to collect basic information about Datumaro usage.

To enable/disable telemetry data collection please see theguide.