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.