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)