Release v0.1.2

Added

• ByteImage class to represent encoded images in memory and avoid recoding on save (#27)

Changed

• Implementation of format plugins simplified (#22)
• default is now a default subset name, instead of None. The values are interchangeable. (#22)
• Improved performance of transforms (#22)

Removed

• image/depth value from VOC export (#27)

Fixed

• Zero division errors in dataset statistics (#31)

Release v0.1.1

Added

• reindex option in COCO and CVAT format converters (#18)
• Support for relative paths in LabelMe format (#19)
• MOTS png mask format support (#21)

Release v0.1.0

Supported Python versions: 3.6, 3.7, 3.8

Interfaces

• Python API for user code
  • Installation as a package
• A command-line tool for dataset manipulations

Features

• Dataset format support (reading, writing, conversions - any to any)

  • Own format
  • CVAT
  • COCO
  • PASCAL VOC
  • YOLO
  • TF Detection API
  • LabelMe

• Dataset building

  • Composite dataset building
  • Class remapping (project transform remap_labels)
  • Subset splitting (project transform random_split)
  • Dataset filtering (project filter)
  • Dataset merging / updating (project merge)

• Dataset operations

  • Dataset multi-source merging + quality checking + cross-source checking (merge)
  • Annotation transformations (project transform)
  • Dataset info (project info)

• Calculation of statistics for datasets (project stats)

  • Pixel mean, std
  • Object counts, area distribution (detection scenario)
  • Image-Class distribution (classification scenario)
  • Pixel-Class distribution (segmentation scenario)
  • Attributes distribution per label

• Dataset comparison (project diff, project ediff)

  • Annotation-annotation comparison
  • Annotation-inference comparison

• Dataset and model debugging

  • Inference explanation (explain)
    • Black-box approach (RISE paper)
  • Ability to run a model on a dataset, read and write the results
    • OpenVINO
    • Caffe, PyTorch, TensorFlow, MxNet - with Accuracy Checker