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NVIDIA ODTK change log

Version 0.2.3 -- 2020-04-14

Added

  • MobileNetV2FPN backbone

Version 0.2.2 -- 2020-04-01

Added

  • Rotated bounding box detections models can now be exported to ONNX and TensorRT using odtk export model.pth model.plan --rotated-bbox
  • The --rotated-bbox flag is automatically applied when running odtk infer or odtk export on a model trained with ODTK version 0.2.2 or later.

Changed

  • Improvements to the rotated IoU calculations.

Limitations

  • The C++ API cannot currently infer rotated bounding box models.

Version 0.2.1 -- 2020-03-18

Added

  • The DALI dataloader (flag --with-dali) now supports image augmentation using:
    • --augment-brightness : Randomly adjusts brightness of image
    • --augment-contrast : Randomly adjusts contrast of image
    • --augment-hue : Randomly adjusts hue of image
    • --augment-saturation : Randomly adjusts saturation of image

Changed

  • The code in box.py for generating anchors has been improved.

Version 0.2.0 -- 2020-03-13

Version 0.2.0 introduces rotated detections.

Added

  • train arguments:
    • --rotated-bbox: Trains a model is predict rotated bounding boxes [x, y, w, h, theta] instead of axis aligned boxes [x, y, w, h].
  • infer arguments:
    • --rotated-bbox: Infer a rotated model.

Changed

The project has reverted to the name Object Detection Toolkit (ODTK), to better reflect the multi-network nature of the repo.

  • retinanet has been replaced with odtk. All subcommands remain the same.

Limitations

  • Models trained using the --rotated-bbox flag cannot be exported to ONNX or a TensorRT Engine.
  • PyTorch raises two warnings which can be ignored:

Warning 1: NCCL watchdog

[E ProcessGroupNCCL.cpp:284] NCCL watchdog thread terminated

Warning 2: Save state warning

/opt/conda/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:201: UserWarning: Please also save or load the state of the optimzer when saving or loading the scheduler.
  warnings.warn(SAVE_STATE_WARNING, UserWarning)

Version 0.1.1 -- 2020-03-06

Added

  • train arguments
    • --augment-rotate: Randomly rotates the training images by 0°, 90°, 180° or 270°.
    • --augment-brightness : Randomly adjusts brightness of image
    • --augment-contrast : Randomly adjusts contrast of image
    • --augment-hue : Randomly adjusts hue of image
    • --augment-saturation : Randomly adjusts saturation of image
    • --regularization-l2 : Sets the L2 regularization of the optimizer.