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Changelog

All notable changes to this project will be documented in this file.

[2.3.0]

New features

Enhancements

Bug fixes

[2.2.0]

New features

Enhancements

Bug fixes

[v2.1.0]

NOTES

OpenVINO™ Training Extensions, version 2.1.0 does not include the latest functional and security updates. OpenVINO™ Training Extensions, version 2.2.0 is targeted to be released in September 2024 and will include additional functional and security updates. Customers should update to the latest version as it becomes available.

New features

Enhancements

Bug fixes

Known issues

  • Post-Training Quantization (PTQ) optimization applied to maskrcnn_swint in the instance segmentation task may result in significantly reduced accuracy. This issue is expected to be addressed with an upgrade to OpenVINO and NNCF in a future release.

[v2.0.0]

NOTES

OpenVINO™ Training Extensions which version 2.0.0 has been updated to include refactoring of the overall architecture and functional updates. Users should install the new environment.

New features

  • Enable New design to provide a more seamless API/CLI that delivers the value of OTX: Product Design
  • Moved away from MMLab's libraries to provide a Lightning-based core and training pipeline
  • Use Lightning-based modules and trainers to deliver APIs/CLIs in a more user-friendly way
  • Support Intel devices for accelerating deep learning model training

Enhancements

  • Support more models for each task
  • Improve the API so user can configure efficient training with shorter code
  • Provide more customize settings through the CLI and API
  • Enhance the Auto-Configuration feature and made it available in the API

Bug fixes

  • Fixing some minor issues

Known issues

  • Anomaly task processing times have increased compared with v1.* version, with anomaly classification experiencing a slowdown of approximately 26%, anomaly detection by approximately 213%, and anomaly segmentation by approximately 78%. Issue #3592
  • Post-Training Quantization (PTQ) optimization applied to maskrcnn_swint in the instance segmentation task may result in significantly reduced accuracy compared with v1.* Issue #3593

[v1.6.1]

Enhancements

Bug fixes

[1.6.0]

New features

Enhancements

Bug fixes

[1.5.2]

Enhancements

[1.5.1]

Enhancements

Bug fixes

[v1.5.0]

New features

Enhancements

Bug fixes

Known issues

  • OpenVINO(==2023.0) IR inference is not working well on 2-stage models (e.g. Mask-RCNN) exported from torch>=1.13.1
  • NNCF QAT optimization is disabled for MaskRCNN models due to CUDA runtime error in ROIAlign kernel on torch==2.0.1

[v1.4.4]

Enhancements

Bug fixes

[v1.4.3]

Enhancements

[v1.4.2]

Enhancements

Bug fixes

[v1.4.1]

Enhancements

Bug fixes

[v1.4.0]

New features

Enhancements

Bug fixes

Known issues

  • OpenVINO(==2023.0) IR inference is not working well on 2-stage models (e.g. Mask-RCNN) exported from torch==1.13.1

[v1.3.1]

Enhancements

  • n/a

Bug fixes

Known issues

  • OpenVINO(==2022.3) IR inference is not working well on 2-stage models (e.g. Mask-RCNN) exported from torch==1.13.1 (working well up to torch==1.12.1) (openvinotoolkit#1906)

[v1.3.0]

New features

Enhancements

Bug fixes

Known issues

  • OpenVINO(==2022.3) IR inference is not working well on 2-stage models (e.g. Mask-RCNN) exported from torch==1.13.1 (working well up to torch==1.12.1) (openvinotoolkit#1906)

[v1.2.3]

Bug fixes

  • Return raw anomaly map instead of colormap as metadata to prevent applying colormap conversion twice (openvinotoolkit#2217)
  • Hotfix: use 0 confidence threshold when computing best threshold based on F1

[v1.2.2]

Enhancements

  • Improve warning message for tiling configurable parameter

Known issues

  • OpenVINO(==2022.3) IR inference is not working well on 2-stage models (e.g. Mask-RCNN) exported from torch==1.13.1 (working well up to torch==1.12.1) (openvinotoolkit#1906)

[v1.2.1]

Enhancements

Bug fixes

[v1.2.0]

New features

Enhancements

Bug fixes

  • Fix backward compatibility with OpenVINO SSD-like detection models from OTE 0.5 (openvinotoolkit#1970)

Known issues

  • OpenVINO(==2022.3) IR inference is not working well on 2-stage models (e.g. Mask-RCNN) exported from torch==1.13.1 (working well up to torch==1.12.1) (openvinotoolkit#1906)

[v1.1.2]

Bug fixes

  • Fix exception -> warning for anomaly dump_feature option
  • Remove dataset.with_empty_annotations() to keep original input structure (openvinotoolkit#1964)
  • Fix OV batch inference (saliency map generation) (openvinotoolkit#1965)
  • Replace EfficentNetB0 model download logic by pytorchcv to resolve zip issue (openvinotoolkit#1967)

[v1.1.1]

Bug fixes

  • Add missing OpenVINO dependency in exportable code requirement

[v1.1.0]

New features

Enhancements

Bug fixes

Known issues

  • OpenVINO(==2022.3) IR inference is not working well on 2-stage models (e.g. Mask-RCNN) exported from torch==1.13.1 (working well up to torch==1.12.1) (openvinotoolkit#1906)

[v1.0.1]

Enhancements

  • Refine documents by proof review
  • Separate installation for each tasks
  • Improve POT efficiency by setting stat_requests_number parameter to 1
  • Introduce new tile classifier to enhance tiling inference performance in MaskRCNN.

Bug fixes

  • Fix missing classes in cls checkpoint
  • Fix action task sample codes
  • Fix label_scheme mismatch in classification
  • Fix training error when batch size is 1
  • Fix hang issue when tracing a stack in certain scenario
  • Fix pickling error by Removing mmcv cfg dump in ckpt

[v1.0.0]

NOTES

OpenVINO™ Training Extensions which version 1.0.0 has been updated to include functional and security updates. Users should update to the latest version.

New features

  • Adaptation of Datumaro component as a dataset interface
  • Integrate hyper-parameter optimizations
  • Support action recognition task
  • Self-supervised learning mode for representational training
  • Semi-supervised learning mode for better model quality

Enhancements

  • Installation via PyPI package
  • Enhance find command to find configurations of supported tasks / algorithms / models / backbones
  • Introduce build command to customize task or model configurations in isolated workspace
  • Auto-config feature to automatically select the right algorithm and default model for the train & build command by detecting the task type of given input dataset
  • Improve documentation
  • Improve training performance by introducing enhanced loss for the few-shot transfer

Bug fixes

  • Fixing configuration loading issue from the meta data of the model in OpenVINO task for the backward compatibility
  • Fixing some minor issues

[v0.5.0]

NOTES

OpenVINO Training Extension which version is equal or older then v0.5.0 does not include the latest functional and security updates. OTE Version 1.0.0 is targeted to be released in February 2023 and will include additional functional and security updates. Customers should update to the latest version as it becomes available.

Added

Changed

Fixed

[v0.4.0]

Added

Fixed

  • Hot-fix for Detection fix two stage error (openvinotoolkit#1433)
  • Fixing ZeroDivisionError in iteration counter for detection-classification project trainings (openvinotoolkit#1449)
  • Some minor issues

[v0.3.1]

Fixed

  • Neural Network Compression Framework (NNCF)

  • Model Preparation Algorithm (MPA)

    • Fix 'Shape out of bounds' error when accepting AI predictions for detection oriented (openvinotoolkit#1326)
    • Fix weird confidence behaviour issue on predictions for hierarchical classification (openvinotoolkit#1332)
    • Fix training failure issue for hierarchical classification (openvinotoolkit#1329)
    • Fix training failure issues for segmentation and instance segmentation during inference process (openvinotoolkit#1338)
    • Some minor issues

Security

[v0.3.0]

Added

Changed

Fixed

[v0.2.0]

Added

  • Model Preparation Algorithm (MPA), a newly introduced OTE Algorithm backend for advanced transfer learning
    • Class-Incremental Learning support for OTE models
      • Image Classification
      • Object Detection
      • Semantic Segmentation
  • Object counting & Rotated object detection are added to Object Detection backend
  • Increased support for NNCF / FP16 / HPO
  • Ignored label support
  • Stop training on NaN losses

Changed

  • Major refactoring
    • Tasks & model templates had been moved to OTE repo from each OTE Algorithm backend

[v0.1.1]

Fixed

  • Some minor issues

[v0.1.0]

Added

  • OTE SDK, defines an interface which can be used by OTE CLI to access OTE Algorithms.
  • OTE CLI, contains set of commands needed to operate with deep learning models using OTE SDK Task interfaces.
  • OTE Algorithms, contains sub-projects implementing OTE SDK Task interfaces for different deep learning models.
    • Anomaly Classification
    • Image Classification
    • Object Detection
    • Semantic Segmentation