Releases#

v2.0.0 (1Q24)#

v1.6.0 (2024.04)#

New features#

  • Changed supported Python version range (>=3.9, <=3.11)

  • Support MMDetection COCO format

  • Develop JsonSectionPageMapper in Rust API

  • Add Filtering via User-Provided Python Functions

  • Remove supporting MacOS platform

  • Support Kaggle image data (KaggleImageCsvBase, KaggleImageTxtBase, KaggleImageMaskBase, KaggleVocBase, KaggleYoloBase)

  • Add __getitem__() for random accessing with O(1) time complexity

  • Add Data-aware Anchor Generator

  • Support bounding box import within Kaggle extractors and add KaggleCocoBase

Enhancements#

  • Optimize Python import to make CLI entrypoint faster

  • Add ImageColorScale context manager

  • Enhance visualizer to toggle plot title visibility

  • Enhance Datumaro data format detect() to be memory-bounded and performant

  • Change RoIImage and MosaicImage to have np.uint8 dtype as default

  • Enable image backend and color channel format to be selectable

  • Boost up CityscapesBase and KaggleImageMaskBase by dropping np.unique

  • Enhance RISE algortihm for explainable AI

  • Enhance explore unit test to use real dataset from ImageNet

  • Fix each method of the comparator to be used separately

Bug fixes#

  • Fix wrong example of Datumaro dataset creation in document

  • Fix wrong command to install datumaro from github

  • Update document to correct wrong datum project import command and add filtering example to filter out items containing annotations.

  • Fix label compare of distance method

  • Fix Datumaro visualizer’s import errors after introducing lazy import

  • Fix broken link to supported formats in readme

  • Fix Kinetics data format to have media data

  • Handling undefined labels at the annotation statistics

  • Add unit test for item rename

  • Fix a bug in the previous behavior when importing nested datasets in the project

  • Fix Kaggle importer when adding duplicated labels

  • Fix input tensor shape in model interpreter for OpenVINO 2023.3

  • Add default value for target in prune cli

  • Remove deprecated MediaManager

  • Fix explore command without project

v1.5.2 (2024.01)#

Enhancements#

  • Add memory bounded datumaro data format detect

  • Remove Protobuf version limitation (<4)

v1.5.1 (2023.11)#

Enhancements#

  • Enhance Datumaro data format stream importer performance

  • Change image default dtype from float32 to uint8

  • Add comparison level-up doc

  • Add ImportError to catch GitPython import error

Bug fixes#

  • Modify the draw function in the visualizer not to raise an error for unsupported annotation types.

  • Correct explore path in the related document.

  • Fix errata in the voc document. Color values in the labelmap.txt should be separated by commas, not colons.

  • Fix hyperlink errors in the document.

  • Fix memory unbounded Arrow data format export/import.

  • Update CVAT format doc to bypass warning.

v1.5.0 (4Q23)#

  • Enable configurable confidence threshold for otx eval and export

  • Add YOLOX variants as new object detector models

  • Enable FeatureVectorHook to support action tasks

  • Add ONNX metadata to detection, instance segmantation, and segmentation models

  • Add a new feature to configure input size

  • Introduce the OTXSampler and AdaptiveRepeatDataHook to achieve faster training at the small data regime

  • Add a new object detector Lite-DINO

  • Add Semi-SL Mean Teacher algorithm for Instance Segmentation task

  • Official supports for YOLOX-X, YOLOX-L, YOLOX-S, ResNeXt101-ATSS

  • Add new argument to track resource usage in train command

  • Add Self-SL for semantic segmentation of SegNext families

  • Adapt input size automatically based on dataset statistics

  • Refine input data in-memory caching

  • Adapt timeout value of initialization for distributed training

  • Optimize data loading by merging load & resize operations w/ caching support for cls/det/iseg/sseg

  • Support torch==2.0.1

  • Set “Auto” as default input size mode

v1.4.4 (4Q23)#

  • Update ModelAPI configuration

  • Add Anomaly modelAPI changes

  • Update Image numpy access

v1.4.3 (4Q23)#

  • Re introduce adaptive scheduling for training

v1.4.2 (4Q23)#

  • Upgrade nncf version to 2.6.0

  • Bump datumaro version to 1.5.0

  • Set tox version constraint

  • Add model category attributes to model template

  • Minor bug fixes

v1.4.1 (3Q23)#

  • Update the README file in exportable code

  • Minor bug fixes

v1.4.0 (3Q23)#

  • Support encrypted dataset training

  • Add custom max iou assigner to prevent CPU OOM when large annotations are used

  • Auto train type detection for Semi-SL, Self-SL and Incremental: “–train-type” now is optional

  • Add per-class XAI saliency maps for Mask R-CNN model

  • Add new object detector Deformable DETR

  • Add new object detector DINO

  • Add new visual prompting task

  • Add new object detector ResNeXt101-ATSS

  • Introduce channel_last parameter to improve the performance

  • Decrease time for making a workspace

  • Set persistent_workers and pin_memory as True in detection task

  • New algorithm for Semi-SL semantic segmentation based on metric learning via class prototypes

  • Self-SL for classification now can recieve just folder with any images to start contrastive pretraining

  • Update OpenVINO version to 2023.0, and NNCF verion to 2.5

  • Improve XAI saliency map generation for tiling detection and tiling instance segmentation

  • Remove CenterCrop from Classification test pipeline and editing missing docs link

  • Switch to PTQ for sseg

  • Minor bug fixes

v1.3.1 (2Q23)#

  • Minor bug fixes

v1.3.0 (2Q23)#

  • Support direct annotation input for COCO format

  • Action task supports multi GPU training

  • Support storage cache in Apache Arrow using Datumaro for action tasks

  • Add a simplified greedy labels postprocessing for hierarchical classification

  • Support auto adapting batch size

  • Support auto adapting num_workers

  • Support noisy label detection for detection tasks

  • Make semantic segmentation OpenVINO models compatible with ModelAPI

  • Support label hierarchy through LabelTree in LabelSchema for classification task

  • Enhance exportable code file structure, video inference and default value for demo

  • Speedup OpenVINO inference in image classificaiton, semantic segmentation, object detection and instance segmentation tasks

  • Refactoring of ONNX export functionality

  • Minor bug fixes

v1.2.4 (3Q23)#

  • Per-class saliency maps for M-RCNN

  • Disable semantic segmentation soft prediction processing

  • Update export and nncf hyperparameters

  • Minor bug fixes

v1.2.3 (2Q23)#

  • Improve warning message for tiling configurable parameter

  • Minor bug fixes

v1.2.1 (2Q23)#

  • Upgrade mmdeploy==0.14.0 from official PyPI

  • Integrate new ignored loss in semantic segmentation

  • Optimize YOLOX data pipeline

  • Tiling Spatial Concatenation for OpenVINO IR

  • Optimize counting train & inference speed and memory consumption

  • Minor bug fixes

v1.2.0 (2Q23)#

  • Add generating feature cli_report.log in output for otx training

  • Support multiple python versions up to 3.10

  • Support export of onnx models

  • Add option to save images after inference in OTX CLI demo together with demo in exportable code

  • Support storage cache in Apache Arrow using Datumaro for cls, det, seg tasks

  • Add noisy label detection for multi-class classification task

  • Clean up and refactor the output of the OTX CLI

  • Enhance DetCon logic and SupCon for semantic segmentation

  • Detection task refactoring

  • Classification task refactoring

  • Extend OTX explain CLI

  • Segmentation task refactoring

  • Action task refactoring

  • Optimize data preprocessing time and enhance overall performance in semantic segmentation

  • Support automatic batch size decrease when there is no enough GPU memory

  • Minor bug fixes

v1.1.2 (2Q23)#

  • Minor bug fixes

v1.1.1 (1Q23)#

  • Minor bug fixes

v1.1.0 (1Q23)#

  • Add FP16 IR export support

  • Add in-memory caching in dataloader

  • Add MoViNet template for action classification

  • Add Semi-SL multilabel classification algorithm

  • Integrate multi-gpu training for semi-supervised learning and self-supervised learning

  • Add train-type parameter to otx train

  • Add embedding of inference configuration to IR for classification

  • Enable VOC dataset in OTX

  • Add mmcls.VisionTransformer backbone support

  • Parametrize saliency maps dumping in export

  • Bring mmdeploy to action recognition model export & Test optimization of action tasks

  • Update backbone lists

  • Add explanation for XAI & minor doc fixes

  • Refactor phase#1: MPA modules

v1.0.1 (1Q23)#

  • Refine documents by proof review

  • Separate installation for each tasks

  • Improve POT efficiency by setting stat_requests_number parameter to 1

  • Minor bug fixes

v1.0.0 (1Q23)#

  • Installation through PyPI - Package will be renamed as OpenVINO™ Training Extensions

  • CLI update - Update otx find command to find configurations of tasks/algorithms - Introduce otx build command to customize task or model configurations - Automatic algorithm selection for the otx train command using the given input dataset

  • Adaptation of Datumaro component as a dataset interface