Releases ######## .. toctree:: :maxdepth: 1 v1.6.0 (2Q24) ------------- - Add zero-shot visual prompting - Add support for the training and validation on the Intel Max GPU devices - Upgrade OpenVINO to 2023.3 - Automate performance benchmark - Bump ONNX version to 1.16.0 to resolve CVE-2022-25882 v1.5.2 (1Q24) ------------- - Remove polygon clipping code - Hotfix default memcache size to 100MB 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 v1.4.5 (1Q24) ------------- - Filter invalid polygon shapes - Fix a bug to set reverse_input_channels for OpenVINO models - Remove unreferenced dependency of protobuf 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