Releases#

v1.6.1 (2Q24)#

  • Replace the default model for rotated_det/ins_seg task from resnet50_maskrcnn to efficientnetb2b_maskrcnn

  • Update pymongo version to 4.6.3 for resolving CVE-2024-21506

  • Use torchvision in MRCNN on CUDA

  • Update IPEX version in installation guide documentation

  • Update benchmark

  • Bump idan version to 3.7

  • Support benchmark history summary

  • Upgrade MAPI

  • Add NMS iou threshold configurable parameter

  • Remedy some medium/low severity bandit issues

  • Update documentations

  • Add perf benchmark test cases for action and visual prompting

  • Explicitly cast incorrect output type in OV model

  • Update QAT configs for rotated detection

  • Hotfix :wrench: Bypass ClsIncrSampler for tiling

  • [NNCF] Dynamic shape datasets WA

  • [Hotfix] :fire: Fixing detection oriented OV inferencer

  • Revert adaptive batch size

  • Fix e2e tests for XPU

  • Remove torch.xpu.optimize for semantic_segmentation task

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