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v0.6.0 (15/8/2023)

Highlights

  • Support YOLOv5 instance segmentation

  • Support YOLOX-Pose based on MMPose

  • Add 15 minutes instance segmentation tutorial.

  • YOLOv5 supports using mask annotation to optimize bbox

  • Add Multi-scale training and testing docs

New Features

  • Add training and testing tricks doc (#659)

  • Support setting the cache_size_limit parameter and support mmdet 3.0.0 (#707)

  • Support YOLOv5u and YOLOv6 3.0 inference (#624, #744)

  • Support model-only inference (#733)

  • Add YOLOv8 deepstream config (#633)

  • Add ionogram example in MMYOLO application (#643)

Bug Fixes

  • Fix the browse_dataset for visualization of test and val (#641)

  • Fix installation doc error (#662)

  • Fix yolox-l ckpt link (#677)

  • Fix typos in the YOLOv7 and YOLOv8 diagram (#621, #710)

  • Adjust the order of package imports in boxam_vis_demo.py (#655)

Improvements

  • Optimize the convert_kd_ckpt_to_student.py file (#647)

  • Add en doc of FAQ and training_testing_tricks (#691,#693)

Contributors

A total of 21 developers contributed to this release.

Thank @Lum1104,@azure-wings,@FeiGeChuanShu,@Lingrui Gu,@Nioolek,@huayuan4396,@RangeKing,@danielhonies,@yechenzhi,@JosonChan1998,@kitecats,@Qingrenn,@triple-Mu,@kikefdezl,@zhangrui-wolf,@xin-li-67,@Ben-Louis,@zgzhengSEU,@VoyagerXvoyagerx,@tang576225574,@hhaAndroid

v0.5.0 (2/3/2023)

Highlights

  1. Support RTMDet-R rotated object detection

  2. Support for using mask annotation to improve YOLOv8 object detection performance

  3. Support MMRazor searchable NAS sub-network as the backbone of YOLO series algorithm

  4. Support calling MMRazor to distill the knowledge of RTMDet

  5. MMYOLO document structure optimization, comprehensive content upgrade

  6. Improve YOLOX mAP and training speed based on RTMDet training hyperparameters

  7. Support calculation of model parameters and FLOPs, provide GPU latency data on T4 devices, and update Model Zoo

  8. Support test-time augmentation (TTA)

  9. Support RTMDet, YOLOv8 and YOLOv7 assigner visualization

New Features

  1. Support inference for RTMDet instance segmentation tasks (#583)

  2. Beautify the configuration file in MMYOLO and add more comments (#501, #506, #516, #529, #531, #539)

  3. Refactor and optimize documentation (#568, #573, #579, #584, #587, #589, #596, #599, #600)

  4. Support fast version of YOLOX (#518)

  5. Support DeepStream in EasyDeploy and add documentation (#485, #545, #571)

  6. Add confusion matrix drawing script (#572)

  7. Add single channel application case (#460)

  8. Support auto registration (#597)

  9. Support Box CAM of YOLOv7, YOLOv8 and PPYOLOE (#601)

  10. Add automated generation of MM series repo registration information and tools scripts (#559)

  11. Added YOLOv7 model structure diagram (#504)

  12. Add how to specify specific GPU training and inference files (#503)

  13. Add check if metainfo is all lowercase when training or testing (#535)

  14. Add links to Twitter, Discord, Medium, YouTube, etc. (#555)

Bug Fixes

  1. Fix isort version issue (#492, #497)

  2. Fix type error of assigner visualization (#509)

  3. Fix YOLOv8 documentation link error (#517)

  4. Fix RTMDet Decoder error in EasyDeploy (#519)

  5. Fix some document linking errors (#537)

  6. Fix RTMDet-Tiny weight path error (#580)

Improvements

  1. Update contributing.md

  2. Optimize DetDataPreprocessor branch to support multitasking (#511)

  3. Optimize gt_instances_preprocess so it can be used for other YOLO algorithms (#532)

  4. Add yolov7-e6e weight conversion script (#570)

  5. Reference YOLOv8 inference code modification PPYOLOE

Contributors

A total of 22 developers contributed to this release.

Thank @triple-Mu, @isLinXu, @Audrey528, @TianWen580, @yechenzhi, @RangeKing, @lyviva, @Nioolek, @PeterH0323, @tianleiSHI, @aptsunny, @satuoqaq, @vansin, @xin-li-67, @VoyagerXvoyagerx, @landhill, @kitecats, @tang576225574, @HIT-cwh, @AI-Tianlong, @RangiLyu, @hhaAndroid

v0.4.0 (18/1/2023)

Highlights

  1. Implemented YOLOv8 object detection model, and supports model deployment in projects/easydeploy

  2. Added Chinese and English versions of Algorithm principles and implementation with YOLOv8

New Features

  1. Added YOLOv8 and PPYOLOE model structure diagrams (#459, #471)

  2. Adjust the minimum supported Python version from 3.6 to 3.7 (#449)

  3. Added a new YOLOX decoder in TensorRT-8 (#450)

  4. Add a tool for scheduler visualization (#479)

Bug Fixes

  1. Fix optimize_anchors.py script import error (#452)

  2. Fix the wrong installation steps in get_started.md (#474)

  3. Fix the neck error when using the RTMDet P6 model (#480)

Contributors

A total of 9 developers contributed to this release.

Thank @VoyagerXvoyagerx, @tianleiSHI, @RangeKing, @PeterH0323, @Nioolek, @triple-Mu, @lyviva, @Zheng-LinXiao, @hhaAndroid

v0.3.0 (8/1/2023)

Highlights

  1. Implement fast version of RTMDet. RTMDet-s 8xA100 training takes only 14 hours. The training speed is 2.6 times faster than the previous version.

  2. Support PPYOLOE training

  3. Support iscrowd attribute training in YOLOv5

  4. Support YOLOv5 assigner result visualization

New Features

  1. Add crowdhuman dataset (#368)

  2. Easydeploy support TensorRT inference (#377)

  3. Add YOLOX structure description (#402)

  4. Add a feature for the video demo (#392)

  5. Support YOLOv7 easy deploy (#427)

  6. Add resume from specific checkpoint in CLI (#393)

  7. Set metainfo fields to lower case (#362, #412)

  8. Add module combination doc (#349, #352, #345)

  9. Add docs about how to freeze the weight of backbone or neck (#418)

  10. Add don’t used pre-training weights doc in how_to.md (#404)

  11. Add docs about how to set the random seed (#386)

  12. Translate rtmdet_description.md document to English (#353)

  13. Add doc of yolov6_description.md (#382, #372)

Bug Fixes

  1. Fix bugs in the output annotation file when --class-id-txt is set (#430)

  2. Fix batch inference bug in YOLOv5 head (#413)

  3. Fix typehint in some heads (#415, #416, #443)

  4. Fix RuntimeError of torch.cat() expected a non-empty list of Tensors (#376)

  5. Fix the device inconsistency error in YOLOv7 training (#397)

  6. Fix the scale_factor and pad_param value in LetterResize (#387)

  7. Fix docstring graph rendering error of readthedocs (#400)

  8. Fix AssertionError when YOLOv6 from training to val (#378)

  9. Fix CI error due to np.int and legacy builder.py (#389)

  10. Fix MMDeploy rewriter (#366)

  11. Fix MMYOLO unittest scope bug (#351)

  12. Fix pad_param error (#354)

  13. Fix twice head inference bug (#342)

  14. Fix customize dataset training (#428)

Improvements

  1. Update useful_tools.md (#384)

  2. update the English version of custom_dataset.md (#381)

  3. Remove context argument from the rewriter function (#395)

  4. deprecating np.bool type alias (#396)

  5. Add new video link for custom dataset (#365)

  6. Export onnx for model only (#361)

  7. Add MMYOLO regression test yml (#359)

  8. Update video tutorials in article.md (#350)

  9. Add deploy demo (#343)

  10. Optimize the vis results of large images in debug mode (#346)

  11. Improve args for browse_dataset and support RepeatDataset (#340, #338)

Contributors

A total of 28 developers contributed to this release.

Thank @RangeKing, @PeterH0323, @Nioolek, @triple-Mu, @matrixgame2018, @xin-li-67, @tang576225574, @kitecats, @Seperendity, @diplomatist, @vaew, @wzr-skn, @VoyagerXvoyagerx, @MambaWong, @tianleiSHI, @caj-github, @zhubochao, @lvhan028, @dsghaonan, @lyviva, @yuewangg, @wang-tf, @satuoqaq, @grimoire, @RunningLeon, @hanrui1sensetime, @RangiLyu, @hhaAndroid

v0.2.0（1/12/2022)

Highlights

  1. Support YOLOv7 P5 and P6 model

  2. Support YOLOv6 ML model

  3. Support Grad-Based CAM and Grad-Free CAM

  4. Support large image inference based on sahi

  5. Add easydeploy project under the projects folder

  6. Add custom dataset guide

New Features

  1. browse_dataset.py script supports visualization of original image, data augmentation and intermediate results (#304)

  2. Add flag to output labelme label file in image_demo.py (#288, #314)

  3. Add labelme2coco script (#308, #313)

  4. Add split COCO dataset script (#311)

  5. Add two examples of backbone replacement in how-to.md and update plugin.md (#291)

  6. Add contributing.md and code_style.md (#322)

  7. Add docs about how to use mim to run scripts across libraries (#321)

  8. Support YOLOv5 deployment at RV1126 device (#262)

Bug Fixes

  1. Fix MixUp padding error (#319)

  2. Fix scale factor order error of LetterResize and YOLOv5KeepRatioResize (#305)

  3. Fix training errors of YOLOX Nano model (#285)

  4. Fix RTMDet deploy error (#287)

  5. Fix int8 deploy config (#315)

  6. Fix make_stage_plugins doc in basebackbone (#296)

  7. Enable switch to deploy when create pytorch model in deployment (#324)

  8. Fix some errors in RTMDet model graph (#317)

Improvements

  1. Add option of json output in test.py (#316)

  2. Add area condition in extract_subcoco.py script (#286)

  3. Deployment doc translation (#289)

  4. Add YOLOv6 description overview doc (#252)

  5. Improve config.md (#297, #303) 6Add mosaic9 graph in docstring (#307)

  6. Improve browse_coco_json.py script args (#309)

  7. Refactor some functions in dataset_analysis.py to be more general (#294)

Contributors

A total of 14 developers contributed to this release.

Thank @fcakyon, @matrixgame2018, @MambaWong, @imAzhou, @triple-Mu, @RangeKing, @PeterH0323, @xin-li-67, @kitecats, @hanrui1sensetime, @AllentDan, @Zheng-LinXiao, @hhaAndroid, @wanghonglie

v0.1.3（10/11/2022)

New Features

  1. Support CBAM plug-in and provide plug-in documentation (#246)

  2. Add YOLOv5 P6 model structure diagram and related descriptions (#273)

Bug Fixes

  1. Fix training failure when saving best weights based on mmengine 0.3.1

  2. Fix add_dump_metric error based on mmdet 3.0.0rc3 (#253)

  3. Fix backbone does not support init_cfg issue (#272)

  4. Change typing import method based on mmdet 3.0.0rc3 (#261)

Improvements

  1. featmap_vis_demo support for folder and url input (#248)

  2. Deploy docker file refinement (#242)

Contributors

A total of 10 developers contributed to this release.

Thank @kitecats, @triple-Mu, @RangeKing, @PeterH0323, @Zheng-LinXiao, @tkhe, @weikai520, @zytx121, @wanghonglie, @hhaAndroid

v0.1.2（3/11/2022)

Highlights

  1. Support YOLOv5/YOLOv6/YOLOX/RTMDet deployments for ONNXRuntime and TensorRT

  2. Support YOLOv6 s/t/n model training

  3. YOLOv5 supports P6 model training which can input 1280-scale images

  4. YOLOv5 supports VOC dataset training

  5. Support PPYOLOE and YOLOv7 model inference and official weight conversion

  6. Add YOLOv5 replacement backbone tutorial in How-to documentation

New Features

  1. Add optimize_anchors script (#175)

  2. Add extract_subcoco script (#186)

  3. Add yolo2coco conversion script (#161)

  4. Add dataset_analysis script (#172)

  5. Remove Albu version restrictions (#187)

Bug Fixes

  1. Fix the problem that cfg.resume does not work when set (#221)

  2. Fix the problem of not showing bbox in feature map visualization script (#204)

  3. uUpdate the metafile of RTMDet (#188)

  4. Fix a visualization error in test_pipeline (#166)

  5. Update badges (#140)

Improvements

  1. Optimize Readthedoc display page (#209)

  2. Add docstring for module structure diagram for base model (#196)

  3. Support for not including any instance logic in LoadAnnotations (#161)

  4. Update image_demo script to support folder and url paths (#128)

  5. Update pre-commit hook (#129)

Documentation

  1. Translate yolov5_description.md, yolov5_tutorial.md and visualization.md into English (#138, #198, #206)

  2. Add deployment-related Chinese documentation (#220)

  3. Update config.md, faq.md and pull_request_template.md (#190, #191, #200)

  4. Update the article page (#133)

Contributors

A total of 14 developers contributed to this release.

Thank @imAzhou, @triple-Mu, @RangeKing, @PeterH0323, @xin-li-67, @Nioolek, @kitecats, @Bin-ze, @JiayuXu0, @cydiachen, @zhiqwang, @Zheng-LinXiao, @hhaAndroid, @wanghonglie

v0.1.1（29/9/2022)

Based on MMDetection’s RTMDet high precision and low latency object detection algorithm, we have also released RTMDet and provided a Chinese document on the principle and implementation of RTMDet.

Highlights

  1. Support RTMDet

  2. Support for backbone customization plugins and update How-to documentation (#75)

Bug Fixes

  1. Fix some documentation errors (#66, #72, #76, #83, #86)

  2. Fix checkpoints link error (#63)

  3. Fix the bug that the output of LetterResize does not meet the expectation when using imscale (#105)

Improvements

  1. Reducing the size of docker images (#67)

  2. Simplifying Compose Logic in BaseMixImageTransform (#71)

  3. Supports dump results in test.py (#84)

Contributors

A total of 13 developers contributed to this release.

Thank @wanghonglie, @hhaAndroid, @yang-0201, @PeterH0323, @RangeKing, @satuoqaq, @Zheng-LinXiao, @xin-li-67, @suibe-qingtian, @MambaWong, @MichaelCai0912, @rimoire, @Nioolek

v0.1.0（21/9/2022)

We have released MMYOLO open source library, which is based on MMEngine, MMCV 2.x and MMDetection 3.x libraries. At present, the object detection has been realized, and it will be expanded to multi-task in the future.

Highlights

  1. Support YOLOv5/YOLOX training, support YOLOv6 inference. Deployment will be supported soon.

  2. Refactored YOLOX from MMDetection to accelerate training and inference.

  3. Detailed introduction and advanced tutorials are provided, see the English tutorial.

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