diff --git a/.circleci/test.yml b/.circleci/test.yml index 809c1f311f1..994d4b94e01 100644 --- a/.circleci/test.yml +++ b/.circleci/test.yml @@ -69,7 +69,7 @@ jobs: command: | python -m pip install git+ssh://git@github.com/open-mmlab/mmengine.git@main pip install -U openmim - mim install 'mmcv >= 2.0.0rc0' + mim install 'mmcv >= 2.0.0rc4' pip install -r requirements/tests.txt -r requirements/optional.txt pip install --force-reinstall pycocotools pip install albumentations>=0.3.2 --no-binary imgaug,albumentations @@ -117,7 +117,7 @@ jobs: command: | docker exec mmdetection pip install -e /mmengine docker exec mmdetection pip install -U openmim - docker exec mmdetection mim install 'mmcv >= 2.0.0rc0' + docker exec mmdetection mim install 'mmcv >= 2.0.0rc4' docker exec mmdetection pip install -r requirements/tests.txt -r requirements/optional.txt docker exec mmdetection pip install pycocotools docker exec mmdetection pip install albumentations>=0.3.2 --no-binary imgaug,albumentations diff --git a/README.md b/README.md index 53d516fcdc2..2cbbe559f1f 100644 --- a/README.md +++ b/README.md @@ -108,10 +108,14 @@ We are excited to announce our latest work on real-time object recognition tasks -**v3.0.0rc5** was released in 26/12/2022: +**v3.0.0rc6** was released in 24/2/2023: -- Support [RTMDet](https://arxiv.org/abs/2212.07784) instance segmentation models. The technical report of RTMDet is on [arxiv](https://arxiv.org/abs/2212.07784) -- Support SSHContextModule in paper [SSH: Single Stage Headless Face Detector](https://arxiv.org/abs/1708.03979) +- Support [Boxinst](configs/boxinst), [Objects365 Dataset](configs/objects365), and [Separated and Occluded COCO metric](docs/en/user_guides/useful_tools.md#coco-separated--occluded-mask-metric) +- Support [ConvNeXt-V2](projects/ConvNeXt-V2), [DiffusionDet](projects/DiffusionDet), and inference of [EfficientDet](projects/EfficientDet) and [Detic](projects/Detic) in `Projects` +- Refactor [DETR](configs/detr) series and support [Conditional-DETR](configs/conditional_detr), [DAB-DETR](configs/dab_detr), and [DINO](configs/dino) +- Support `DetInferencer` for inference, Test Time Augmentation, and automatically importing modules from registry +- Support RTMDet-Ins ONNXRuntime and TensorRT [deployment](configs/rtmdet/README.md#deployment-tutorial) +- Support [calculating FLOPs of detectors](docs/en/user_guides/useful_tools.md#Model-Complexity) ## Installation @@ -220,6 +224,12 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
  • TOOD (ICCV'2021)
  • DDOD (ACM MM'2021)
  • RTMDet (ArXiv'2022)
    • +
  • Conditional DETR (ICCV'2021)
    • +
  • DAB-DETR (ICLR'2022)
    • +
  • DINO (ICLR'2023)
    • +
  • DiffusionDet (ArXiv'2023)
    • +
  • EfficientDet (CVPR'2020)
    • +
  • Detic (ECCV'2022)
    • @@ -237,9 +247,10 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
  • SCNet (AAAI'2021)
  • QueryInst (ICCV'2021)
  • Mask2Former (ArXiv'2021)
    • -
  • CondInst (ECCV 2020)
    • -
  • SparseInst (CVPR 2022)
    • +
  • CondInst (ECCV'2020)
    • +
  • SparseInst (CVPR'2022)
  • RTMDet (ArXiv'2022)
    • +
  • BoxInst (CVPR'2021)
    • @@ -319,6 +330,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
  • ResNet strikes back (ArXiv'2021)
  • EfficientNet (ArXiv'2021)
  • ConvNeXt (CVPR'2022)
    • +
  • ConvNeXtv2 (ArXiv'2023)
    • diff --git a/README_zh-CN.md b/README_zh-CN.md index 21c133f876e..7f68b926957 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -93,10 +93,14 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope -**v3.0.0rc5** 版本已经在 2022.12.26 发布： +**v3.0.0rc6** 版本已经在 2023.2.24 发布： -- 支持了 [RTMDet](https://arxiv.org/abs/2212.07784) 的实例分割模型。RTMDet 的技术报告发布在了 [arxiv](https://arxiv.org/abs/2212.07784) 上。 -- 支持了 [SSH: Single Stage Headless Face Detector](https://arxiv.org/abs/1708.03979) 论文中的 SSHContextModule +- 支持了 [Boxinst](configs/boxinst), [Objects365 Dataset](configs/objects365) 和 [Separated and Occluded COCO metric](docs/zh_cn/user_guides/useful_tools.md#coco-分离和遮挡实例分割性能评估) +- 在 `Projects` 中支持了 [ConvNeXt-V2](projects/ConvNeXt-V2), [DiffusionDet](projects/DiffusionDet) 和 [EfficientDet](projects/EfficientDet), [Detic](projects/Detic) 的推理 +- 重构了 [DETR](configs/detr) 系列并支持了 [Conditional-DETR](configs/conditional_detr), [DAB-DETR](configs/dab_detr) 和 [DINO](configs/dino) +- 支持了通过 `DetInferencer` 用于推理, Test Time Augmentation 以及从注册表(registry)自动导入模块 +- 支持了 RTMDet-Ins 的 ONNXRuntime 和 TensorRT [部署](configs/rtmdet/README.md#deployment-tutorial) +- 支持了检测器[计算 FLOPS](docs/zh_cn/user_guides/useful_tools.md#模型复杂度) ## 安装 @@ -206,7 +210,13 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
  • Deformable DETR (ICLR'2021)
  • TOOD (ICCV'2021)
  • DDOD (ACM MM'2021)
    • -
  • RTMDet (ArXiv'2022)
    • +
  • RTMDet (ArXiv'2022)
    • +
  • Conditional DETR (ICCV'2021)
    • +
  • DAB-DETR (ICLR'2022)
    • +
  • DINO (ICLR'2023)
    • +
  • DiffusionDet (ArXiv'2023)
    • +
  • EfficientDet (CVPR'2020)
    • +
  • Detic (ECCV'2022)
    • @@ -224,9 +234,10 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
  • SCNet (AAAI'2021)
  • QueryInst (ICCV'2021)
  • Mask2Former (ArXiv'2021)
    • -
  • CondInst (ECCV 2020)
    • -
  • SparseInst (CVPR 2022)
    • -
  • RTMDet (ArXiv'2022)
    • +
  • CondInst (ECCV'2020)
    • +
  • SparseInst (CVPR'2022)
    • +
  • RTMDet (ArXiv'2022)
    • +
  • BoxInst (CVPR'2021)
    • @@ -306,6 +317,7 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
  • ResNet strikes back (ArXiv'2021)
  • EfficientNet (ArXiv'2021)
  • ConvNeXt (CVPR'2022)
    • +
  • ConvNeXtv2 (ArXiv'2023)
    • diff --git a/docker/Dockerfile b/docker/Dockerfile index 2385017213e..4c804044c7a 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -29,7 +29,7 @@ RUN apt-get update \ # Install MMEngine and MMCV RUN pip install openmim && \ - mim install "mmengine==0.3.0" "mmcv>=2.0.0rc1" + mim install "mmengine>=0.6.0" "mmcv>=2.0.0rc4" # Install MMDetection RUN conda clean --all \ diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile index d10f79682d9..7a215f935ab 100644 --- a/docker/serve/Dockerfile +++ b/docker/serve/Dockerfile @@ -3,8 +3,8 @@ ARG CUDA="11.1" ARG CUDNN="8" FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel -ARG MMCV="2.0.0rc1" -ARG MMDET="3.0.0rc5" +ARG MMCV="2.0.0rc4" +ARG MMDET="3.0.0rc6" ENV PYTHONUNBUFFERED TRUE diff --git a/docker/serve_cn/Dockerfile b/docker/serve_cn/Dockerfile index 4a8abd93429..7812d8b7198 100644 --- a/docker/serve_cn/Dockerfile +++ b/docker/serve_cn/Dockerfile @@ -3,8 +3,8 @@ ARG CUDA="11.1" ARG CUDNN="8" FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel -ARG MMCV="2.0.0rc1" -ARG MMDET="3.0.0rc5" +ARG MMCV="2.0.0rc4" +ARG MMDET="3.0.0rc6" ENV PYTHONUNBUFFERED TRUE diff --git a/docs/en/notes/changelog.md b/docs/en/notes/changelog.md index d382012b57f..4e8bb27a742 100644 --- a/docs/en/notes/changelog.md +++ b/docs/en/notes/changelog.md @@ -1,5 +1,88 @@ # Changelog of v3.x +## v3.0.0rc6 (24/2/2023) + +### Highlights + +- Support [Boxinst](../../../configs/boxinst), [Objects365 Dataset](../../../configs/objects365), and [Separated and Occluded COCO metric](../user_guides/useful_tools.md#COCO-Separated-&-Occluded-Mask-Metric) +- Support [ConvNeXt-V2](../../../projects/ConvNeXt-V2), [DiffusionDet](../../../projects/DiffusionDet), and inference of [EfficientDet](../../../projects/EfficientDet) and [Detic](../../../projects/Detic) in `Projects` +- Refactor [DETR](../../../configs/detr) series and support [Conditional-DETR](../../../configs/conditional_detr), [DAB-DETR](../../../configs/dab_detr), and [DINO](../../../configs/detr) +- Support `DetInferencer` for inference, Test Time Augmentation, and automatically importing modules from registry +- Support RTMDet-Ins ONNXRuntime and TensorRT [deployment](../../../configs/rtmdet/README.md#deployment-tutorial) +- Support [calculating FLOPs of detectors](../user_guides/useful_tools.md#Model-Complexity) + +### New Features + +- Support [Boxinst](https://arxiv.org/abs/2012.02310) (#9525) +- Support [Objects365 Dataset](https://openaccess.thecvf.com/content_ICCV_2019/papers/Shao_Objects365_A_Large-Scale_High-Quality_Dataset_for_Object_Detection_ICCV_2019_paper.pdf) (#9600) +- Support [ConvNeXt-V2](http://arxiv.org/abs/2301.00808) in `Projects` (#9619) +- Support [DiffusionDet](https://arxiv.org/abs/2211.09788) in `Projects` (#9639, #9768) +- Support [Detic](http://arxiv.org/abs/2201.02605) inference in `Projects` (#9645) +- Support [EfficientDet](https://arxiv.org/abs/1911.09070) inference in `Projects` (#9645) +- Support [Separated and Occluded COCO metric](https://arxiv.org/abs/2210.10046) (#9710) +- Support auto import modules from registry (#9143) +- Refactor DETR series and support Conditional-DETR, DAB-DETR and DINO (#9646) +- Support `DetInferencer` for inference (#9561) +- Support Test Time Augmentation (#9452) +- Support calculating FLOPs of detectors (#9777) + +### Bug Fixes + +- Fix deprecating old type alias due to new version of numpy (#9625, #9537) +- Fix VOC metrics (#9784) +- Fix the wrong link of RTMDet-x log (#9549) +- Fix RTMDet link in README (#9575) +- Fix MMDet get flops error (#9589) +- Fix `use_depthwise` in RTMDet (#9624) +- Fix `albumentations` augmentation post process with masks (#9551) +- Fix DETR series Unit Test (#9647) +- Fix `LoadPanopticAnnotations` bug (#9703) +- Fix `isort` CI (#9680) +- Fix amp pooling overflow (#9670) +- Fix docstring about noise in DINO (#9747) +- Fix potential bug in `MultiImageMixDataset` (#9764) + +### Improvements + +- Replace NumPy transpose with PyTorch permute to speed-up (#9762) +- Deprecate `sklearn` (#9725) +- Add RTMDet-Ins deployment guide (#9823) +- Update RTMDet config and README (#9603) +- Replace the models used in the tutorial document with RTMDet (#9843) +- Adjust the minimum supported python version to 3.7 (#9602) +- Support modifying palette through configuration (#9445) +- Update README document in `Project` (#9599) +- Replace `github` with `gitee` in `.pre-commit-config-zh-cn.yaml` file (#9586) +- Use official `isort` in `.pre-commit-config.yaml` file (#9701) +- Change MMCV minimum version to `2.0.0rc4` for `dev-3.x` (#9695) +- Add Chinese version of single_stage_as_rpn.md and test_results_submission.md (#9434) +- Add OpenDataLab download link (#9605, #9738) +- Add type hints of several layers (#9346) +- Add typehint for `DarknetBottleneck` (#9591) +- Add dockerfile (#9659) +- Add twitter, discord, medium, and youtube link (#9775) +- Prepare for merging refactor-detr (#9656) +- Add metafile to ConditionalDETR, DABDETR and DINO (#9715) +- Support to modify `non_blocking` parameters (#9723) +- Comment repeater visualizer register (#9740) +- Update user guide: `finetune.md` and `inference.md` (#9578) + +### New Contributors + +- @NoFish-528 made their first contribution in +- @137208 made their first contribution in +- @lyviva made their first contribution in +- @zwhus made their first contribution in +- @zylo117 made their first contribution in +- @chg0901 made their first contribution in +- @DanShouzhu made their first contribution in https://github.com/open-mmlab/mmdetection/pull/9578 + +### Contributors + +A total of 27 developers contributed to this release. + +Thanks @JosonChan1998, @RangeKing, @NoFish-528, @likyoo, @Xiangxu-0103, @137208, @PeterH0323, @tianleiSHI, @wufan-tb, @lyviva, @zwhus, @jshilong, @Li-Qingyun, @sanbuphy, @zylo117, @triple-Mu, @KeiChiTse, @LYMDLUT, @nijkah, @chg0901, @DanShouzhu, @zytx121, @vansin, @BIGWangYuDong, @hhaAndroid, @RangiLyu, @ZwwWayne + ## v3.0.0rc5 (26/12/2022) ### Highlights @@ -25,6 +108,7 @@ - Fix demo API in instance segmentation tutorial (#9226) - Fix `analyze_results` (#9380) - Fix the error that Readthedocs API cannot be displayed (#9510) +- Fix the error when there are no prediction results and support visualize the groundtruth of TTA (#9840) ### Improvements diff --git a/docs/en/notes/faq.md b/docs/en/notes/faq.md index 389d195f299..f93b4a84f47 100644 --- a/docs/en/notes/faq.md +++ b/docs/en/notes/faq.md @@ -10,8 +10,8 @@ We list some common troubles faced by many users and their corresponding solutio | MMDetection version | MMCV version | MMEngine version | | :-----------------: | :---------------------: | :----------------------: | - | 3.x | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.4.0, \<1.0.0 | - | 3.0.0rc6 | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 | + | 3.x | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.6.0, \<1.0.0 | + | 3.0.0rc6 | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.6.0, \<1.0.0 | | 3.0.0rc5 | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 | | 3.0.0rc4 | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 | | 3.0.0rc3 | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 | diff --git a/docs/zh_cn/notes/faq.md b/docs/zh_cn/notes/faq.md index 52d975c5aef..bca80ba18ba 100644 --- a/docs/zh_cn/notes/faq.md +++ b/docs/zh_cn/notes/faq.md @@ -10,8 +10,8 @@ | MMDetection 版本 | MMCV 版本 | MMEngine 版本 | | :--------------: | :---------------------: | :----------------------: | - | 3.x | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.4.0, \<1.0.0 | - | 3.0.0rc6 | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 | + | 3.x | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.6.0, \<1.0.0 | + | 3.0.0rc6 | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.6.0, \<1.0.0 | | 3.0.0rc5 | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 | | 3.0.0rc4 | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 | | 3.0.0rc3 | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 | diff --git a/mmdet/__init__.py b/mmdet/__init__.py index 4946da851eb..d48c523bc79 100644 --- a/mmdet/__init__.py +++ b/mmdet/__init__.py @@ -9,7 +9,7 @@ mmcv_maximum_version = '2.1.0' mmcv_version = digit_version(mmcv.__version__) -mmengine_minimum_version = '0.4.0' +mmengine_minimum_version = '0.6.0' mmengine_maximum_version = '1.0.0' mmengine_version = digit_version(mmengine.__version__) diff --git a/mmdet/version.py b/mmdet/version.py index 26b6d6c8b7a..56a7e9d62ce 100644 --- a/mmdet/version.py +++ b/mmdet/version.py @@ -1,6 +1,6 @@ # Copyright (c) OpenMMLab. All rights reserved. -__version__ = '3.0.0rc5' +__version__ = '3.0.0rc6' short_version = __version__