Bump version to v2.28.2 (#9820)

README.md

@@ -90,11 +90,9 @@ Apart from MMDetection, we also released a library [mmcv](https://github.com/ope
 
 ### 💎 Stable version
 
-**2.28.1** was released in 1/2/2023:
+**2.28.2** was released in 27/2/2023:
 
-- Support Objects365 Dataset, and Separated and Occluded COCO metric
-- Support acceleration of RetinaNet and SSD on Ascend
-- Deprecate the support of Python 3.6 and fix some bugs of 2.28.0
+- Fixed some known documentation, configuration and linking error issues
 
 Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
@@ -120,10 +118,14 @@ We are excited to announce our latest work on real-time object recognition tasks
 <img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
 </div>
 
-A brand new version of **MMDetection v3.0.0rc5** was released in 26/12/2022:
+A brand new version of **MMDetection v3.0.0rc6** was released in 27/2/2023:
 
-- 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](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/boxinst), [Objects365 Dataset](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/objects365), and [Separated and Occluded COCO metric](https://github.com/open-mmlab/mmdetection/tree/3.x/docs/en/user_guides/useful_tools.md#coco-separated--occluded-mask-metric)
+- Support [ConvNeXt-V2](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/ConvNeXt-V2), [DiffusionDet](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/DiffusionDet), and inference of [EfficientDet](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/EfficientDet) and [Detic](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/Detic) in `Projects`
+- Refactor [DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/detr) series and support [Conditional-DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/conditional_detr), [DAB-DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/dab_detr), and [DINO](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/dino)
+- Support DetInferencer, Test Time Augmentation, and auto import modules from registry
+- Support RTMDet-Ins ONNXRuntime and TensorRT [deployment](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet/README.md#deployment-tutorial)
+- Support [calculating FLOPs of detectors](https://github.com/open-mmlab/mmdetection/tree/3.x/docs/en/user_guides/useful_tools.md#Model-Complexity)
 
 Find more new features in [3.x branch](https://github.com/open-mmlab/mmdetection/tree/3.x). Issues and PRs are welcome!
 

README_zh-CN.md

@@ -76,11 +76,9 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
 
 ### 💎 稳定版本
 
-最新的 **2.28.1** 版本已经在 2023.2.1 发布:
+最新的 **2.28.2** 版本已经在 2023.2.27 发布:
 
-- 支持 Object365 数据集和遮挡物检测的 benchmark
-- 支持 SSD 和 RetinaNet 算法在昇腾芯片上的加速
-- 不再保证对 Python 3.6 的支持并修复了 2.28.0 的一些 bug
+- 修复了一些已知的文档、配置和链接错误问题
 
 如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/changelog.md)。
 
@@ -106,10 +104,14 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
 <img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
 </div>
 
-全新的 **v3.0.0rc5** 版本已经在 2022.12.26 发布：
+全新的 **v3.0.0rc6** 版本已经在 2023.2.27 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](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/boxinst), [Objects365 Dataset](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/objects365) 和 [Separated and Occluded COCO metric](https://github.com/open-mmlab/mmdetection/tree/3.x/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](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/ConvNeXt-V2), [DiffusionDet](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/DiffusionDet) 和 [EfficientDet](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/EfficientDet), [Detic](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/Detic) 的推理
+- 重构了 [DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/detr) 系列并支持了 [Conditional-DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/conditional_detr), [DAB-DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/dab_detr) 和 [DINO](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/dino)
+- 支持了 DetInferencer, 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](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet/README.md#deployment-tutorial)
+- 支持了检测器[计算 FLOPS](https://github.com/open-mmlab/mmdetection/tree/3.x/docs/zh_cn/user_guides/useful_tools.md#模型复杂度)
 
 ## 安装
 

docker/serve/Dockerfile

@@ -4,7 +4,7 @@ ARG CUDNN="7"
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
 ARG MMCV="1.3.17"
-ARG MMDET="2.28.1"
+ARG MMDET="2.28.2"
 
 ENV PYTHONUNBUFFERED TRUE
 

docs/en/changelog.md

@@ -1,5 +1,23 @@
 ## Changelog
 
+### v2.28.2 (24/2/2023)
+
+#### New Features and Improvements
+
+- Add Twitter, Discord, Medium and YouTube link (#9774)
+- Update `customize_runtime.md` (#9797)
+
+#### Bug Fixes
+
+- Fix `WIDERFace SSD` loss for Nan problem (#9734)
+- Fix missing API documentation in Readthedoc (#9729)
+- Fix the configuration file and log path of CenterNet (#9791)
+
+#### Contributors
+
+A total of 4 developers contributed to this release.
+Thanks @co63oc, @Ginray, @vansin, @RangiLyu
+
 ### v2.28.1 (1/2/2023)
 
 #### Bug Fixes

docs/en/faq.md

@@ -11,6 +11,7 @@ We list some common troubles faced by many users and their corresponding solutio
 | MMDetection version |        MMCV version        |
 | :-----------------: | :------------------------: |
 |       master        | mmcv-full>=1.3.17, \<1.8.0 |
+|       2.28.2        | mmcv-full>=1.3.17, \<1.8.0 |
 |       2.28.1        | mmcv-full>=1.3.17, \<1.8.0 |
 |       2.28.0        | mmcv-full>=1.3.17, \<1.8.0 |
 |       2.27.0        | mmcv-full>=1.3.17, \<1.8.0 |

docs/zh_cn/get_started.md

@@ -12,6 +12,7 @@ MMDetection 和 MMCV 版本兼容性如下所示，需要安装正确的 MMCV 
 | MMDetection 版本 |         MMCV 版本          |
 | :--------------: | :------------------------: |
 |      master      | mmcv-full>=1.3.17, \<1.8.0 |
+|      2.28.2      | mmcv-full>=1.3.17, \<1.8.0 |
 |      2.28.1      | mmcv-full>=1.3.17, \<1.8.0 |
 |      2.28.0      | mmcv-full>=1.3.17, \<1.8.0 |
 |      2.27.0      | mmcv-full>=1.3.17, \<1.8.0 |

mmdet/version.py

@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 
-__version__ = '2.28.1'
+__version__ = '2.28.2'
 short_version = __version__