Bump version to 3.0.0rc5 (#9532)

* Bump version to 3.0.0rc5

* update

* minor fix

* update release date

* Update README.md

Co-authored-by: Wenwei Zhang <[email protected]>

* update date

* update changelog

* update zh-cn readme

* update changelog

* update

Co-authored-by: Wenwei Zhang <[email protected]>

Author: BIGWangYuDong

README.md

@@ -94,11 +94,10 @@ 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>
 
-**v3.0.0rc4** was released in 25/11/2022:
+**v3.0.0rc5** was released in 26/12/2022:
 
-- Support [CondInst](https://arxiv.org/abs/2003.05664)
-- Add [`projects/`](./projects/) folder and give an [example](./projects/example_project/README.md) for communities to contribute their projects.
-- Support [SparseInst](https://arxiv.org/abs/2203.12827) in [`projects`](./projects/SparseInst/README.md)
+- 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)
 
 ## Installation
 

README_zh-CN.md

@@ -77,7 +77,7 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
 
 ### 亮点
 
-我们很高兴向大家介绍我们在实时目标识别任务方面的最新成果 RTMDet，包含了一系列的全卷积单阶段检测模型。 RTMDet 不仅在从 tiny 到 extra-large 尺寸的目标检测模型上上实现了最佳的参数量和精度的平衡，而且在实时实例分割和旋转目标检测任务上取得了最先进的成果。 更多细节请参阅[技术报告](https://arxiv.org/abs/2212.07784)。 预训练模型可以在[这里](configs/rtmdet)找到。
+我们很高兴向大家介绍我们在实时目标识别任务方面的最新成果 RTMDet，包含了一系列的全卷积单阶段检测模型。 RTMDet 不仅在从 tiny 到 extra-large 尺寸的目标检测模型上实现了最佳的参数量和精度的平衡，而且在实时实例分割和旋转目标检测任务上取得了最先进的成果。 更多细节请参阅[技术报告](https://arxiv.org/abs/2212.07784)。 预训练模型可以在[这里](configs/rtmdet)找到。
 
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/real-time-instance-segmentation-on-mscoco)](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-dota-1)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
@@ -93,11 +93,10 @@ 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>
 
-**v3.0.0rc4** 版本已经在 2022.11.25 发布：
+**v3.0.0rc5** 版本已经在 2022.12.26 发布：
 
-- 支持了 [CondInst](https://arxiv.org/abs/2003.05664)
-- 添加了 [`projects/`](./projects/) 文件夹并提供了[示例](./projects/example_project/README.md)，方便社区快速地贡献相关项目
-- 在 [`projects`](./projects/SparseInst/README.md) 中支持了 [SparseInst](https://arxiv.org/abs/2203.12827)
+- 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
 
 ## 安装
 

docker/serve/Dockerfile

@@ -4,7 +4,7 @@ ARG CUDNN="8"
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
 ARG MMCV="2.0.0rc1"
-ARG MMDET="3.0.0rc4"
+ARG MMDET="3.0.0rc5"
 
 ENV PYTHONUNBUFFERED TRUE
 

docs/en/notes/changelog.md

@@ -1,5 +1,65 @@
 # Changelog of v3.x
 
+## v3.0.0rc5 (26/12/2022)
+
+### Highlights
+
+- 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).
+
+### New Features
+
+- Support [RTMDet](https://arxiv.org/abs/2212.07784) instance segmentation models and improve RTMDet test config (#9494)
+- Support SSHContextModule in paper [SSH: Single Stage Headless Face Detector](https://arxiv.org/abs/1708.03979) (#8953)
+- Release [CondInst](https://arxiv.org/abs/2003.05664) pre-trained model (#9406)
+
+### Bug Fixes
+
+- Fix CondInst predict error when `batch_size` is greater than 1 in inference (#9400)
+- Fix the bug of visualization when the dtype of the pipeline output image is not uint8 in browse dataset (#9401)
+- Fix `analyze_logs.py` to plot mAP and calculate train time correctly (#9409)
+- Fix backward inplace error with `PAFPN` (#9450)
+- Fix config import links in model converters (#9441)
+- Fix `DeformableDETRHead` object has no attribute `loss_single` (#9477)
+- Fix the logic of pseudo bboxes predicted by teacher model in SemiBaseDetector (#9414)
+- Fix demo API in instance segmentation tutorial (#9226)
+- Fix `analyze_results` (#9380)
+- Fix the error that Readthedocs API cannot be displayed (#9510)
+
+### Improvements
+
+- Remove legacy `builder.py` (#9479)
+- Make sure the pipeline argument shape is in `(width, height)` order (#9324)
+- Add `.pre-commit-config-zh-cn.yaml` file (#9388)
+- Refactor dataset metainfo to lowercase (#9469)
+- Add PyTorch 1.13 checking in CI (#9478)
+- Adjust `FocalLoss` and `QualityFocalLoss` to allow different kinds of targets (#9481)
+- Refactor `setup.cfg` (#9370)
+- Clip saturation value to valid range `[0, 1]` (#9391)
+- Only keep meta and state_dict when publishing model (#9356)
+- Add segm evaluator in ms-poly_3x_coco_instance config (#9524)
+- Update deployment guide (#9527)
+- Update zh_cn `faq.md` (#9396)
+- Update `get_started` (#9480)
+- Update the zh_cn user_guides of `useful_tools.md` and `useful_hooks.md` (#9453)
+- Add type hints for `bfp` and `channel_mapper` (#9410)
+- Add type hints of several losses (#9397)
+- Add type hints and update docstring for task modules (#9468)
+
+### New Contributors
+
+- @lihua199710 made their first contribution in <https://github.com/open-mmlab/mmdetection/pull/9388>
+- @twmht made their first contribution in <https://github.com/open-mmlab/mmdetection/pull/9450>
+- @tianleiSHI made their first contribution in <https://github.com/open-mmlab/mmdetection/pull/9453>
+- @kitecats made their first contribution in <https://github.com/open-mmlab/mmdetection/pull/9481>
+- @QJC123654 made their first contribution in <https://github.com/open-mmlab/mmdetection/pull/9468>
+
+### Contributors
+
+A total of 20 developers contributed to this release.
+
+Thanks @liuyanyi, @RangeKing, @lihua199710, @MambaWong, @sanbuphy, @Xiangxu-0103, @twmht, @JunyaoHu, @Chan-Sun, @tianleiSHI, @zytx121, @kitecats, @QJC123654, @JosonChan1998, @lvhan028, @Czm369, @BIGWangYuDong, @RangiLyu, @hhaAndroid, @ZwwWayne
+
 ## v3.0.0rc4 (23/11/2022)
 
 ### Highlights

docs/en/notes/faq.md

@@ -11,6 +11,7 @@ We list some common troubles faced by many users and their corresponding solutio
   | MMDetection version |      MMCV version       |     MMEngine version     |
   | :-----------------: | :---------------------: | :----------------------: |
   |         3.x         | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<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 |
   |      3.0.0rc2       | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.1.0, \<1.0.0 |

docs/zh_cn/notes/faq.md

@@ -11,6 +11,7 @@
   | MMDetection 版本 |        MMCV 版本        |      MMEngine 版本       |
   | :--------------: | :---------------------: | :----------------------: |
   |       3.x        | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<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 |
   |     3.0.0rc2     | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.1.0, \<1.0.0 |

mmdet/version.py

@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 
-__version__ = '3.0.0rc4'
+__version__ = '3.0.0rc5'
 short_version = __version__