Bump version to 3.1.0 (#10571)

open-mmlab · Jun 29, 2023 · 8822264 · 8822264
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diff --git a/README.md b/README.md
@@ -70,7 +70,7 @@ English | [简体中文](README_zh-CN.md)
 MMDetection is an open source object detection toolbox based on PyTorch. It is
 a part of the [OpenMMLab](https://openmmlab.com/) project.
 
-The main branch works with **PyTorch 1.6+**.
+The main branch works with **PyTorch 1.8+**.
 
 <img src="https://user-images.githubusercontent.com/12907710/187674113-2074d658-f2fb-42d1-ac15-9c4a695e64d7.png"/>
 
@@ -118,11 +118,11 @@ 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>
 
-**v3.0.0** was released in 6/4/2023:
+**v3.1.0** was released in 30/6/2023:
 
-- Release MMDetection 3.0.0 official version
-- Support Semi-automatic annotation Base [Label-Studio](projects/LabelStudio) (#10039)
-- Support [EfficientDet](projects/EfficientDet) in projects (#9810)
+- Supports tracking algorithms including multi-object tracking (MOT) algorithms SORT, DeepSORT, StrongSORT, OCSORT, ByteTrack, QDTrack, and video instance segmentation (VIS) algorithm MaskTrackRCNN, Mask2Former-VIS.
+- Supports inference and evaluation of multimodal algorithms [GLIP](configs/glip) and [XDecoder](projects/XDecoder), and also supports datasets such as COCO semantic segmentation, COCO Caption, ADE20k general segmentation, and RefCOCO. GLIP fine-tuning will be supported in the future.
+- Provides a [gradio demo](https://github.com/open-mmlab/mmdetection/blob/dev-3.x/projects/gradio_demo/README.md) for image type tasks of MMDetection, making it easy for users to experience.
 
 ## Installation
 

diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -69,7 +69,7 @@
 
 MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
 
-主分支代码目前支持 PyTorch 1.6 以上的版本。
+主分支代码目前支持 PyTorch 1.8 及其以上的版本。
 
 <img src="https://user-images.githubusercontent.com/12907710/187674113-2074d658-f2fb-42d1-ac15-9c4a695e64d7.png"/>
 
@@ -117,11 +117,11 @@ 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>
 
-**v3.0.0** 版本已经在 2023.4.6 发布：
+**v3.1.0** 版本已经在 2023.6.30 发布：
 
-- 发布 MMDetection 3.0.0 正式版
-- 基于 [Label-Studio](projects/LabelStudio) 支持半自动标注流程
-- projects 中支持了 [EfficientDet](projects/EfficientDet)
+- 支持 Tracking 类算法，包括多目标跟踪 MOT 算法 SORT、DeepSORT、StrongSORT、OCSORT、ByteTrack、QDTrack 和视频实例分割 VIS 算法 MaskTrackRCNN、Mask2Former-VIS。
+- 支持多模态开放检测算法 [GLIP](configs/glip) 和 [XDecoder](projects/XDecoder) 推理和评估，并同时支持了 COCO 语义分割、COCO Caption、ADE20k 通用分割、RefCOCO 等数据集。后续将支持 GLIP 微调
+- 提供了包括 MMDetection 图片任务的 [gradio demo](https://github.com/open-mmlab/mmdetection/blob/dev-3.x/projects/gradio_demo/README.md)，方便用户快速体验
 
 ## 安装
 

diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile
@@ -4,7 +4,7 @@ ARG CUDNN="8"
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
 ARG MMCV="2.0.0rc4"
-ARG MMDET="3.0.0"
+ARG MMDET="3.1.0"
 
 ENV PYTHONUNBUFFERED TRUE
 

diff --git a/docker/serve_cn/Dockerfile b/docker/serve_cn/Dockerfile
@@ -4,7 +4,7 @@ ARG CUDNN="8"
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
 ARG MMCV="2.0.0rc4"
-ARG MMDET="3.0.0"
+ARG MMDET="3.1.0"
 
 ENV PYTHONUNBUFFERED TRUE
 

diff --git a/docs/en/get_started.md b/docs/en/get_started.md
@@ -4,7 +4,7 @@
 
 In this section, we demonstrate how to prepare an environment with PyTorch.
 
-MMDetection works on Linux, Windows, and macOS. It requires Python 3.7+, CUDA 9.2+, and PyTorch 1.6+.
+MMDetection works on Linux, Windows, and macOS. It requires Python 3.7+, CUDA 9.2+, and PyTorch 1.8+.
 
 ```{note}
 If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](#installation). Otherwise, you can follow these steps for the preparation.

diff --git a/docs/en/notes/changelog.md b/docs/en/notes/changelog.md
@@ -1,5 +1,44 @@
 # Changelog of v3.x
 
+## v3.1.0 (30/6/2023)
+
+### Highlights
+
+- Supports tracking algorithms including multi-object tracking (MOT) algorithms SORT, DeepSORT, StrongSORT, OCSORT, ByteTrack, QDTrack, and video instance segmentation (VIS) algorithm MaskTrackRCNN, Mask2Former-VIS.
+- Supports inference and evaluation of multimodal algorithms [GLIP](../../../configs/glip) and [XDecoder](../../../projects/XDecoder), and also supports datasets such as COCO semantic segmentation, COCO Caption, ADE20k general segmentation, and RefCOCO. GLIP fine-tuning will be supported in the future.
+- Provides a [gradio demo](https://github.com/open-mmlab/mmdetection/blob/dev-3.x/projects/gradio_demo/README.md) for image type tasks of MMDetection, making it easy for users to experience.
+
+### New Features
+
+- Support DSDL Dataset (#9801)
+- Support iSAID dataset (#10028)
+- Support VISION dataset (#10530)
+- Release SoftTeacher checkpoints (#10119)
+- Release `centernet-update_r50-caffe_fpn_ms-1x_coco` checkpoints  (#10327)
+- Support SIoULoss (#10290)
+- Support Eqlv2 loss (#10120)
+- Support CopyPaste when mask is not available (#10509)
+- Support MIM to download ODL dataset (#10460)
+- Support new config (#10566)
+
+### Bug Fixes
+
+- Fix benchmark scripts error in windows (#10128)
+- Fix error of `YOLOXModeSwitchHook` does not switch the mode when resumed from the checkpoint after switched (#10116)
+- Fix pred and weight dims unmatch in SmoothL1Loss (#10423)
+
+### Improvements
+
+- Update MMDet_Tutorial.ipynb (#10081)
+- Support to hide inference progress (#10519)
+- Replace mmcls with mmpretrain  (#10545)
+
+### Contributors
+
+A total of 29 developers contributed to this release.
+
+Thanks @lovelykite, @minato-ellie, @freepoet, @wufan-tb, @yalibian, @keyakiluo, @gihanjayatilaka, @i-aki-y, @xin-li-67, @RangeKing, @JingweiZhang12, @MambaWong, @lucianovk, @tall-josh, @xiuqhou, @jamiechoi1995, @YQisme, @yechenzhi, @bjzhb666, @xiexinch, @jamiechoi1995, @yarkable, @Renzhihan, @nijkah, @amaizr, @Lum1104, @zwhus, @Czm369, @hhaAndroid
+
 ## v3.0.0 (6/4/2023)
 
 ### Highlights

diff --git a/docs/en/notes/faq.md b/docs/en/notes/faq.md
@@ -47,7 +47,8 @@ Compatible MMDetection, MMEngine, and MMCV versions are shown as below. Please c
 | MMDetection version |      MMCV version       |     MMEngine version     |
 | :-----------------: | :---------------------: | :----------------------: |
 |        main         |  mmcv>=2.0.0, \<2.1.0   | mmengine>=0.7.1, \<1.0.0 |
-|         3.x         |  mmcv>=2.0.0, \<2.1.0   | mmengine>=0.7.1, \<1.0.0 |
+|        3.1.0        |  mmcv>=2.0.0, \<2.1.0   | mmengine>=0.7.1, \<1.0.0 |
+|        3.0.0        |  mmcv>=2.0.0, \<2.1.0   | mmengine>=0.7.1, \<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 |

diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md
@@ -4,7 +4,7 @@
 
 本节中，我们将演示如何用 PyTorch 准备一个环境。
 
-MMDetection 支持在 Linux，Windows 和 macOS 上运行。它需要 Python 3.7 以上，CUDA 9.2 以上和 PyTorch 1.6 以上。
+MMDetection 支持在 Linux，Windows 和 macOS 上运行。它需要 Python 3.7 以上，CUDA 9.2 以上和 PyTorch 1.8 及其以上。
 
 ```{note}
 如果你对 PyTorch 有经验并且已经安装了它，你可以直接跳转到[下一小节](#安装流程)。否则，你可以按照下述步骤进行准备。

diff --git a/docs/zh_cn/notes/faq.md b/docs/zh_cn/notes/faq.md
@@ -47,7 +47,8 @@ export DYNAMO_CACHE_SIZE_LIMIT = 4
   | MMDetection 版本 |        MMCV 版本        |      MMEngine 版本       |
   | :--------------: | :---------------------: | :----------------------: |
   |       main       |  mmcv>=2.0.0, \<2.1.0   | mmengine>=0.7.1, \<1.0.0 |
-  |       3.x        |  mmcv>=2.0.0, \<2.1.0   | mmengine>=0.7.1, \<1.0.0 |
+  |      3.1.0       |  mmcv>=2.0.0, \<2.1.0   | mmengine>=0.7.1, \<1.0.0 |
+  |      3.0.0       |  mmcv>=2.0.0, \<2.1.0   | mmengine>=0.7.1, \<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 |