add changelog (#851)

README.md

@@ -77,17 +77,13 @@ English | [简体中文](README_zh-CN.md)
 
 ## 🥳 🚀 What's New [🔝](#-table-of-contents)
 
-💎 **v0.5.0** was released on 2/3/2023:
-
-1. Support [RTMDet-R](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/README.md#rotated-object-detection) rotated object detection
-2. Support for using mask annotation to improve [YOLOv8](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov8/README.md) object detection performance
-3. Support [MMRazor](https://github.com/open-mmlab/mmyolo/blob/dev/configs/razor/subnets/README.md) searchable NAS sub-network as the backbone of YOLO series algorithm
-4. Support calling [MMRazor](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/distillation/README.md) to distill the knowledge of RTMDet
-5. [MMYOLO](https://mmyolo.readthedocs.io/zh_CN/dev/) 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](https://github.com/open-mmlab/mmyolo/blob/dev/docs/en/model_zoo.md)
-8. Support test-time augmentation (TTA)
-9. Support RTMDet, YOLOv8 and YOLOv7 assigner visualization
+💎 **v0.6.0** was released on 15/8/2023:
+
+- 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
 
 For release history and update details, please refer to [changelog](https://mmyolo.readthedocs.io/en/latest/notes/changelog.html).
 
@@ -150,7 +146,7 @@ conda activate mmyolo
 pip install openmim
 mim install "mmengine>=0.6.0"
 mim install "mmcv>=2.0.0rc4,<2.1.0"
-mim install "mmdet>=3.0.0rc6,<3.1.0"
+mim install "mmdet>=3.0.0,<4.0.0"
 git clone https://github.com/open-mmlab/mmyolo.git
 cd mmyolo
 # Install albumentations

README_zh-CN.md

@@ -78,17 +78,13 @@
 
 ## 🥳 🚀 最新进展 [🔝](#-table-of-contents)
 
-💎 **v0.5.0** 版本已经在 2023.3.2 发布：
-
-1. 支持了 [RTMDet-R](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/README.md#rotated-object-detection) 旋转框目标检测任务和算法
-2. [YOLOv8](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov8/README.md) 支持使用 mask 标注提升目标检测模型性能
-3. 支持 [MMRazor](https://github.com/open-mmlab/mmyolo/blob/dev/configs/razor/subnets/README.md) 搜索的 NAS 子网络作为 YOLO 系列算法的 backbone
-4. 支持调用 [MMRazor](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/distillation/README.md) 对 RTMDet 进行知识蒸馏
-5. [MMYOLO](https://mmyolo.readthedocs.io/zh_CN/dev/) 文档结构优化，内容全面升级
-6. 基于 RTMDet 训练超参提升 YOLOX 精度和训练速度
-7. 支持模型参数量、FLOPs 计算和提供 T4 设备上 GPU 延时数据，并更新了 [Model Zoo](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/model_zoo.md)
-8. 支持测试时增强 TTA
-9. 支持 RTMDet、YOLOv8 和 YOLOv7 assigner 可视化
+💎 **v0.6.0** 版本已经在 2023.8.15 发布：
+
+- 支持 YOLOv5 实例分割
+- 基于 MMPose 支持 YOLOX-Pose
+- 添加 15 分钟的实例分割教程
+- YOLOv5 支持使用 mask 标注来优化边界框
+- 添加多尺度训练和测试文档
 
 我们提供了实用的**脚本命令速查表**
 
@@ -171,7 +167,7 @@ conda activate mmyolo
 pip install openmim
 mim install "mmengine>=0.6.0"
 mim install "mmcv>=2.0.0rc4,<2.1.0"
-mim install "mmdet>=3.0.0rc6,<3.1.0"
+mim install "mmdet>=3.0.0,<4.0.0"
 git clone https://github.com/open-mmlab/mmyolo.git
 cd mmyolo
 # Install albumentations

docker/Dockerfile

@@ -26,7 +26,7 @@ RUN apt-get update \
 
 # Install MMEngine , MMCV and MMDet
 RUN pip install --no-cache-dir openmim && \
-    mim install --no-cache-dir "mmengine>=0.6.0" "mmcv>=2.0.0rc4,<2.1.0" "mmdet>=3.0.0rc6,<3.1.0"
+    mim install --no-cache-dir "mmengine>=0.6.0" "mmcv>=2.0.0rc4,<2.1.0" "mmdet>=3.0.0,<4.0.0"
 
 # Install MMYOLO
 RUN git clone https://github.com/open-mmlab/mmyolo.git /mmyolo && \

docker/Dockerfile_deployment

@@ -30,7 +30,7 @@ RUN wget -q https://github.com/microsoft/onnxruntime/releases/download/v${ONNXRU
 
 # Install OPENMIM MMENGINE MMDET
 RUN pip install --no-cache-dir openmim \
-    && mim install --no-cache-dir "mmengine>=0.6.0" "mmdet>=3.0.0rc6,<3.1.0" \
+    && mim install --no-cache-dir "mmengine>=0.6.0" "mmdet>=3.0.0,<4.0.0" \
     && mim install --no-cache-dir opencv-python==4.5.5.64 opencv-python-headless==4.5.5.64
 
 RUN git clone https://github.com/open-mmlab/mmcv.git -b 2.x mmcv \

docs/en/get_started/installation.md

@@ -8,7 +8,7 @@
 pip install -U openmim
 mim install "mmengine>=0.6.0"
 mim install "mmcv>=2.0.0rc4,<2.1.0"
-mim install "mmdet>=3.0.0rc6,<3.1.0"
+mim install "mmdet>=3.0.0,<4.0.0"
 ```
 
 If you are currently in the mmyolo project directory, you can use the following simplified commands

docs/en/notes/changelog.md

@@ -1,5 +1,43 @@
 # Changelog
 
+## 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

docs/en/tutorials/custom_installation.md

@@ -75,7 +75,7 @@ thus we only need to install MMEngine, MMCV, MMDetection, and MMYOLO with the fo
 !pip3 install openmim
 !mim install "mmengine>=0.6.0"
 !mim install "mmcv>=2.0.0rc4,<2.1.0"
-!mim install "mmdet>=3.0.0rc6,<3.1.0"
+!mim install "mmdet>=3.0.0,<4.0.0"
 ```
 
 **Step 2.** Install MMYOLO from the source.

docs/zh_cn/get_started/installation.md

@@ -8,7 +8,7 @@
 pip install -U openmim
 mim install "mmengine>=0.6.0"
 mim install "mmcv>=2.0.0rc4,<2.1.0"
-mim install "mmdet>=3.0.0rc6,<3.1.0"
+mim install "mmdet>=3.0.0,<4.0.0"
 ```
 
 如果你当前已经处于 mmyolo 工程目录下，则可以采用如下简化写法

docs/zh_cn/notes/changelog.md

@@ -1,5 +1,43 @@
 # 更新日志
 
+## v0.6.0 (15/8/2023)
+
+### 亮点
+
+- 支持 YOLOv5 实例分割
+- 基于 MMPose 支持 YOLOX-Pose
+- 添加 15 分钟的实例分割教程
+- YOLOv5 支持使用 mask 标注来优化边界框
+- 添加多尺度训练和测试文档
+
+### 新特性
+
+- 添加训练和测试技巧文档 (#659)
+- 支持设置 `cache_size_limit` 参数，并支持 mmdet 3.0.0 (#707)
+- 支持 YOLOv5u 和 YOLOv6 3.0 推理 (#624, #744)
+- 支持仅模型推断 (#733)
+- 添加 YOLOv8 deepstream 配置 (#633)
+- 在 MMYOLO 应用程序中添加电离图示例 (#643)
+
+### Bug 修复
+
+- 修复 browse_dataset 以可视化测试和验证集的问题 (#641)
+- 修复安装文档错误 (#662)
+- 修复 yolox-l ckpt 链接 (#677)
+- 修正 YOLOv7 和 YOLOv8 图表中的拼写错误 (#621, #710)
+- 调整 `boxam_vis_demo.py` 中包导入的顺序 (#655)
+
+### 完善
+
+- 优化 `convert_kd_ckpt_to_student.py` 文件 (#647)
+- 添加 FAQ 和 training_testing_tricks 的英文文档 (#691, #693)
+
+### 贡献者
+
+总共 21 位开发者参与了本次版本
+
+感谢 @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)
 
 ### 亮点

docs/zh_cn/tutorials/custom_installation.md

@@ -77,7 +77,7 @@ pip install "mmcv>=2.0.0rc4" -f https://download.openmmlab.com/mmcv/dist/cu116/t
 !pip3 install openmim
 !mim install "mmengine>=0.6.0"
 !mim install "mmcv>=2.0.0rc4,<2.1.0"
-!mim install "mmdet>=3.0.0rc6,<3.1.0"
+!mim install "mmdet>=3.0.0,<4.0.0"
 ```
 
 **步骤 2.** 使用源码安装 MMYOLO：

mmyolo/__init__.py

@@ -15,7 +15,7 @@
 mmengine_version = digit_version(mmengine.__version__)
 
 mmdet_minimum_version = '3.0.0'
-mmdet_maximum_version = '3.1.0'
+mmdet_maximum_version = '4.0.0'
 mmdet_version = digit_version(mmdet.__version__)