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add changelog (#851)

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Haian Huang(深度眸) 2023-08-15 12:06:30 +08:00 committed by GitHub
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18
README.md
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@ -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:
💎 **v0.6.0** was released on 15/8/2023:
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
- 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

18
README_zh-CN.md
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@ -78,17 +78,13 @@
## 🥳 🚀 最新进展 [🔝](#-table-of-contents)
💎 **v0.5.0** 版本已经在 2023.3.2 发布：
💎 **v0.6.0** 版本已经在 2023.8.15 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
- 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

2
docker/Dockerfile
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@ -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 && \

2
docker/Dockerfile_deployment
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@ -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 \

2
docs/en/get_started/installation.md
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@ -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

38
docs/en/notes/changelog.md
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@ -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

2
docs/en/tutorials/custom_installation.md
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@ -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.

2
docs/zh_cn/get_started/installation.md
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@ -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

38
docs/zh_cn/notes/changelog.md
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@ -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

2
docs/zh_cn/tutorials/custom_installation.md
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@ -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：

2
mmyolo/__init__.py
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@ -15,7 +15,7 @@ mmengine_maximum_version = '1.0.0'
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__)