diff --git a/README.md b/README.md
index 163985b5..e5e8af2c 100644
--- a/README.md
+++ b/README.md
@@ -71,14 +71,32 @@ And the figure of P6 model is in [model_design.md](docs/en/algorithm_description
## What's New
-💎 **v0.2.0** was released on 1/12/2022:
+### Highlight
-1. Support [YOLOv7](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolov7) P5 and P6 model
-2. Support [YOLOv6](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov6/README.md) ML model
-3. Support [Grad-Based CAM and Grad-Free CAM](https://github.com/open-mmlab/mmyolo/blob/dev/demo/boxam_vis_demo.py)
-4. Support [large image inference](https://github.com/open-mmlab/mmyolo/blob/dev/demo/large_image_demo.py) based on sahi
-5. Add [easydeploy](https://github.com/open-mmlab/mmyolo/blob/dev/projects/easydeploy/README.md) project under the projects folder
-6. Add [custom dataset guide](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/user_guides/custom_dataset.md)
+We are excited to announce our latest work on real-time object recognition tasks, **RTMDet**, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the [technical report](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](configs/rtmdet).
+
+[](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
+[](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
+[](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)
+
+| Task | Dataset | AP | FPS(TRT FP16 BS1 3090) |
+| ------------------------ | ------- | ------------------------------------ | ---------------------- |
+| Object Detection | COCO | 52.8 | 322 |
+| Instance Segmentation | COCO | 44.6 | 188 |
+| Rotated Object Detection | DOTA | 78.9(single-scale)/81.3(multi-scale) | 121 |
+
+
+
+
@@ -191,6 +211,8 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
YOLOv6RepPAFPN
YOLOXPAFPN
CSPNeXtPAFPN
+ YOLOv7PAFPN
+ PPYOLOECSPPAFPN
|
diff --git a/README_zh-CN.md b/README_zh-CN.md
index 43040071..3c0fa5ad 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -71,25 +71,46 @@ P6 模型图详见 [model_design.md](docs/zh_CN/algorithm_descriptions/model_des
## 最新进展
-💎 **v0.2.0** 版本已经在 2022.12.1 发布：
+### 亮点
-1. 支持 [YOLOv7](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolov7) P5 和 P6 模型
-2. 支持 [YOLOv6](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov6/README.md) 中的 ML 大模型
-3. 支持 [Grad-Based CAM 和 Grad-Free CAM](https://github.com/open-mmlab/mmyolo/blob/dev/demo/boxam_vis_demo.py)
-4. 基于 sahi 支持 [大图推理](https://github.com/open-mmlab/mmyolo/blob/dev/demo/large_image_demo.py)
-5. projects 文件夹下新增 [easydeploy](https://github.com/open-mmlab/mmyolo/blob/dev/projects/easydeploy/README.md) 项目
-6. 新增 [自定义数据集教程](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/user_guides/custom_dataset.md)
+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。
+
+[](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
+[](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
+[](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)
+
+| Task | Dataset | AP | FPS(TRT FP16 BS1 3090) |
+| ------------------------ | ------- | ------------------------------------ | ---------------------- |
+| Object Detection | COCO | 52.8 | 322 |
+| Instance Segmentation | COCO | 44.6 | 188 |
+| Rotated Object Detection | DOTA | 78.9(single-scale)/81.3(multi-scale) | 121 |
+
+
+
+
[10分钟换遍主干网络.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/[实用类第二期]10分钟换遍主干网络.ipynb) |
+| | 内容 | 视频 | 课程中的代码 |
+| :-: | :--------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| 🌟 | 特征图可视化 | [](https://www.bilibili.com/video/BV188411s7o8) [](https://www.bilibili.com/video/BV188411s7o8) | [特征图可视化.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/%5B%E5%B7%A5%E5%85%B7%E7%B1%BB%E7%AC%AC%E4%B8%80%E6%9C%9F%5D%E7%89%B9%E5%BE%81%E5%9B%BE%E5%8F%AF%E8%A7%86%E5%8C%96.ipynb) |
+| 🌟 | 特征图可视化 Demo | [](https://www.bilibili.com/video/BV1je4y1478R/) [](https://www.bilibili.com/video/BV1je4y1478R/) | |
+| 🌟 | 配置全解读 | [](https://www.bilibili.com/video/BV1214y157ck) [](https://www.bilibili.com/video/BV1214y157ck) | [配置全解读文档](https://zhuanlan.zhihu.com/p/577715188) |
+| 🌟 | 源码阅读和调试「必备」技巧 | [](https://www.bilibili.com/video/BV1N14y1V7mB) [](https://www.bilibili.com/video/BV1N14y1V7mB) | [源码阅读和调试「必备」技巧文档](https://zhuanlan.zhihu.com/p/580885852) |
+| 🌟 | 工程文件结构简析 | [](https://www.bilibili.com/video/BV1LP4y117jS)[](https://www.bilibili.com/video/BV1LP4y117jS) | [工程文件结构简析文档](https://zhuanlan.zhihu.com/p/584807195) |
+| 🌟 | 10分钟换遍主干网络 | [](https://www.bilibili.com/video/BV1JG4y1d7GC) [](https://www.bilibili.com/video/BV1JG4y1d7GC) | [10分钟换遍主干网络文档](https://zhuanlan.zhihu.com/p/585641598)
[10分钟换遍主干网络.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/[实用类第二期]10分钟换遍主干网络.ipynb) |
+| 🌟 | 基于 sahi 的大图推理 | [](https://www.bilibili.com/video/BV1EK411R7Ws/) [](https://www.bilibili.com/video/BV1EK411R7Ws/) | [10分钟轻松掌握大图推理.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/[工具类第二期]10分钟轻松掌握大图推理.ipynb) |
+| 🌟 | 自定义数据集从标注到部署保姆级教程 | [](https://www.bilibili.com/video/BV1RG4y137i5) [](https://www.bilibili.com/video/BV1JG4y1d7GC) | [自定义数据集从标注到部署保姆级教程](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/user_guides/custom_dataset.md) |
发布历史和更新细节请参考 [更新日志](https://mmyolo.readthedocs.io/zh_CN/latest/notes/changelog.html)
@@ -103,7 +124,7 @@ conda activate open-mmlab
pip install openmim
mim install "mmengine>=0.3.1"
mim install "mmcv>=2.0.0rc1,<2.1.0"
-mim install "mmdet>=3.0.0rc3,<3.1.0"
+mim install "mmdet>=3.0.0rc5,<3.1.0"
git clone https://github.com/open-mmlab/mmyolo.git
cd mmyolo
# Install albumentations
@@ -149,6 +170,7 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
- 进阶指南
+ - [模块组合](docs/zh_cn/advanced_guides/module_combination.md)
- [数据流](docs/zh_cn/advanced_guides/data_flow.md)
- [How to](docs/zh_cn/advanced_guides/how_to.md)
- [插件](docs/zh_cn/advanced_guides/plugins.md)
@@ -167,7 +189,7 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
- [x] [RTMDet](configs/rtmdet)
- [x] [YOLOv6](configs/yolov6)
- [x] [YOLOv7](configs/yolov7)
-- [ ] [PPYOLOE](configs/ppyoloe)(仅推理)
+- [x] [PPYOLOE](configs/ppyoloe)
@@ -198,6 +220,8 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
YOLOXCSPDarknet
EfficientRep
CSPNeXt
+ YOLOv7Backbone
+ PPYOLOECSPResNet
|
@@ -206,6 +230,8 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
YOLOv6RepPAFPN
YOLOXPAFPN
CSPNeXtPAFPN
+ YOLOv7PAFPN
+ PPYOLOECSPPAFPN
|
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 191273c1..2bd00697 100644
--- a/docker/Dockerfile
+++ b/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>=0.3.1" "mmcv>=2.0.0rc1,<2.1.0" "mmdet>=3.0.0rc3,<3.1.0"
+ mim install --no-cache-dir "mmengine>=0.3.1" "mmcv>=2.0.0rc1,<2.1.0" "mmdet>=3.0.0rc5,<3.1.0"
# Install MMYOLO
RUN git clone https://github.com/open-mmlab/mmyolo.git /mmyolo && \
diff --git a/docker/Dockerfile_deployment b/docker/Dockerfile_deployment
index 7326cafa..7f63c1cc 100644
--- a/docker/Dockerfile_deployment
+++ b/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>=0.3.1" "mmdet>=3.0.0rc3,<3.1.0" \
+ && mim install --no-cache-dir "mmengine>=0.3.1" "mmdet>=3.0.0rc5,<3.1.0" \
&& 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 \
diff --git a/docs/en/get_started.md b/docs/en/get_started.md
index ab04f5ba..2848c7bb 100644
--- a/docs/en/get_started.md
+++ b/docs/en/get_started.md
@@ -6,7 +6,8 @@ Compatible MMEngine, MMCV and MMDetection versions are shown as below. Please in
| MMYOLO version | MMDetection version | MMEngine version | MMCV version |
| :------------: | :----------------------: | :----------------------: | :---------------------: |
-| main | mmdet>=3.0.0rc3, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
+| main | mmdet>=3.0.0rc5, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
+| 0.3.0 | mmdet>=3.0.0rc5, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
| 0.2.0 | mmdet>=3.0.0rc3, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
| 0.1.3 | mmdet>=3.0.0rc3, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
| 0.1.2 | mmdet>=3.0.0rc2, \<3.1.0 | mmengine>=0.3.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
@@ -54,7 +55,7 @@ conda install pytorch torchvision cpuonly -c pytorch
pip install -U openmim
mim install "mmengine>=0.3.1"
mim install "mmcv>=2.0.0rc1,<2.1.0"
-mim install "mmdet>=3.0.0rc3,<3.1.0"
+mim install "mmdet>=3.0.0rc5,<3.1.0"
```
**Note:**
@@ -213,7 +214,7 @@ thus we only need to install MMEngine, MMCV, MMDetection, and MMYOLO with the fo
!pip3 install openmim
!mim install "mmengine==0.1.0"
!mim install "mmcv>=2.0.0rc1,<2.1.0"
-!mim install "mmdet>=3.0.0.rc1"
+!mim install "mmdet>=3.0.0rc5,<3.1.0"
```
**Step 2.** Install MMYOLO from the source.
diff --git a/docs/en/notes/changelog.md b/docs/en/notes/changelog.md
index fc09f75d..f05b39b1 100644
--- a/docs/en/notes/changelog.md
+++ b/docs/en/notes/changelog.md
@@ -1,5 +1,67 @@
# Changelog
+## v0.3.0 (8/1/2023)
+
+### Highlights
+
+1. Implement fast version of [RTMDet](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/README.md). RTMDet-s 8xA100 training takes only 14 hours. The training speed is 2.6 times faster than the previous version.
+2. Support [PPYOLOE](https://github.com/open-mmlab/mmyolo/blob/dev/configs/ppyoloe/README.md) training
+3. Support `iscrowd` attribute training in [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov5/crowdhuman/yolov5_s-v61_8xb16-300e_ignore_crowdhuman.py)
+4. Support [YOLOv5 assigner result visualization](https://github.com/open-mmlab/mmyolo/blob/dev/projects/assigner_visualization/README.md)
+
+### New Features
+
+01. Add `crowdhuman` dataset (#368)
+02. Easydeploy support TensorRT inference (#377)
+03. Add `YOLOX` structure description (#402)
+04. Add a feature for the video demo (#392)
+05. Support `YOLOv7` easy deploy (#427)
+06. Add resume from specific checkpoint in CLI (#393)
+07. Set `metainfo` fields to lower case (#362, #412)
+08. Add module combination doc (#349, #352, #345)
+09. Add docs about how to freeze the weight of backbone or neck (#418)
+10. Add don't used pre-training weights doc in `how_to.md` (#404)
+11. Add docs about how to set the random seed (#386)
+12. Translate `rtmdet_description.md` document to English (#353)
+13. Add doc of `yolov6_description.md` (#382, #372)
+
+### Bug Fixes
+
+01. Fix bugs in the output annotation file when `--class-id-txt` is set (#430)
+02. Fix batch inference bug in `YOLOv5` head (#413)
+03. Fix typehint in some heads (#415, #416, #443)
+04. Fix RuntimeError of `torch.cat()` expected a non-empty list of Tensors (#376)
+05. Fix the device inconsistency error in `YOLOv7` training (#397)
+06. Fix the `scale_factor` and `pad_param` value in `LetterResize` (#387)
+07. Fix docstring graph rendering error of readthedocs (#400)
+08. Fix AssertionError when `YOLOv6` from training to val (#378)
+09. Fix CI error due to `np.int` and legacy builder.py (#389)
+10. Fix MMDeploy rewriter (#366)
+11. Fix MMYOLO unittest scope bug (#351)
+12. Fix `pad_param` error (#354)
+13. Fix twice head inference bug (#342)
+14. Fix customize dataset training (#428)
+
+### Improvements
+
+01. Update `useful_tools.md` (#384)
+02. update the English version of `custom_dataset.md` (#381)
+03. Remove context argument from the rewriter function (#395)
+04. deprecating `np.bool` type alias (#396)
+05. Add new video link for custom dataset (#365)
+06. Export onnx for model only (#361)
+07. Add MMYOLO regression test yml (#359)
+08. Update video tutorials in `article.md` (#350)
+09. Add deploy demo (#343)
+10. Optimize the vis results of large images in debug mode (#346)
+11. Improve args for `browse_dataset` and support `RepeatDataset` (#340, #338)
+
+### Contributors
+
+A total of 28 developers contributed to this release.
+
+Thank @RangeKing, @PeterH0323, @Nioolek, @triple-Mu, @matrixgame2018, @xin-li-67, @tang576225574, @kitecats, @Seperendity, @diplomatist, @vaew, @wzr-skn, @VoyagerXvoyagerx, @MambaWong, @tianleiSHI, @caj-github, @zhubochao, @lvhan028, @dsghaonan, @lyviva, @yuewangg, @wang-tf, @satuoqaq, @grimoire, @RunningLeon, @hanrui1sensetime, @RangiLyu, @hhaAndroid
+
## v0.2.0（1/12/2022)
### Highlights
diff --git a/docs/en/user_guides/yolov5_tutorial.md b/docs/en/user_guides/yolov5_tutorial.md
index c225757f..ff9d703a 100644
--- a/docs/en/user_guides/yolov5_tutorial.md
+++ b/docs/en/user_guides/yolov5_tutorial.md
@@ -12,7 +12,7 @@ conda install pytorch torchvision -c pytorch
pip install -U openmim
mim install "mmengine>=0.3.1"
mim install "mmcv>=2.0.0rc1,<2.1.0"
-mim install "mmdet>=3.0.0rc3,<3.1.0"
+mim install "mmdet>=3.0.0rc5,<3.1.0"
git clone https://github.com/open-mmlab/mmyolo.git
cd mmyolo
# Install albumentations
diff --git a/docs/zh_cn/advanced_guides/index.rst b/docs/zh_cn/advanced_guides/index.rst
index 81810a88..02b06e61 100644
--- a/docs/zh_cn/advanced_guides/index.rst
+++ b/docs/zh_cn/advanced_guides/index.rst
@@ -1,3 +1,11 @@
+模块组合
+************************
+
+.. toctree::
+ :maxdepth: 1
+
+ module_combination.md
+
数据流
************************
diff --git a/docs/zh_cn/article.md b/docs/zh_cn/article.md
index 6c999e5c..706f11d0 100644
--- a/docs/zh_cn/article.md
+++ b/docs/zh_cn/article.md
@@ -7,18 +7,14 @@
### 文章
- [社区协作，简洁易用，快来开箱新一代 YOLO 系列开源库](https://zhuanlan.zhihu.com/p/575615805)
-
- [MMYOLO 社区倾情贡献，RTMDet 原理社区开发者解读来啦！](https://zhuanlan.zhihu.com/p/569777684)
-
- [玩转 MMYOLO 基础类第一期： 配置文件太复杂？继承用法看不懂？配置全解读来了](https://zhuanlan.zhihu.com/p/577715188)
-
- [玩转 MMYOLO 工具类第一期： 特征图可视化](https://zhuanlan.zhihu.com/p/578141381?)
-
- [玩转 MMYOLO 实用类第二期：源码阅读和调试「必备」技巧文档](https://zhuanlan.zhihu.com/p/580885852)
-
- [玩转 MMYOLO 基础类第二期：工程文件结构简析](https://zhuanlan.zhihu.com/p/584807195)
-
- [玩转 MMYOLO 实用类第二期：10分钟换遍主干网络文档](https://zhuanlan.zhihu.com/p/585641598)
+- [MMYOLO 自定义数据集从标注到部署保姆级教程](https://zhuanlan.zhihu.com/p/595497726)
+- [满足一切需求的 MMYOLO 可视化：测试过程可视化](https://zhuanlan.zhihu.com/p/593179372)
### 视频
diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md
index c1371f37..32b158bd 100644
--- a/docs/zh_cn/get_started.md
+++ b/docs/zh_cn/get_started.md
@@ -6,7 +6,8 @@
| MMYOLO version | MMDetection version | MMEngine version | MMCV version |
| :------------: | :----------------------: | :----------------------: | :---------------------: |
-| main | mmdet>=3.0.0rc3, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
+| main | mmdet>=3.0.0rc5, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
+| 0.3.0 | mmdet>=3.0.0rc5, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
| 0.2.0 | mmdet>=3.0.0rc3, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
| 0.1.3 | mmdet>=3.0.0rc3, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
| 0.1.2 | mmdet>=3.0.0rc2, \<3.1.0 | mmengine>=0.3.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
@@ -54,7 +55,7 @@ conda install pytorch torchvision cpuonly -c pytorch
pip install -U openmim
mim install "mmengine>=0.3.1"
mim install "mmcv>=2.0.0rc1,<2.1.0"
-mim install "mmdet>=3.0.0rc3,<3.1.0"
+mim install "mmdet>=3.0.0rc5,<3.1.0"
```
**注意：**
@@ -214,7 +215,7 @@ pip install "mmcv>=2.0.0rc1" -f https://download.openmmlab.com/mmcv/dist/cu116/t
!pip3 install openmim
!mim install "mmengine==0.1.0"
!mim install "mmcv>=2.0.0rc1,<2.1.0"
-!mim install "mmdet>=3.0.0.rc1"
+!mim install "mmdet>=3.0.0rc5,<3.1.0"
```
**步骤 2.** 使用源码安装 MMYOLO：
diff --git a/docs/zh_cn/notes/changelog.md b/docs/zh_cn/notes/changelog.md
index ac5df1dc..7f5a9b3d 100644
--- a/docs/zh_cn/notes/changelog.md
+++ b/docs/zh_cn/notes/changelog.md
@@ -1,5 +1,72 @@
# 更新日志
+## v0.3.0 (8/1/2023)
+
+### 亮点
+
+1. 实现了 [RTMDet](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/README.md) 的快速版本。RTMDet-s 8xA100 训练只需要 14 个小时，训练速度相比原先版本提升 2.6 倍。
+2. 支持 [PPYOLOE](https://github.com/open-mmlab/mmyolo/blob/dev/configs/ppyoloe/README.md) 训练。
+3. 支持 [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov5/crowdhuman/yolov5_s-v61_8xb16-300e_ignore_crowdhuman.py) 的 `iscrowd` 属性训练。
+4. 支持 [YOLOv5 正样本分配结果可视化](https://github.com/open-mmlab/mmyolo/blob/dev/projects/assigner_visualization/README.md)
+5. 新增 [YOLOv6 原理和实现全解析文档](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/algorithm_descriptions/yolov6_description.md)
+
+### 新特性
+
+01. 新增 `crowdhuman` 数据集 (#368)
+02. EasyDeploy 中支持 TensorRT 推理 (#377)
+03. 新增 `YOLOX` 结构图描述 (#402)
+04. 新增视频推理脚本 (#392)
+05. EasyDeploy 中支持 `YOLOv7` 部署 (#427)
+06. 支持从 CLI 中的特定检查点恢复训练 (#393)
+07. 将元信息字段设置为小写（#362、#412）
+08. 新增模块组合文档 (#349, #352, #345)
+09. 新增关于如何冻结 backbone 或 neck 权重的文档 (#418)
+10. 在 `how_to.md` 中添加不使用预训练权重的文档 (#404)
+11. 新增关于如何设置随机种子的文档 (#386)
+12. 将 `rtmdet_description.md` 文档翻译成英文 (#353)
+
+### Bug 修复
+
+01. 修复设置 `--class-id-txt` 时输出注释文件中的错误 (#430)
+02. 修复 `YOLOv5` head 中的批量推理错误 (#413)
+03. 修复某些 head 的类型提示（#415、#416、#443）
+04. 修复 expected a non-empty list of Tensors 错误 (#376)
+05. 修复 `YOLOv7` 训练中的设备不一致错误（#397）
+06. 修复 `LetterResize` 中的 `scale_factor` 和 `pad_param` 值 (#387)
+07. 修复 readthedocs 的 docstring 图形渲染错误 (#400)
+08. 修复 `YOLOv6` 从训练到验证时的断言错误 (#378)
+09. 修复 `np.int` 和旧版 builder.py 导致的 CI 错误 (#389)
+10. 修复 MMDeploy 重写器 (#366)
+11. 修复 MMYOLO 单元测试错误 (#351)
+12. 修复 `pad_param` 错误 (#354)
+13. 修复 head 推理两次的错误（#342）
+14. 修复自定义数据集训练 (#428)
+
+### 完善
+
+01. 更新 `useful_tools.md` (#384)
+02. 更新英文版 `custom_dataset.md` (#381)
+03. 重写函数删除上下文参数 (#395)
+04. 弃用 `np.bool` 类型别名 (#396)
+05. 为自定义数据集添加新的视频链接 (#365)
+06. 仅为模型导出 onnx (#361)
+07. 添加 MMYOLO 回归测试 yml (#359)
+08. 更新 `article.md` 中的视频教程 (#350)
+09. 添加部署 demo (#343)
+10. 优化 debug 模式下大图的可视化效果(#346)
+11. 改进 `browse_dataset` 的参数并支持 `RepeatDataset` (#340, #338)
+
+### 视频
+
+1. 发布了 [基于 sahi 的大图推理](https://www.bilibili.com/video/BV1EK411R7Ws/)
+2. 发布了 [自定义数据集从标注到部署保姆级教程](https://www.bilibili.com/video/BV1RG4y137i5)
+
+### 贡献者
+
+总共 28 位开发者参与了本次版本
+
+谢谢 @RangeKing, @PeterH0323, @Nioolek, @triple-Mu, @matrixgame2018, @xin-li-67, @tang576225574, @kitecats, @Seperendity, @diplomatist, @vaew, @wzr-skn, @VoyagerXvoyagerx, @MambaWong, @tianleiSHI, @caj-github, @zhubochao, @lvhan028, @dsghaonan, @lyviva, @yuewangg, @wang-tf, @satuoqaq, @grimoire, @RunningLeon, @hanrui1sensetime, @RangiLyu, @hhaAndroid
+
## v0.2.0（1/12/2022)
### 亮点
diff --git a/docs/zh_cn/overview.md b/docs/zh_cn/overview.md
index 1515b038..6856b132 100644
--- a/docs/zh_cn/overview.md
+++ b/docs/zh_cn/overview.md
@@ -51,8 +51,9 @@ MMYOLO 文件结构和 MMDetection 完全一致。为了能够充分复用 MMDet
5. 参考以下教程深入了解：
- - [数据流](https://mmyolo.readthedocs.io/zh_CN/latest/advanced_guides/index.html#id1)
+ - [模块组合](https://mmyolo.readthedocs.io/zh_CN/latest/advanced_guides/index.html#id1)
+ - [数据流](https://mmyolo.readthedocs.io/zh_CN/latest/advanced_guides/index.html#id2)
- [How to](https://mmyolo.readthedocs.io/zh_CN/latest/advanced_guides/index.html#how-to)
- - [插件](https://mmyolo.readthedocs.io/zh_CN/latest/advanced_guides/index.html#id3)
+ - [插件](https://mmyolo.readthedocs.io/zh_CN/latest/advanced_guides/index.html#id4)
6. [解读文章和资源汇总](article.md)
diff --git a/docs/zh_cn/user_guides/yolov5_tutorial.md b/docs/zh_cn/user_guides/yolov5_tutorial.md
index 20049899..411c4cb4 100644
--- a/docs/zh_cn/user_guides/yolov5_tutorial.md
+++ b/docs/zh_cn/user_guides/yolov5_tutorial.md
@@ -12,7 +12,7 @@ conda install pytorch torchvision -c pytorch
pip install -U openmim
mim install "mmengine>=0.3.1"
mim install "mmcv>=2.0.0rc1,<2.1.0"
-mim install "mmdet>=3.0.0rc3,<3.1.0"
+mim install "mmdet>=3.0.0rc5,<3.1.0"
git clone https://github.com/open-mmlab/mmyolo.git
cd mmyolo
# Install albumentations
diff --git a/mmyolo/__init__.py b/mmyolo/__init__.py
index 67367292..757c4084 100644
--- a/mmyolo/__init__.py
+++ b/mmyolo/__init__.py
@@ -14,7 +14,7 @@ mmengine_minimum_version = '0.3.1'
mmengine_maximum_version = '1.0.0'
mmengine_version = digit_version(mmengine.__version__)
-mmdet_minimum_version = '3.0.0rc3'
+mmdet_minimum_version = '3.0.0rc5'
mmdet_maximum_version = '3.1.0'
mmdet_version = digit_version(mmdet.__version__)
diff --git a/mmyolo/models/task_modules/assigners/batch_yolov7_assigner.py b/mmyolo/models/task_modules/assigners/batch_yolov7_assigner.py
index 2bfb0562..6709968e 100644
--- a/mmyolo/models/task_modules/assigners/batch_yolov7_assigner.py
+++ b/mmyolo/models/task_modules/assigners/batch_yolov7_assigner.py
@@ -254,6 +254,9 @@ class BatchYOLOv7Assigner(nn.Module):
_mlvl_decoderd_bboxes = torch.cat(_mlvl_decoderd_bboxes, dim=0)
num_pred_positive = _mlvl_decoderd_bboxes.shape[0]
+ if num_pred_positive == 0:
+ continue
+
# scaled xywh
batch_input_shape_wh = pred_results[0].new_tensor(
batch_input_shape[::-1]).repeat((1, 2))
diff --git a/mmyolo/version.py b/mmyolo/version.py
index f823adab..f3c663b4 100644
--- a/mmyolo/version.py
+++ b/mmyolo/version.py
@@ -1,6 +1,6 @@
# Copyright (c) OpenMMLab. All rights reserved.
-__version__ = '0.2.0'
+__version__ = '0.3.0'
from typing import Tuple
diff --git a/projects/example_project/README.md b/projects/example_project/README.md
index 52ca748f..24c84d98 100644
--- a/projects/example_project/README.md
+++ b/projects/example_project/README.md
@@ -37,20 +37,51 @@ You should claim whether this is based on the pre-trained weights, which are con
| Method | Backbone | Pretrained Model | Training set | Test set | #epoch | box AP | Download |
| :---------------------------------------------------------------------------: | :-------------------: | :--------------: | :------------: | :----------: | :----: | :----: | :----------------------: |
-| [YOLOv5 dummy](configs/yolov5_s_dummy-backbone_v61_syncbn_8xb16-300e_coco.py) | DummyYOLOv5CSPDarknet | - | COCO2017 Train | COCO2017 Val | 12 | 0.8853 | [model](<>) \| [log](<>) |
+| [YOLOv5 dummy](configs/yolov5_s_dummy-backbone_v61_syncbn_8xb16-300e_coco.py) | DummyYOLOv5CSPDarknet | - | COCO2017 Train | COCO2017 Val | 300 | 37.7 | [model](<>) \| [log](<>) |
## Citation
```latex
-@article{Ren_2017,
- title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},
- journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
- publisher={Institute of Electrical and Electronics Engineers (IEEE)},
- author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
- year={2017},
- month={Jun},
+@software{glenn_jocher_2022_7002879,
+ author = {Glenn Jocher and
+ Ayush Chaurasia and
+ Alex Stoken and
+ Jirka Borovec and
+ NanoCode012 and
+ Yonghye Kwon and
+ TaoXie and
+ Kalen Michael and
+ Jiacong Fang and
+ imyhxy and
+ Lorna and
+ Colin Wong and
+ 曾逸夫(Zeng Yifu) and
+ Abhiram V and
+ Diego Montes and
+ Zhiqiang Wang and
+ Cristi Fati and
+ Jebastin Nadar and
+ Laughing and
+ UnglvKitDe and
+ tkianai and
+ yxNONG and
+ Piotr Skalski and
+ Adam Hogan and
+ Max Strobel and
+ Mrinal Jain and
+ Lorenzo Mammana and
+ xylieong},
+ title = {{ultralytics/yolov5: v6.2 - YOLOv5 Classification
+ Models, Apple M1, Reproducibility, ClearML and
+ Deci.ai integrations}},
+ month = aug,
+ year = 2022,
+ publisher = {Zenodo},
+ version = {v6.2},
+ doi = {10.5281/zenodo.7002879},
+ url = {https://doi.org/10.5281/zenodo.7002879}
}
```
diff --git a/projects/example_project/configs/yolov5_s_dummy-backbone_v61_syncbn_8xb16-300e_coco.py b/projects/example_project/configs/yolov5_s_dummy-backbone_v61_syncbn_8xb16-300e_coco.py
index bdbdcffd..55b43bb3 100644
--- a/projects/example_project/configs/yolov5_s_dummy-backbone_v61_syncbn_8xb16-300e_coco.py
+++ b/projects/example_project/configs/yolov5_s_dummy-backbone_v61_syncbn_8xb16-300e_coco.py
@@ -1,4 +1,4 @@
-_base_ = ['../../../configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py']
+_base_ = '../../../configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py'
custom_imports = dict(imports=['projects.example_project.dummy'])
diff --git a/projects/misc/custom_dataset/yolov7_tiny_syncbn_fast_1xb32-100e_cat.py b/projects/misc/custom_dataset/yolov7_tiny_syncbn_fast_1xb32-100e_cat.py
new file mode 100644
index 00000000..fff59cb3
--- /dev/null
+++ b/projects/misc/custom_dataset/yolov7_tiny_syncbn_fast_1xb32-100e_cat.py
@@ -0,0 +1,78 @@
+_base_ = '../yolov7/yolov7_tiny_syncbn_fast_8x16b-300e_coco.py'
+
+max_epochs = 100
+data_root = './data/cat/'
+
+work_dir = './work_dirs/yolov7_tiny_syncbn_fast_1xb32-100e_cat'
+
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_tiny_syncbn_fast_8x16b-300e_coco/yolov7_tiny_syncbn_fast_8x16b-300e_coco_20221126_102719-0ee5bbdf.pth' # noqa
+
+train_batch_size_per_gpu = 32
+train_num_workers = 4 # train_num_workers = nGPU x 4
+
+save_epoch_intervals = 2
+
+# base_lr_default * (your_bs / default_bs)
+base_lr = 0.01 / 4
+
+anchors = [
+ [(68, 69), (154, 91), (143, 162)], # P3/8
+ [(242, 160), (189, 287), (391, 207)], # P4/16
+ [(353, 337), (539, 341), (443, 432)] # P5/32
+]
+
+class_name = ('cat', )
+num_classes = len(class_name)
+metainfo = dict(classes=class_name, palette=[(220, 20, 60)])
+
+train_cfg = dict(
+ max_epochs=max_epochs,
+ val_begin=20,
+ val_interval=save_epoch_intervals,
+ dynamic_intervals=[(max_epochs - 10, 1)])
+
+model = dict(
+ bbox_head=dict(
+ head_module=dict(num_classes=num_classes),
+ prior_generator=dict(base_sizes=anchors),
+ loss_cls=dict(loss_weight=0.5 *
+ (num_classes / 80 * 3 / _base_.num_det_layers))))
+
+train_dataloader = dict(
+ batch_size=train_batch_size_per_gpu,
+ num_workers=train_num_workers,
+ dataset=dict(
+ _delete_=True,
+ type='RepeatDataset',
+ times=5,
+ dataset=dict(
+ type=_base_.dataset_type,
+ data_root=data_root,
+ metainfo=metainfo,
+ ann_file='annotations/trainval.json',
+ data_prefix=dict(img='images/'),
+ filter_cfg=dict(filter_empty_gt=False, min_size=32),
+ pipeline=_base_.train_pipeline)))
+
+val_dataloader = dict(
+ dataset=dict(
+ metainfo=metainfo,
+ data_root=data_root,
+ ann_file='annotations/trainval.json',
+ data_prefix=dict(img='images/')))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(ann_file=data_root + 'annotations/trainval.json')
+test_evaluator = val_evaluator
+
+optim_wrapper = dict(optimizer=dict(lr=base_lr))
+
+default_hooks = dict(
+ checkpoint=dict(
+ type='CheckpointHook',
+ interval=save_epoch_intervals,
+ max_keep_ckpts=2,
+ save_best='auto'),
+ param_scheduler=dict(max_epochs=max_epochs),
+ logger=dict(type='LoggerHook', interval=10))
diff --git a/setup.cfg b/setup.cfg
index c62d88cc..d30673d0 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -18,4 +18,4 @@ SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
[codespell]
skip = *.ipynb
quiet-level = 3
-ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids,TOOD,tood,ba,warmup,elease
+ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids,tood,ba,warmup,elease,dota
|