Merge pull request #331 from open-mmlab/dev

Bump version to v0.2.0
open-mmlab · Dec 1, 2022 · 27487fd · 27487fd
2 parents 178b0bf + 4e8dac5
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diff --git a/README.md b/README.md
@@ -71,19 +71,14 @@ And the figure of P6 model is in [model_design.md](docs/en/algorithm_description
 
 ## What's New
 
-💎 **v0.1.3** was released on 10/11/2022:
+💎 **v0.2.0** was released on 1/12/2022:
 
-1. Fix training failure when saving best weights based on mmengine 0.3.1
-2. Fix `add_dump_metric` error based on mmdet 3.0.0rc3
-
-💎 **v0.1.2** was released on 3/11/2022:
-
-1. Support [YOLOv5/YOLOv6/YOLOX/RTMDet deployments](https://github.com/open-mmlab/mmyolo/blob/main/configs/deploy) for ONNXRuntime and TensorRT
-2. Support [YOLOv6](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov6) s/t/n model training
-3. YOLOv5 supports [P6 model training which can input 1280-scale images](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)
-4. YOLOv5 supports [VOC dataset training](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5/voc)
-5. Support [PPYOLOE](https://github.com/open-mmlab/mmyolo/blob/main/configs/ppyoloe) and [YOLOv7](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov7) model inference and official weight conversion
-6. Add YOLOv5 replacement [backbone tutorial](https://github.com/open-mmlab/mmyolo/blob/dev/docs/en/advanced_guides/how_to.md#use-backbone-network-implemented-in-other-openmmlab-repositories) in How-to documentation
+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)
 
 For release history and update details, please refer to [changelog](https://mmyolo.readthedocs.io/en/latest/notes/changelog.html).
 
@@ -123,6 +118,7 @@ For different parts from MMDetection, we have also prepared user guides and adva
   - [Useful Tools](https://mmdetection.readthedocs.io/en/latest/user_guides/index.html#useful-tools)
     - [Visualization](docs/en/user_guides/visualization.md)
     - [Useful Tools](docs/en/user_guides/useful_tools.md)
+    - [Custom Dataset](docs/en/user_guides/custom_dataset.md)
 
 - Algorithm description
 
@@ -155,8 +151,8 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 - [x] [YOLOX](configs/yolox)
 - [x] [RTMDet](configs/rtmdet)
 - [x] [YOLOv6](configs/yolov6)
+- [x] [YOLOv7](configs/yolov7)
 - [ ] [PPYOLOE](configs/ppyoloe)(Inference only)
-- [ ] [YOLOv7](configs/yolov7)(Inference only)
 
 </details>
 

diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -71,19 +71,14 @@ P6 模型图详见 [model_design.md](docs/zh_CN/algorithm_descriptions/model_des
 
 ## 最新进展
 
-💎 **v0.1.3** 版本已经在 2022.11.10 发布：
+💎 **v0.2.0** 版本已经在 2022.12.1 发布：
 
-1. 基于 mmengine 0.3.1 修复保存最好权重时训练失败问题
-2. 基于 mmdet 3.0.0rc3 修复 `add_dump_metric` 报错 (#253)
-
-💎 **v0.1.2** 版本已经在 2022.11.3 发布：
-
-1. 支持 ONNXRuntime 和 TensorRT 的 [YOLOv5/YOLOv6/YOLOX/RTMDet 部署](https://github.com/open-mmlab/mmyolo/blob/main/configs/deploy)
-2. 支持 [YOLOv6](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov6) s/t/n 模型训练
-3. YOLOv5 支持 [P6 大分辨率 1280 尺度训练](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)
-4. YOLOv5 支持 [VOC 数据集训练](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5/voc)
-5. 支持 [PPYOLOE](https://github.com/open-mmlab/mmyolo/blob/main/configs/ppyoloe) 和 [YOLOv7](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov7) 模型推理和官方权重转化
-6. How-to 文档中新增 YOLOv5 替换 [backbone 教程](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/advanced_guides/how_to.md#%E8%B7%A8%E5%BA%93%E4%BD%BF%E7%94%A8%E4%B8%BB%E5%B9%B2%E7%BD%91%E7%BB%9C)
+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)
 
 同时我们也推出了解读视频：
 
@@ -92,7 +87,9 @@ P6 模型图详见 [model_design.md](docs/zh_CN/algorithm_descriptions/model_des
 | 🌟  |        特征图可视化        | [![Link](https://i2.hdslb.com/bfs/archive/480a0eb41fce26e0acb65f82a74501418eee1032.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV188411s7o8)  [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV188411s7o8)](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      | [![Link](http://i0.hdslb.com/bfs/archive/081f300c84d6556f40d984cfbe801fc0644ff449.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1je4y1478R/)  [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1je4y1478R)](https://www.bilibili.com/video/BV1je4y1478R/) |                                                                                                                                                                                                                               |
 | 🌟  |         配置全解读         |  [![Link](http://i1.hdslb.com/bfs/archive/e06daf640ea39b3c0700bb4dc758f1a253f33e13.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1214y157ck)  [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1214y157ck)](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 | [![Link](https://i2.hdslb.com/bfs/archive/790d2422c879ff20488910da1c4422b667ea6af7.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1N14y1V7mB)  [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1N14y1V7mB)](https://www.bilibili.com/video/BV1N14y1V7mB)  |                                                                                                                                                                                                                               |
+| 🌟  | 源码阅读和调试「必备」技巧 | [![Link](https://i2.hdslb.com/bfs/archive/790d2422c879ff20488910da1c4422b667ea6af7.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1N14y1V7mB)  [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1N14y1V7mB)](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      |   [![Link](http://i2.hdslb.com/bfs/archive/41030efb84d0cada06d5451c1e6e9bccc0cdb5a3.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1LP4y117jS)[![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1LP4y117jS)](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     |  [![Link](http://i0.hdslb.com/bfs/archive/c51f1aef7c605856777249a7b4478f44bd69f3bd.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1JG4y1d7GC)  [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1JG4y1d7GC)](https://www.bilibili.com/video/BV1JG4y1d7GC)  |     [10分钟换遍主干网络文档](https://zhuanlan.zhihu.com/p/585641598)<br>[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)     |
 
 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)
 
@@ -132,13 +129,15 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
   - [实用工具](https://mmyolo.readthedocs.io/zh_CN/latest/user_guides/index.html#实用工具)
     - [可视化教程](docs/zh_cn/user_guides/visualization.md)
     - [实用工具](docs/zh_cn/user_guides/useful_tools.md)
+    - [自定义数据集](docs/zh_cn/user_guides/custom_dataset.md)
 
 - 算法描述
 
   - [必备基础](https://mmyolo.readthedocs.io/zh_CN/latest/algorithm_descriptions/index.html#基础内容)
     - [模型设计相关说明](docs/zh_cn/algorithm_descriptions/model_design.md)
   - [算法原理和实现全解析](https://mmyolo.readthedocs.io/zh_CN/latest/algorithm_descriptions/index.html#算法原理和实现全解析)
     - [YOLOv5 原理和实现全解析](docs/zh_cn/algorithm_descriptions/yolov5_description.md)
+    - [YOLOv6 原理和实现全解析](docs/zh_cn/algorithm_descriptions/yolov6_description.md)
     - [RTMDet 原理和实现全解析](docs/zh_cn/algorithm_descriptions/rtmdet_description.md)
 
 - 算法部署
@@ -167,8 +166,8 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
 - [x] [YOLOX](configs/yolox)
 - [x] [RTMDet](configs/rtmdet)
 - [x] [YOLOv6](configs/yolov6)
+- [x] [YOLOv7](configs/yolov7)
 - [ ] [PPYOLOE](configs/ppyoloe)(仅推理)
-- [ ] [YOLOv7](configs/yolov7)(仅推理)
 
 </details>
 

diff --git a/configs/deploy/detection_rknn-fp16_static-320x320.py b/configs/deploy/detection_rknn-fp16_static-320x320.py
@@ -0,0 +1,9 @@
+_base_ = ['./base_static.py']
+onnx_config = dict(
+    input_shape=[320, 320], output_names=['feat0', 'feat1', 'feat2'])
+codebase_config = dict(model_type='rknn')
+backend_config = dict(
+    type='rknn',
+    common_config=dict(target_platform='rv1126', optimization_level=1),
+    quantization_config=dict(do_quantization=False, dataset=None),
+    input_size_list=[[3, 320, 320]])
diff --git a/configs/deploy/detection_rknn-int8_static-320x320.py b/configs/deploy/detection_rknn-int8_static-320x320.py
@@ -0,0 +1,9 @@
+_base_ = ['./base_static.py']
+onnx_config = dict(
+    input_shape=[320, 320], output_names=['feat0', 'feat1', 'feat2'])
+codebase_config = dict(model_type='rknn')
+backend_config = dict(
+    type='rknn',
+    common_config=dict(target_platform='rv1126', optimization_level=1),
+    quantization_config=dict(do_quantization=True, dataset=None),
+    input_size_list=[[3, 320, 320]])
diff --git a/configs/deploy/detection_tensorrt-int8_dynamic-192x192-960x960.py b/configs/deploy/detection_tensorrt-int8_dynamic-192x192-960x960.py
@@ -10,6 +10,6 @@
                     min_shape=[1, 3, 192, 192],
                     opt_shape=[1, 3, 640, 640],
                     max_shape=[1, 3, 960, 960])))
-    ],
-    calib_config=dict(create_calib=True, calib_file='calib_data.h5'))
+    ])
+calib_config = dict(create_calib=True, calib_file='calib_data.h5')
 use_efficientnms = False  # whether to replace TRTBatchedNMS plugin with EfficientNMS plugin # noqa E501
diff --git a/configs/deploy/detection_tensorrt-int8_static-640x640.py b/configs/deploy/detection_tensorrt-int8_static-640x640.py
@@ -11,6 +11,6 @@
                     min_shape=[1, 3, 640, 640],
                     opt_shape=[1, 3, 640, 640],
                     max_shape=[1, 3, 640, 640])))
-    ],
-    calib_config=dict(create_calib=True, calib_file='calib_data.h5'))
+    ])
+calib_config = dict(create_calib=True, calib_file='calib_data.h5')
 use_efficientnms = False  # whether to replace TRTBatchedNMS plugin with EfficientNMS plugin # noqa E501
diff --git a/configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py b/configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py
@@ -19,6 +19,9 @@
 # persistent_workers must be False if num_workers is 0.
 persistent_workers = True
 
+# Base learning rate for optim_wrapper
+base_lr = 0.01
+
 # only on Val
 batch_shapes_cfg = dict(
     type='BatchShapePolicy',
@@ -199,7 +202,7 @@
     type='OptimWrapper',
     optimizer=dict(
         type='SGD',
-        lr=0.01,
+        lr=base_lr,
         momentum=0.937,
         weight_decay=0.0005,
         nesterov=True,

diff --git a/configs/yolov6/README.md b/configs/yolov6/README.md
@@ -16,17 +16,20 @@ For years, YOLO series have been de facto industry-level standard for efficient
 
 ### COCO
 
-| Backbone | Arch | size | SyncBN | AMP | Mem (GB) | box AP |                           Config                            |                                                                                                                                                           Download                                                                                                                                                           |
-| :------: | :--: | :--: | :----: | :-: | :------: | :----: | :---------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| YOLOv6-n |  P5  | 640  |  Yes   | Yes |   6.04   |  36.2  | [config](../yolov6/yolov6_n_syncbn_fast_8xb32-400e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_n_syncbn_fast_8xb32-400e_coco/yolov6_n_syncbn_fast_8xb32-400e_coco_20221030_202726-d99b2e82.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_n_syncbn_fast_8xb32-400e_coco/yolov6_n_syncbn_fast_8xb32-400e_coco_20221030_202726.log.json) |
-| YOLOv6-t |  P5  | 640  |  Yes   | Yes |   8.13   |  41.0  | [config](../yolov6/yolov6_t_syncbn_fast_8xb32-400e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_t_syncbn_fast_8xb32-400e_coco/yolov6_t_syncbn_fast_8xb32-400e_coco_20221030_143755-cf0d278f.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_t_syncbn_fast_8xb32-400e_coco/yolov6_t_syncbn_fast_8xb32-400e_coco_20221030_143755.log.json) |
-| YOLOv6-s |  P5  | 640  |  Yes   | Yes |   8.88   |  44.0  | [config](../yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco/yolov6_s_syncbn_fast_8xb32-400e_coco_20221102_203035-932e1d91.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco/yolov6_s_syncbn_fast_8xb32-400e_coco_20221102_203035.log.json) |
+| Backbone | Arch | Size | Epoch | SyncBN | AMP | Mem (GB) | Box AP |                           Config                            |                                                                                                                                                           Download                                                                                                                                                           |
+| :------: | :--: | :--: | :---: | :----: | :-: | :------: | :----: | :---------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| YOLOv6-n |  P5  | 640  |  400  |  Yes   | Yes |   6.04   |  36.2  | [config](../yolov6/yolov6_n_syncbn_fast_8xb32-400e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_n_syncbn_fast_8xb32-400e_coco/yolov6_n_syncbn_fast_8xb32-400e_coco_20221030_202726-d99b2e82.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_n_syncbn_fast_8xb32-400e_coco/yolov6_n_syncbn_fast_8xb32-400e_coco_20221030_202726.log.json) |
+| YOLOv6-t |  P5  | 640  |  400  |  Yes   | Yes |   8.13   |  41.0  | [config](../yolov6/yolov6_t_syncbn_fast_8xb32-400e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_t_syncbn_fast_8xb32-400e_coco/yolov6_t_syncbn_fast_8xb32-400e_coco_20221030_143755-cf0d278f.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_t_syncbn_fast_8xb32-400e_coco/yolov6_t_syncbn_fast_8xb32-400e_coco_20221030_143755.log.json) |
+| YOLOv6-s |  P5  | 640  |  400  |  Yes   | Yes |   8.88   |  44.0  | [config](../yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco/yolov6_s_syncbn_fast_8xb32-400e_coco_20221102_203035-932e1d91.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco/yolov6_s_syncbn_fast_8xb32-400e_coco_20221102_203035.log.json) |
+| YOLOv6-m |  P5  | 640  |  300  |  Yes   | Yes |  16.69   |  48.4  | [config](../yolov6/yolov6_m_syncbn_fast_8xb32-400e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_m_syncbn_fast_8xb32-300e_coco/yolov6_m_syncbn_fast_8xb32-300e_coco_20221109_182658-85bda3f4.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_m_syncbn_fast_8xb32-300e_coco/yolov6_m_syncbn_fast_8xb32-300e_coco_20221109_182658.log.json) |
+| YOLOv6-l |  P5  | 640  |  300  |  Yes   | Yes |  20.86   |  51.0  | [config](../yolov6/yolov6_l_syncbn_fast_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_l_syncbn_fast_8xb32-300e_coco/yolov6_l_syncbn_fast_8xb32-300e_coco_20221109_183156-91e3c447.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_l_syncbn_fast_8xb32-300e_coco/yolov6_l_syncbn_fast_8xb32-300e_coco_20221109_183156.log.json) |
 
 **Note**:
 
-1. The performance is unstable and may fluctuate by about 0.3 mAP.
-2. YOLOv6-m,l,x will be supported in later version.
-3. If users need the weight of 300 epoch, they can train according to the configs of 300 epoch provided by us, or convert the official weight according to the [converter script](../../tools/model_converters/).
+1. The official m and l models use knowledge distillation, but our version does not support it, which will be implemented in [MMRazor](https://github.com/open-mmlab/mmrazor) in the future.
+2. The performance is unstable and may fluctuate by about 0.3 mAP.
+3. If users need the weight of 300 epoch for nano, tiny and small model, they can train according to the configs of 300 epoch provided by us, or convert the official weight according to the [converter script](../../tools/model_converters/).
+4. We have observed that the [base model](https://github.com/meituan/YOLOv6/tree/main/configs/base) has been officially released in v6 recently. Although the accuracy has decreased, it is more efficient. We will also provide the base model configuration in the future.
 
 ## Citation