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[Feature] Add model zoo (#607) 

* Update model_zoo.md

* fix

* update

* update

* update
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Haian Huang(深度眸) 2023-03-01 16:34:17 +08:00 committed by GitHub
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18
README.md
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@ -259,6 +259,14 @@ For different parts from MMDetection, we have also prepared user guides and adva
Results and models are available in the [model zoo](docs/en/model_zoo.md). Results and models are available in the [model zoo](docs/en/model_zoo.md).
<details open>
<summary><b>Supported Tasks</b></summary>
- [x] Object detection
- [x] Rotated object detection
</details>
<details open> <details open>
<summary><b>Supported Algorithms</b></summary> <summary><b>Supported Algorithms</b></summary>
@ -272,6 +280,16 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
</details> </details>
<details open>
<summary><b>Supported Datasets</b></summary>
- [x] COCO Dataset
- [x] VOC Dataset
- [x] CrowdHuman Dataset
- [x] DOTA 1.0 Dataset
</details>
<details open> <details open>
<div align="center"> <div align="center">
<b>Module Components</b> <b>Module Components</b>

18
README_zh-CN.md
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@ -281,6 +281,14 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
测试结果和模型可以在 [模型库](docs/zh_cn/model_zoo.md) 中找到。 测试结果和模型可以在 [模型库](docs/zh_cn/model_zoo.md) 中找到。
<details open>
<summary><b>支持的任务</b></summary>
- [x] 目标检测
- [x] 旋转框目标检测
</details>
<details open> <details open>
<summary><b>支持的算法</b></summary> <summary><b>支持的算法</b></summary>
@ -294,6 +302,16 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
</details> </details>
<details open>
<summary><b>支持的数据集</b></summary>
- [x] COCO Dataset
- [x] VOC Dataset
- [x] CrowdHuman Dataset
- [x] DOTA 1.0 Dataset
</details>
<details open> <details open>
<div align="center"> <div align="center">
<b>模块组件</b> <b>模块组件</b>

2
configs/razor/subnets/README.md
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@ -62,7 +62,7 @@ CUDA_VISIBLE_DEVICES=0 PORT=29500 ./tools/dist_test.sh configs/razor/subnets/yol
Here we provide the baseline version of YOLO Series with NAS backbone. Here we provide the baseline version of YOLO Series with NAS backbone.
| Model | size | box AP | Params(M) | FLOPS(G) | Config | Download | | Model | size | box AP | Params(M) | FLOPs(G) | Config | Download |
| :------------------------: | :--: | :----: | :----------: | :------: | :----------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | :------------------------: | :--: | :----: | :----------: | :------: | :----------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| yolov5-s | 640 | 37.7 | 7.235 | 8.265 | [config](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth) | | yolov5-s | 640 | 37.7 | 7.235 | 8.265 | [config](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth) |
| yolov5_s_spos_shufflenetv2 | 640 | 37.9 | 7.04(-2.7%) | 7.03 | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/razor/subnets/yolov5_s_spos_shufflenetv2_syncbn_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmrazor/v1/spos/yolov5/yolov5_s_spos_shufflenetv2_syncbn_8xb16-300e_coco_20230109_155302-777fd6f1.pth) | | yolov5_s_spos_shufflenetv2 | 640 | 37.9 | 7.04(-2.7%) | 7.03 | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/razor/subnets/yolov5_s_spos_shufflenetv2_syncbn_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmrazor/v1/spos/yolov5/yolov5_s_spos_shufflenetv2_syncbn_8xb16-300e_coco_20230109_155302-777fd6f1.pth) |

2
configs/rtmdet/README.md
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@ -23,7 +23,7 @@ RTMDet-l model structure
## Object Detection ## Object Detection
| Model | size | Params(M) | FLOPS(G) | TRT-FP16-Latency(ms) | box AP | TTA box AP | Config | Download | | Model | size | Params(M) | FLOPs(G) | TRT-FP16-Latency(ms) | box AP | TTA box AP | Config | Download |
| :------------: | :--: | :-------: | :------: | :------------------: | :---------: | :---------: | :---------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | :------------: | :--: | :-------: | :------: | :------------------: | :---------: | :---------: | :---------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| RTMDet-tiny | 640 | 4.8 | 8.1 | 0.98 | 41.0 | 42.7 | [config](./rtmdet_tiny_syncbn_fast_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_tiny_syncbn_fast_8xb32-300e_coco/rtmdet_tiny_syncbn_fast_8xb32-300e_coco_20230102_140117-dbb1dc83.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_tiny_syncbn_fast_8xb32-300e_coco/rtmdet_tiny_syncbn_fast_8xb32-300e_coco_20230102_140117.log.json) | | RTMDet-tiny | 640 | 4.8 | 8.1 | 0.98 | 41.0 | 42.7 | [config](./rtmdet_tiny_syncbn_fast_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_tiny_syncbn_fast_8xb32-300e_coco/rtmdet_tiny_syncbn_fast_8xb32-300e_coco_20230102_140117-dbb1dc83.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_tiny_syncbn_fast_8xb32-300e_coco/rtmdet_tiny_syncbn_fast_8xb32-300e_coco_20230102_140117.log.json) |
| RTMDet-tiny \* | 640 | 4.8 | 8.1 | 0.98 | 41.8 (+0.8) | 43.2 (+0.5) | [config](./distillation/kd_tiny_rtmdet_s_neck_300e_coco.py) | [model](https://download.openmmlab.com/mmrazor/v1/rtmdet_distillation/kd_tiny_rtmdet_s_neck_300e_coco/kd_tiny_rtmdet_s_neck_300e_coco_20230213_104240-e1e4197c.pth) \| [log](https://download.openmmlab.com/mmrazor/v1/rtmdet_distillation/kd_tiny_rtmdet_s_neck_300e_coco/kd_tiny_rtmdet_s_neck_300e_coco_20230213_104240-176901d8.json) | | RTMDet-tiny \* | 640 | 4.8 | 8.1 | 0.98 | 41.8 (+0.8) | 43.2 (+0.5) | [config](./distillation/kd_tiny_rtmdet_s_neck_300e_coco.py) | [model](https://download.openmmlab.com/mmrazor/v1/rtmdet_distillation/kd_tiny_rtmdet_s_neck_300e_coco/kd_tiny_rtmdet_s_neck_300e_coco_20230213_104240-e1e4197c.pth) \| [log](https://download.openmmlab.com/mmrazor/v1/rtmdet_distillation/kd_tiny_rtmdet_s_neck_300e_coco/kd_tiny_rtmdet_s_neck_300e_coco_20230213_104240-176901d8.json) |

2
configs/yolov6/README.md
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@ -31,7 +31,7 @@ YOLOv6-l model structure
| 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-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-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-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-m | P5 | 640 | 300 | Yes | Yes | 16.69 | 48.4 | [config](../yolov6/yolov6_m_syncbn_fast_8xb32-300e_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) | | 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**: **Note**:

20
docs/en/get_started/overview.md
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@ -12,6 +12,16 @@ MMYOLO is an open-source algorithms toolkit of YOLO based on PyTorch and MMDetec
<img src="https://user-images.githubusercontent.com/17425982/220060451-d50414e5-a239-45b7-a4db-ed8699820300.png" alt="vision diagram"/> <img src="https://user-images.githubusercontent.com/17425982/220060451-d50414e5-a239-45b7-a4db-ed8699820300.png" alt="vision diagram"/>
</div> </div>
The following tasks are currently supported:
<details open>
<summary><b>Tasks currently supported</b></summary>
- object detection
- rotated object detection
</details>
The YOLO series of algorithms currently supported are as follows: The YOLO series of algorithms currently supported are as follows:
<details open> <details open>
@ -27,13 +37,15 @@ The YOLO series of algorithms currently supported are as follows:
</details> </details>
The following tasks are currently supported: The datasets currently supported are as follows:
<details open> <details open>
<summary><b>Tasks currently supported</b></summary> <summary><b>Datasets currently supported</b></summary>
- object detection - COCO Dataset
- rotated object detection - VOC Dataset
- CrowdHuman Dataset
- DOTA 1.0 Dataset
</details> </details>

14
docs/en/index.rst
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@ -78,19 +78,14 @@ You can switch between Chinese and English documents in the top-right corner of
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 2
:caption: Advanced Tutorials :caption: Advanced Tutorials
advanced_guides/cross-library_application.md advanced_guides/cross-library_application.md
.. toctree::
:maxdepth: 1
:caption: API Reference
api.rst
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 2
:caption: Model Zoo :caption: Model Zoo
model_zoo.md model_zoo.md
@ -104,6 +99,11 @@ You can switch between Chinese and English documents in the top-right corner of
notes/conventions.md notes/conventions.md
notes/code_style.md notes/code_style.md
.. toctree::
:maxdepth: 1
:caption: API Reference
api.rst
.. toctree:: .. toctree::
:caption: Switch Language :caption: Switch Language

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docs/en/model_zoo.md
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@ -1,36 +1,90 @@
# Benchmark and Model Zoo # Model Zoo and Benchmark
## Common settings This page is used to summarize the performance and related evaluation metrics of various models supported in MMYOLO for users to compare and analyze.
- All models were trained on `coco_2017_train`, and tested on the `coco_2017_val`. ## COCO dataset
- We use distributed training.
## Baselines | Model | Arch | Size | Batch Size | Epoch | SyncBN | AMP | Mem (GB) | Params(M) | FLOPs(G) | TRT-FP16-GPU-Latency(ms) | Box AP | TTA Box AP |
| :--------------: | :--: | :--: | :--------: | :---: | :----: | :-: | :------: | :-------: | :------: | :----------------------: | :----: | :--------: |
| YOLOv5-n | P5 | 640 | 8xb16 | 300 | Yes | Yes | 1.5 | 1.87 | 2.26 | 1.14 | 28.0 | 30.7 |
| YOLOv6-v2.0-n | P5 | 640 | 8xb32 | 400 | Yes | Yes | 6.04 | 4.32 | 5.52 | 1.37 | 36.2 | |
| YOLOv8-n | P5 | 640 | 8xb16 | 500 | Yes | Yes | 2.5 | 3.16 | 4.4 | 1.53 | 37.4 | 39.9 |
| RTMDet-tiny | P5 | 640 | 8xb32 | 300 | Yes | No | 11.9 | 4.90 | 8.09 | 2.31 | 41.8 | 43.2 |
| YOLOv6-v2.0-tiny | P5 | 640 | 8xb32 | 400 | Yes | Yes | 8.13 | 9.70 | 12.37 | 2.19 | 41.0 | |
| YOLOv7-tiny | P5 | 640 | 8xb16 | 300 | Yes | Yes | 2.7 | 6.23 | 6.89 | 1.88 | 37.5 | |
| YOLOX-tiny | P5 | 416 | 8xb32 | 300 | No | Yes | 4.9 | 5.06 | 7.63 | 1.19 | 34.3 | |
| RTMDet-s | P5 | 640 | 8xb32 | 300 | Yes | No | 16.3 | 8.89 | 14.84 | 2.89 | 45.7 | 47.3 |
| YOLOv5-s | P5 | 640 | 8xb16 | 300 | Yes | Yes | 2.7 | 7.24 | 8.27 | 1.89 | 37.7 | 40.2 |
| YOLOv6-v2.0-s | P5 | 640 | 8xb32 | 400 | Yes | Yes | 8.88 | 17.22 | 21.94 | 2.67 | 44.0 | |
| YOLOv8-s | P5 | 640 | 8xb16 | 500 | Yes | Yes | 4.0 | 11.17 | 14.36 | 2.61 | 45.1 | 46.8 |
| YOLOX-s | P5 | 640 | 8xb32 | 300 | No | Yes | 9.8 | 8.97 | 13.40 | 2.38 | 41.9 | |
| PPYOLOE+ -s | P5 | 640 | 8xb8 | 80 | Yes | No | 4.7 | 7.93 | 8.68 | 2.54 | 43.5 | |
| RTMDet-m | P5 | 640 | 8xb32 | 300 | Yes | No | 29.0 | 24.71 | 39.21 | 6.23 | 50.2 | 51.9 |
| YOLOv5-m | P5 | 640 | 8xb16 | 300 | Yes | Yes | 5.0 | 21.19 | 24.53 | 4.28 | 45.3 | 46.9 |
| YOLOv6-v2.0-m | P5 | 640 | 8xb32 | 300 | Yes | Yes | 16.69 | 34.25 | 40.7 | 5.12 | 48.4 | |
| YOLOv8-m | P5 | 640 | 8xb16 | 500 | Yes | Yes | 7.0 | 25.9 | 39.57 | 5.78 | 50.6 | 52.3 |
| YOLOX-m | P5 | 640 | 8xb32 | 300 | No | Yes | 17.6 | 25.33 | 36.88 | 5.31 | 47.5 | |
| PPYOLOE+ -m | P5 | 640 | 8xb8 | 80 | Yes | No | 8.4 | 23.43 | 24.97 | 5.47 | 49.5 | |
| RTMDet-l | P5 | 640 | 8xb32 | 300 | Yes | No | 45.2 | 52.32 | 80.12 | 10.13 | 52.3 | 53.7 |
| YOLOv5-l | P5 | 640 | 8xb16 | 300 | Yes | Yes | 8.1 | 46.56 | 54.65 | 6.8 | 48.8 | 49.9 |
| YOLOv6-v2.0-l | P5 | 640 | 8xb32 | 300 | Yes | Yes | 20.86 | 58.53 | 71.43 | 8.78 | 51.0 | |
| YOLOv7-l | P5 | 640 | 8xb16 | 300 | Yes | Yes | 10.3 | 36.93 | 52.42 | 6.63 | 50.9 | |
| YOLOv8-l | P5 | 640 | 8xb16 | 500 | Yes | Yes | 9.1 | 43.69 | 82.73 | 8.97 | 53.0 | 54.4 |
| YOLOX-l | P5 | 640 | 8xb8 | 300 | No | Yes | 8.0 | 54.21 | 77.83 | 9.23 | 50.1 | |
| PPYOLOE+ -l | P5 | 640 | 8xb8 | 80 | Yes | No | 13.2 | 52.20 | 55.05 | 8.2 | 52.6 | |
| RTMDet-x | P5 | 640 | 8xb32 | 300 | Yes | No | 63.4 | 94.86 | 145.41 | 17.89 | 52.8 | 54.2 |
| YOLOv7-x | P5 | 640 | 8xb16 | 300 | Yes | Yes | 13.7 | 71.35 | 95.06 | 11.63 | 52.8 | |
| YOLOv8-x | P5 | 640 | 8xb16 | 500 | Yes | Yes | 12.4 | 68.23 | 132.10 | 14.22 | 54.0 | 55.0 |
| YOLOX-x | P5 | 640 | 8xb8 | 300 | No | Yes | 9.8 | 99.07 | 144.39 | 15.35 | 51.4 | |
| PPYOLOE+ -x | P5 | 640 | 8xb8 | 80 | Yes | No | 19.1 | 98.42 | 105.48 | 14.02 | 54.2 | |
| YOLOv5-n | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 5.8 | 3.25 | 2.30 | | 35.9 | |
| YOLOv5-s | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 10.5 | 12.63 | 8.45 | | 44.4 | |
| YOLOv5-m | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 19.1 | 35.73 | 25.05 | | 51.3 | |
| YOLOv5-l | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 30.5 | 76.77 | 55.77 | | 53.7 | |
| YOLOv7-w | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 27.0 | 82.31 | 45.07 | | 54.1 | |
| YOLOv7-e | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 42.5 | 114.69 | 64.48 | | 55.1 | |
### YOLOv5 - All the models are trained on COCO train2017 dataset and evaluated on val2017 dataset.
- TRT-FP16-GPU-Latency(ms) is the GPU Compute time on NVIDIA Tesla T4 device with TensorRT 8.4, a batch size of 1, a test shape of 640x640 and only model forward (The test shape for YOLOX-tiny is 416x416)
- The number of model parameters and FLOPs are obtained using the [get_flops](https://github.com/open-mmlab/mmyolo/blob/dev/tools/analysis_tools/get_flops.py) script. Different calculation methods may vary slightly
- RTMDet performance is the result of training with [MMRazor Knowledge Distillation](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/distillation/README.md)
- Only YOLOv6 version 2.0 is implemented in MMYOLO for now, and L and M are the results without knowledge distillation
- YOLOv8 results are optimized using mask instance annotation, but YOLOv5, YOLOv6 and YOLOv7 do not use
- PPYOLOE+ uses Obj365 as pre-training weights, so the number of epochs for COCO training only needs 80
- YOLOX-tiny, YOLOX-s and YOLOX-m are trained with the optimizer parameters proposed in RTMDet, with different degrees of performance improvement compared to the original implementation.
Please refer to [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)。 Please see below items for more details
### YOLOv6 - [RTMDet](https://github.com/open-mmlab/mmyolo/blob/main/configs/rtmdet)
- [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)
- [YOLOv6](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov6)
- [YOLOv7](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov7)
- [YOLOv8](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov8)
- [YOLOX](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolox)
- [PPYOLO-E](https://github.com/open-mmlab/mmyolo/blob/main/configs/ppyoloe)
Please refer to [YOLOv6](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov6)。 ## VOC dataset
### YOLOv7 | Backbone | size | Batchsize | AMP | Mem (GB) | box AP(COCO metric) |
| :------: | :--: | :-------: | :-: | :------: | :-----------------: |
| YOLOv5-n | 512 | 64 | Yes | 3.5 | 51.2 |
| YOLOv5-s | 512 | 64 | Yes | 6.5 | 62.7 |
| YOLOv5-m | 512 | 64 | Yes | 12.0 | 70.1 |
| YOLOv5-l | 512 | 32 | Yes | 10.0 | 73.1 |
Please refer to [YOLOv7](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov7)。 Please see below items for more details
### YOLOv8 - [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)
Please refer to [YOLOv8](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov8)。 ## CrowdHuman dataset
### YOLOX | Backbone | size | SyncBN | AMP | Mem (GB) | ignore_iof_thr | box AP50(CrowDHuman Metric) | MR | JI |
| :------: | :--: | :----: | :-: | :------: | :------------: | :-------------------------: | :--: | :---: |
| YOLOv5-s | 640 | Yes | Yes | 2.6 | -1 | 85.79 | 48.7 | 75.33 |
| YOLOv5-s | 640 | Yes | Yes | 2.6 | 0.5 | 86.17 | 48.8 | 75.87 |
Please refer to [YOLOX](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolox)。 Please see below items for more details
### PPYOLO-E - [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)
Please refer to [PPYOLO-E](https://github.com/open-mmlab/mmyolo/blob/main/configs/ppyoloe)。 ## DOTA 1.0 dataset
### RTMDet
Please refer to [RTMDet](https://github.com/open-mmlab/mmyolo/blob/main/configs/rtmdet)。

20
docs/zh_cn/get_started/overview.md
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@ -12,6 +12,16 @@ MMYOLO 是一个基于 PyTorch 和 MMDetection 的 YOLO 系列算法开源工具
<img src="https://user-images.githubusercontent.com/17425982/220060451-d50414e5-a239-45b7-a4db-ed8699820300.png" alt="愿景图"/> <img src="https://user-images.githubusercontent.com/17425982/220060451-d50414e5-a239-45b7-a4db-ed8699820300.png" alt="愿景图"/>
</div> </div>
目前支持的任务如下：
<details open>
<summary><b>支持的任务</b></summary>
- 目标检测
- 旋转框目标检测
</details>
目前支持的 YOLO 系列算法如下： 目前支持的 YOLO 系列算法如下：
<details open> <details open>
@ -27,13 +37,15 @@ MMYOLO 是一个基于 PyTorch 和 MMDetection 的 YOLO 系列算法开源工具
</details> </details>
目前支持的任务如下： 目前支持的数据集如下：
<details open> <details open>
<summary><b>支持的任务</b></summary> <summary><b>支持的数据集</b></summary>
- 目标检测 - COCO Dataset
- 旋转框目标检测 - VOC Dataset
- CrowdHuman Dataset
- DOTA 1.0 Dataset
</details> </details>

14
docs/zh_cn/index.rst
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@ -79,19 +79,14 @@
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 2
:caption: 进阶教程 :caption: 进阶教程
advanced_guides/cross-library_application.md advanced_guides/cross-library_application.md
.. toctree::
:maxdepth: 1
:caption: 接口文档（英文）
api.rst
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 2
:caption: 模型仓库 :caption: 模型仓库
model_zoo.md model_zoo.md
@ -105,6 +100,11 @@
notes/conventions.md notes/conventions.md
notes/code_style.md notes/code_style.md
.. toctree::
:maxdepth: 1
:caption: 接口文档（英文）
api.rst
.. toctree:: .. toctree::
:caption: 语言切换 :caption: 语言切换

96
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@ -1,36 +1,90 @@
# 模型库 # 模型库和评测
## 共同设置 本页面用于汇总 MMYOLO 中支持的各类模型性能和相关评测指标，方便用户对比分析。
- 所有模型都是在 `coco_2017_train` 上训练，在 `coco_2017_val` 上测试。 ## COCO 数据集
- 我们使用分布式训练。
## Baselines | Model | Arch | Size | Batch Size | Epoch | SyncBN | AMP | Mem (GB) | Params(M) | FLOPs(G) | TRT-FP16-GPU-Latency(ms) | Box AP | TTA Box AP |
| :--------------: | :--: | :--: | :--------: | :---: | :----: | :-: | :------: | :-------: | :------: | :----------------------: | :----: | :--------: |
| YOLOv5-n | P5 | 640 | 8xb16 | 300 | Yes | Yes | 1.5 | 1.87 | 2.26 | 1.14 | 28.0 | 30.7 |
| YOLOv6-v2.0-n | P5 | 640 | 8xb32 | 400 | Yes | Yes | 6.04 | 4.32 | 5.52 | 1.37 | 36.2 | |
| YOLOv8-n | P5 | 640 | 8xb16 | 500 | Yes | Yes | 2.5 | 3.16 | 4.4 | 1.53 | 37.4 | 39.9 |
| RTMDet-tiny | P5 | 640 | 8xb32 | 300 | Yes | No | 11.9 | 4.90 | 8.09 | 2.31 | 41.8 | 43.2 |
| YOLOv6-v2.0-tiny | P5 | 640 | 8xb32 | 400 | Yes | Yes | 8.13 | 9.70 | 12.37 | 2.19 | 41.0 | |
| YOLOv7-tiny | P5 | 640 | 8xb16 | 300 | Yes | Yes | 2.7 | 6.23 | 6.89 | 1.88 | 37.5 | |
| YOLOX-tiny | P5 | 416 | 8xb32 | 300 | No | Yes | 4.9 | 5.06 | 7.63 | 1.19 | 34.3 | |
| RTMDet-s | P5 | 640 | 8xb32 | 300 | Yes | No | 16.3 | 8.89 | 14.84 | 2.89 | 45.7 | 47.3 |
| YOLOv5-s | P5 | 640 | 8xb16 | 300 | Yes | Yes | 2.7 | 7.24 | 8.27 | 1.89 | 37.7 | 40.2 |
| YOLOv6-v2.0-s | P5 | 640 | 8xb32 | 400 | Yes | Yes | 8.88 | 17.22 | 21.94 | 2.67 | 44.0 | |
| YOLOv8-s | P5 | 640 | 8xb16 | 500 | Yes | Yes | 4.0 | 11.17 | 14.36 | 2.61 | 45.1 | 46.8 |
| YOLOX-s | P5 | 640 | 8xb32 | 300 | No | Yes | 9.8 | 8.97 | 13.40 | 2.38 | 41.9 | |
| PPYOLOE+ -s | P5 | 640 | 8xb8 | 80 | Yes | No | 4.7 | 7.93 | 8.68 | 2.54 | 43.5 | |
| RTMDet-m | P5 | 640 | 8xb32 | 300 | Yes | No | 29.0 | 24.71 | 39.21 | 6.23 | 50.2 | 51.9 |
| YOLOv5-m | P5 | 640 | 8xb16 | 300 | Yes | Yes | 5.0 | 21.19 | 24.53 | 4.28 | 45.3 | 46.9 |
| YOLOv6-v2.0-m | P5 | 640 | 8xb32 | 300 | Yes | Yes | 16.69 | 34.25 | 40.7 | 5.12 | 48.4 | |
| YOLOv8-m | P5 | 640 | 8xb16 | 500 | Yes | Yes | 7.0 | 25.9 | 39.57 | 5.78 | 50.6 | 52.3 |
| YOLOX-m | P5 | 640 | 8xb32 | 300 | No | Yes | 17.6 | 25.33 | 36.88 | 5.31 | 47.5 | |
| PPYOLOE+ -m | P5 | 640 | 8xb8 | 80 | Yes | No | 8.4 | 23.43 | 24.97 | 5.47 | 49.5 | |
| RTMDet-l | P5 | 640 | 8xb32 | 300 | Yes | No | 45.2 | 52.32 | 80.12 | 10.13 | 52.3 | 53.7 |
| YOLOv5-l | P5 | 640 | 8xb16 | 300 | Yes | Yes | 8.1 | 46.56 | 54.65 | 6.8 | 48.8 | 49.9 |
| YOLOv6-v2.0-l | P5 | 640 | 8xb32 | 300 | Yes | Yes | 20.86 | 58.53 | 71.43 | 8.78 | 51.0 | |
| YOLOv7-l | P5 | 640 | 8xb16 | 300 | Yes | Yes | 10.3 | 36.93 | 52.42 | 6.63 | 50.9 | |
| YOLOv8-l | P5 | 640 | 8xb16 | 500 | Yes | Yes | 9.1 | 43.69 | 82.73 | 8.97 | 53.0 | 54.4 |
| YOLOX-l | P5 | 640 | 8xb8 | 300 | No | Yes | 8.0 | 54.21 | 77.83 | 9.23 | 50.1 | |
| PPYOLOE+ -l | P5 | 640 | 8xb8 | 80 | Yes | No | 13.2 | 52.20 | 55.05 | 8.2 | 52.6 | |
| RTMDet-x | P5 | 640 | 8xb32 | 300 | Yes | No | 63.4 | 94.86 | 145.41 | 17.89 | 52.8 | 54.2 |
| YOLOv7-x | P5 | 640 | 8xb16 | 300 | Yes | Yes | 13.7 | 71.35 | 95.06 | 11.63 | 52.8 | |
| YOLOv8-x | P5 | 640 | 8xb16 | 500 | Yes | Yes | 12.4 | 68.23 | 132.10 | 14.22 | 54.0 | 55.0 |
| YOLOX-x | P5 | 640 | 8xb8 | 300 | No | Yes | 9.8 | 99.07 | 144.39 | 15.35 | 51.4 | |
| PPYOLOE+ -x | P5 | 640 | 8xb8 | 80 | Yes | No | 19.1 | 98.42 | 105.48 | 14.02 | 54.2 | |
| YOLOv5-n | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 5.8 | 3.25 | 2.30 | | 35.9 | |
| YOLOv5-s | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 10.5 | 12.63 | 8.45 | | 44.4 | |
| YOLOv5-m | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 19.1 | 35.73 | 25.05 | | 51.3 | |
| YOLOv5-l | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 30.5 | 76.77 | 55.77 | | 53.7 | |
| YOLOv7-w | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 27.0 | 82.31 | 45.07 | | 54.1 | |
| YOLOv7-e | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 42.5 | 114.69 | 64.48 | | 55.1 | |
### YOLOv5 - 所有模型均使用 COCO train2017 作为训练集，在 COCO val2017 上验证精度
- TRT-FP16-GPU-Latency(ms) 是指在 NVIDIA Tesla T4 设备上采用 TensorRT 8.4，batch size 为 1， 测试 shape 为 640x640 且仅包括模型 forward 的 GPU Compute time (YOLOX-tiny 测试 shape 是 416x416)
- 模型参数量和 FLOPs 是采用 [get_flops](https://github.com/open-mmlab/mmyolo/blob/dev/tools/analysis_tools/get_flops.py) 脚本得到，不同的运算方式可能略有不同
- RTMDet 性能是通过 [MMRazor 知识蒸馏](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/distillation/README.md) 训练后的结果
- MMYOLO 中暂时只实现了 YOLOv6 2.0 版本，并且 L 和 M 为没有经过知识蒸馏的结果
- YOLOv8 是引入了实例分割标注优化后的结果，YOLOv5、YOLOv6 和 YOLOv7 没有采用实例分割标注优化
- PPYOLOE+ 使用 Obj365 作为预训练权重，因此 COCO 训练的 epoch 数只需要 80
- YOLOX-tiny、YOLOX-s 和 YOLOX-m 为采用了 RTMDet 中所提优化器参数训练所得，性能相比原始实现有不同程度提升
请参考 [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)。 详情见如下内容
### YOLOv6 - [RTMDet](https://github.com/open-mmlab/mmyolo/blob/main/configs/rtmdet)
- [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)
- [YOLOv6](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov6)
- [YOLOv7](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov7)
- [YOLOv8](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov8)
- [YOLOX](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolox)
- [PPYOLO-E](https://github.com/open-mmlab/mmyolo/blob/main/configs/ppyoloe)
请参考 [YOLOv6](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov6)。 ## VOC 数据集
### YOLOv7 | Backbone | size | Batchsize | AMP | Mem (GB) | box AP(COCO metric) |
| :------: | :--: | :-------: | :-: | :------: | :-----------------: |
| YOLOv5-n | 512 | 64 | Yes | 3.5 | 51.2 |
| YOLOv5-s | 512 | 64 | Yes | 6.5 | 62.7 |
| YOLOv5-m | 512 | 64 | Yes | 12.0 | 70.1 |
| YOLOv5-l | 512 | 32 | Yes | 10.0 | 73.1 |
请参考 [YOLOv7](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov7)。 详情见如下内容
### YOLOv8 - [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)
请参考 [YOLOv8](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov8)。 ## CrowdHuman 数据集
### YOLOX | Backbone | size | SyncBN | AMP | Mem (GB) | ignore_iof_thr | box AP50(CrowDHuman Metric) | MR | JI |
| :------: | :--: | :----: | :-: | :------: | :------------: | :-------------------------: | :--: | :---: |
| YOLOv5-s | 640 | Yes | Yes | 2.6 | -1 | 85.79 | 48.7 | 75.33 |
| YOLOv5-s | 640 | Yes | Yes | 2.6 | 0.5 | 86.17 | 48.8 | 75.87 |
请参考 [YOLOX](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolox)。 详情见如下内容
### PPYOLO-E - [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)
请参考 [PPYOLO-E](https://github.com/open-mmlab/mmyolo/blob/main/configs/ppyoloe)。 ## DOTA 1.0 数据集
### RTMDet
请参考 [RTMDet](https://github.com/open-mmlab/mmyolo/blob/main/configs/rtmdet)。

3
tools/analysis_tools/get_flops.py
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@ -11,6 +11,8 @@ from mmengine.logging import MMLogger
from mmengine.model import revert_sync_batchnorm from mmengine.model import revert_sync_batchnorm
from mmengine.registry import init_default_scope from mmengine.registry import init_default_scope
from mmyolo.utils import switch_to_deploy
def parse_args(): def parse_args():
parser = argparse.ArgumentParser(description='Get a detector flops') parser = argparse.ArgumentParser(description='Get a detector flops')
@ -68,6 +70,7 @@ def inference(args, logger):
model.cuda() model.cuda()
model = revert_sync_batchnorm(model) model = revert_sync_batchnorm(model)
model.eval() model.eval()
switch_to_deploy(model)
# input tensor # input tensor
# automatically generate a input tensor with the given input_shape. # automatically generate a input tensor with the given input_shape.