[Feature] Add model zoo (#607)

* Update model_zoo.md * fix * update * update * update
2023-03-01 16:34:17 +08:00 · 2023-03-01 16:34:17 +08:00 · cdc885550a
parent 6f38b781bd
commit cdc885550a
12 changed files with 238 additions and 67 deletions
--- a/README.md
+++ b/README.md
@ -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).

+<details open>
+<summary><b>Supported Tasks</b></summary>
+
+- [x] Object detection
+- [x] Rotated object detection
+
+</details>
+
 <details open>
 <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 open>
+<summary><b>Supported Datasets</b></summary>
+
+- [x] COCO Dataset
+- [x] VOC Dataset
+- [x] CrowdHuman Dataset
+- [x] DOTA 1.0 Dataset
+
+</details>
+
 <details open>
 <div align="center">
  <b>Module Components</b>
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@ -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。

+<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>
 <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 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>
 <div align="center">
  <b>模块组件</b>
--- a/configs/razor/subnets/README.md
+++ b/configs/razor/subnets/README.md
@ -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.

-|           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_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)              |
--- a/configs/rtmdet/README.md
+++ b/configs/rtmdet/README.md
@ -23,7 +23,7 @@ RTMDet-l model structure

 ## 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         | 41.8 (+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) |
--- a/configs/yolov6/README.md
+++ b/configs/yolov6/README.md
@ -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-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-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) |

 **Note**:
--- a/docs/en/get_started/overview.md
+++ b/docs/en/get_started/overview.md
@ -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"/>
 </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:

 <details open>
@ -27,13 +37,15 @@ The YOLO series of algorithms currently supported are as follows:

 </details>

-The following tasks are currently supported:
+The datasets currently supported are as follows:

 <details open>
-<summary><b>Tasks currently supported</b></summary>
+<summary><b>Datasets currently supported</b></summary>

- object detection
- rotated object detection
+- COCO Dataset
+- VOC Dataset
+- CrowdHuman Dataset
+- DOTA 1.0 Dataset

 </details>

--- a/docs/en/index.rst
+++ b/docs/en/index.rst
@ -78,19 +78,14 @@ You can switch between Chinese and English documents in the top-right corner of


 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 2
   :caption: Advanced Tutorials

   advanced_guides/cross-library_application.md

-.. toctree::
-   :maxdepth: 1
-   :caption: API Reference
-
-   api.rst

 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 2
   :caption: Model Zoo

   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/code_style.md

+.. toctree::
+   :maxdepth: 1
+   :caption: API Reference
+
+   api.rst

 .. toctree::
   :caption: Switch Language
--- a/docs/en/model_zoo.md
+++ b/docs/en/model_zoo.md
@ -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`.
- We use distributed training.
+## COCO dataset

-## 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)。
-
-### RTMDet
-
-Please refer to [RTMDet](https://github.com/open-mmlab/mmyolo/blob/main/configs/rtmdet)。
+## DOTA 1.0 dataset
--- a/docs/zh_cn/get_started/overview.md
+++ b/docs/zh_cn/get_started/overview.md
@ -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"/>
 </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：

 <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>

-目前支持的任务如下：
+目前支持的数据集如下：

 <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
- 旋转框目标检测
+- COCO Dataset
+- VOC Dataset
+- CrowdHuman Dataset
+- DOTA 1.0 Dataset

 </details>

--- a/docs/zh_cn/index.rst
+++ b/docs/zh_cn/index.rst
@ -79,19 +79,14 @@


 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 2
   :caption: 进阶教程

   advanced_guides/cross-library_application.md

-.. toctree::
-   :maxdepth: 1
-   :caption: 接口文档（英文）
-
-   api.rst

 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 2
   :caption: 模型仓库

   model_zoo.md
@ -105,6 +100,11 @@
   notes/conventions.md
   notes/code_style.md

+.. toctree::
+   :maxdepth: 1
+   :caption: 接口文档（英文）
+
+   api.rst

 .. toctree::
   :caption: 语言切换
--- a/docs/zh_cn/model_zoo.md
+++ b/docs/zh_cn/model_zoo.md
@ -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。
- 我们使用分布式训练。
+## COCO 数据集

-## 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)。
-
-### RTMDet
-
-请参考 [RTMDet](https://github.com/open-mmlab/mmyolo/blob/main/configs/rtmdet)。
+## DOTA 1.0 数据集
--- a/tools/analysis_tools/get_flops.py
+++ b/tools/analysis_tools/get_flops.py
@ -11,6 +11,8 @@ from mmengine.logging import MMLogger
 from mmengine.model import revert_sync_batchnorm
 from mmengine.registry import init_default_scope

+from mmyolo.utils import switch_to_deploy
+

 def parse_args():
    parser = argparse.ArgumentParser(description='Get a detector flops')
@ -68,6 +70,7 @@ def inference(args, logger):
        model.cuda()
    model = revert_sync_batchnorm(model)
    model.eval()
+    switch_to_deploy(model)

    # input tensor
    # automatically generate a input tensor with the given input_shape.