[Feature] Support YOLOv5u inference (#624)

* support yolov5u inference * minor fix * Update README.md * Add all configs * unify config links
2023-03-07 10:23:13 +08:00 · 2023-03-07 10:23:13 +08:00 · 69b43e696d
parent 356858517a
commit 69b43e696d
21 changed files with 825 additions and 77 deletions
--- a/README.md
+++ b/README.md
@ -283,6 +283,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 <summary><b>Supported Algorithms</b></summary>

 - [x] [YOLOv5](configs/yolov5)
+- [ ] [YOLOv5u](configs/yolov5/yolov5u) (Inference only)
 - [x] [YOLOX](configs/yolox)
 - [x] [RTMDet](configs/rtmdet)
 - [x] [RTMDet-Rotated](configs/rtmdet)
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@ -305,6 +305,7 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
 <summary><b>支持的算法</b></summary>

 - [x] [YOLOv5](configs/yolov5)
+- [ ] [YOLOv5u](configs/yolov5/yolov5u) (仅推理)
 - [x] [YOLOX](configs/yolox)
 - [x] [RTMDet](configs/rtmdet)
 - [x] [RTMDet-Rotated](configs/rtmdet)
--- a/configs/ppyoloe/README.md
+++ b/configs/ppyoloe/README.md
@ -19,12 +19,12 @@ PPYOLOE-PLUS-l model structure

 ### PPYOLOE+ COCO

-|  Backbone   | Arch | Size | Epoch | SyncBN | Mem (GB) | Box AP |                          Config                           |                                                                                                                                                      Download                                                                                                                                                      |
-| :---------: | :--: | :--: | :---: | :----: | :------: | :----: | :-------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| PPYOLOE+ -s |  P5  | 640  |  80   |  Yes   |   4.7    |  43.5  | [config](../ppyoloe/ppyoloe_plus_s_fast_8xb8-80e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_s_fast_8xb8-80e_coco/ppyoloe_plus_s_fast_8xb8-80e_coco_20230101_154052-9fee7619.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_s_fast_8xb8-80e_coco/ppyoloe_plus_s_fast_8xb8-80e_coco_20230101_154052.log.json) |
-| PPYOLOE+ -m |  P5  | 640  |  80   |  Yes   |   8.4    |  49.5  | [config](../ppyoloe/ppyoloe_plus_m_fast_8xb8-80e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_m_fast_8xb8-80e_coco/ppyoloe_plus_m_fast_8xb8-80e_coco_20230104_193132-e4325ada.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_m_fast_8xb8-80e_coco/ppyoloe_plus_m_fast_8xb8-80e_coco_20230104_193132.log.json) |
-| PPYOLOE+ -l |  P5  | 640  |  80   |  Yes   |   13.2   |  52.6  | [config](../ppyoloe/ppyoloe_plus_l_fast_8xb8-80e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_l_fast_8xb8-80e_coco/ppyoloe_plus_l_fast_8xb8-80e_coco_20230102_203825-1864e7b3.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_l_fast_8xb8-80e_coco/ppyoloe_plus_l_fast_8xb8-80e_coco_20230102_203825.log.json) |
-| PPYOLOE+ -x |  P5  | 640  |  80   |  Yes   |   19.1   |  54.2  | [config](../ppyoloe/ppyoloe_plus_x_fast_8xb8-80e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_x_fast_8xb8-80e_coco/ppyoloe_plus_x_fast_8xb8-80e_coco_20230104_194921-8c953949.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_x_fast_8xb8-80e_coco/ppyoloe_plus_x_fast_8xb8-80e_coco_20230104_194921.log.json) |
+|  Backbone   | Arch | Size | Epoch | SyncBN | Mem (GB) | Box AP |                      Config                      |                                                                                                                                                      Download                                                                                                                                                      |
+| :---------: | :--: | :--: | :---: | :----: | :------: | :----: | :----------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| PPYOLOE+ -s |  P5  | 640  |  80   |  Yes   |   4.7    |  43.5  | [config](./ppyoloe_plus_s_fast_8xb8-80e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_s_fast_8xb8-80e_coco/ppyoloe_plus_s_fast_8xb8-80e_coco_20230101_154052-9fee7619.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_s_fast_8xb8-80e_coco/ppyoloe_plus_s_fast_8xb8-80e_coco_20230101_154052.log.json) |
+| PPYOLOE+ -m |  P5  | 640  |  80   |  Yes   |   8.4    |  49.5  | [config](./ppyoloe_plus_m_fast_8xb8-80e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_m_fast_8xb8-80e_coco/ppyoloe_plus_m_fast_8xb8-80e_coco_20230104_193132-e4325ada.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_m_fast_8xb8-80e_coco/ppyoloe_plus_m_fast_8xb8-80e_coco_20230104_193132.log.json) |
+| PPYOLOE+ -l |  P5  | 640  |  80   |  Yes   |   13.2   |  52.6  | [config](./ppyoloe_plus_l_fast_8xb8-80e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_l_fast_8xb8-80e_coco/ppyoloe_plus_l_fast_8xb8-80e_coco_20230102_203825-1864e7b3.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_l_fast_8xb8-80e_coco/ppyoloe_plus_l_fast_8xb8-80e_coco_20230102_203825.log.json) |
+| PPYOLOE+ -x |  P5  | 640  |  80   |  Yes   |   19.1   |  54.2  | [config](./ppyoloe_plus_x_fast_8xb8-80e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_x_fast_8xb8-80e_coco/ppyoloe_plus_x_fast_8xb8-80e_coco_20230104_194921-8c953949.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_plus_x_fast_8xb8-80e_coco/ppyoloe_plus_x_fast_8xb8-80e_coco_20230104_194921.log.json) |

 **Note**:

--- a/configs/razor/subnets/README.md
+++ b/configs/razor/subnets/README.md
@ -62,14 +62,14 @@ 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                                                                                                                                                                   |
-| :------------------------: | :--: | :----: | :----------: | :------: | :---------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-|          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) \| [log](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.log.json) |
-| yolov5_s_spos_shufflenetv2 | 640  |  38.0  | 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/yolo_nas_backbone/yolov5_s_spos_shufflenetv2_syncbn_8xb16-300e_coco_20230211_220635-578be9a9.pth) \| log                                                                                          |
-|          yolov6-s          | 640  |  44.0  |    18.869    |  24.253  |              [config](https://github.com/open-mmlab/mmyolo/blob/main/configs/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_l_attentivenas_a6  | 640  |  45.3  | 18.38(-2.6%) |   8.49   | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/razor/subnets/yolov6_l_attentivenas_a6_d12_syncbn_fast_8xb32-300e_coco.py) |                                                                                      [model](https://download.openmmlab.com/mmrazor/v1/yolo_nas_backbone/yolov6_l_attentivenas_a6_d12_syncbn_fast_8xb32-300e_coco_20230211_222409-dcc72668.pth) \| log                                                                                       |
-|        RTMDet-tiny         | 640  |  41.0  |     4.8      |   8.1    |                                            [config](./rtmdet_l_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_ofa_lat31    | 960  |  41.3  | 3.91(-18.5%) |   6.09   |      [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/razor/subnets/rtmdet_tiny_ofa_lat31_syncbn_16xb16-300e_coco.py)       |                                                                                            [model](https://download.openmmlab.com/mmrazor/v1/yolo_nas_backbone/rtmdet_tiny_ofa_lat31_syncbn_16xb16-300e_coco_20230214_210623-449bb2a0.pth) \| log                                                                                            |
+|           Model            | size | box AP |  Params(M)   | FLOPs(G) |                                 Config                                  |                                                                                                                                                                   Download                                                                                                                                                                   |
+| :------------------------: | :--: | :----: | :----------: | :------: | :---------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|          yolov5-s          | 640  |  37.7  |    7.235     |  8.265   |   [config](../../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) \| [log](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.log.json) |
+| yolov5_s_spos_shufflenetv2 | 640  |  38.0  | 7.04(-2.7%)  |   7.03   |    [config](./yolov5_s_spos_shufflenetv2_syncbn_8xb16-300e_coco.py)     |                                                                                          [model](https://download.openmmlab.com/mmrazor/v1/yolo_nas_backbone/yolov5_s_spos_shufflenetv2_syncbn_8xb16-300e_coco_20230211_220635-578be9a9.pth) \| log                                                                                          |
+|          yolov6-s          | 640  |  44.0  |    18.869    |  24.253  |     [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_l_attentivenas_a6  | 640  |  45.3  | 18.38(-2.6%) |   8.49   | [config](./yolov6_l_attentivenas_a6_d12_syncbn_fast_8xb32-300e_coco.py) |                                                                                      [model](https://download.openmmlab.com/mmrazor/v1/yolo_nas_backbone/yolov6_l_attentivenas_a6_d12_syncbn_fast_8xb32-300e_coco_20230211_222409-dcc72668.pth) \| log                                                                                       |
+|        RTMDet-tiny         | 640  |  41.0  |     4.8      |   8.1    |     [config](../../rtmdet/rtmdet_l_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_ofa_lat31    | 960  |  41.3  | 3.91(-18.5%) |   6.09   |      [config](./rtmdet_tiny_ofa_lat31_syncbn_16xb16-300e_coco.py)       |                                                                                            [model](https://download.openmmlab.com/mmrazor/v1/yolo_nas_backbone/rtmdet_tiny_ofa_lat31_syncbn_16xb16-300e_coco_20230214_210623-449bb2a0.pth) \| log                                                                                            |

 **Note**:

--- a/configs/yolov5/README.md
+++ b/configs/yolov5/README.md
@ -20,22 +20,27 @@ YOLOv5-l-P6 model structure

 ### COCO

-| Backbone | Arch | size | Mask Refine | SyncBN | AMP | Mem (GB) |   box AP    | TTA box AP |                                         Config                                          |                                                                                                                                                                                                       Download                                                                                                                                                                                                       |
-| :------: | :--: | :--: | :---------: | :----: | :-: | :------: | :---------: | :--------: | :-------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| YOLOv5-n |  P5  | 640  |     No      |  Yes   | Yes |   1.5    |    28.0     |    30.7    |             [config](../yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco.py)             |                                     [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco/yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739-b804c1ad.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco/yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739.log.json)                                     |
-| YOLOv5-n |  P5  | 640  |     Yes     |  Yes   | Yes |   1.5    |    28.0     |            | [config](../yolov5/mask_refine/yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_152706-712fb1b2.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_152706.log.json) |
-| YOLOv5-s |  P5  | 640  |     No      |  Yes   | Yes |   2.7    |    37.7     |    40.2    |             [config](../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) \| [log](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.log.json)                                     |
-| YOLOv5-s |  P5  | 640  |     Yes     |  Yes   | Yes |   2.7    | 38.0 (+0.3) |            | [config](../yolov5/mask_refine/yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230304_033134-8e0cd271.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230304_033134.log.json) |
-| YOLOv5-m |  P5  | 640  |     No      |  Yes   | Yes |   5.0    |    45.3     |    46.9    |             [config](../yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py)             |                                     [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944.log.json)                                     |
-| YOLOv5-m |  P5  | 640  |     Yes     |  Yes   | Yes |   5.0    |    45.3     |            | [config](../yolov5/mask_refine/yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_153946-44e96155.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_153946.log.json) |
-| YOLOv5-l |  P5  | 640  |     No      |  Yes   | Yes |   8.1    |    48.8     |    49.9    |             [config](../yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco.py)             |                                     [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco/yolov5_l-v61_syncbn_fast_8xb16-300e_coco_20220917_031007-096ef0eb.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco/yolov5_l-v61_syncbn_fast_8xb16-300e_coco_20220917_031007.log.json)                                     |
-| YOLOv5-l |  P5  | 640  |     Yes     |  Yes   | Yes |   8.1    | 49.3 (+0.5) |            | [config](../yolov5/mask_refine/yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_154301-2c1d912a.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_154301.log.json) |
-| YOLOv5-x |  P5  | 640  |     No      |  Yes   | Yes |   12.2   |    50.2     |            |             [config](../yolov5/yolov5_x-v61_syncbn_fast_8xb16-300e_coco.py)             |                                     [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_x-v61_syncbn_fast_8xb16-300e_coco/yolov5_x-v61_syncbn_fast_8xb16-300e_coco_20230305_152943-00776a4b.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_x-v61_syncbn_fast_8xb16-300e_coco/yolov5_x-v61_syncbn_fast_8xb16-300e_coco_20230305_152943.log.json)                                     |
-| YOLOv5-x |  P5  | 640  |     Yes     |  Yes   | Yes |   12.2   | 50.9 (+0.7) |            | [config](../yolov5/mask_refine/yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_154321-07edeb62.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_154321.log.json) |
-| YOLOv5-n |  P6  | 1280 |     No      |  Yes   | Yes |   5.8    |    35.9     |            |           [config](../yolov5/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco.py)            |                               [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_224705-d493c5f3.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_224705.log.json)                               |
-| YOLOv5-s |  P6  | 1280 |     No      |  Yes   | Yes |   10.5   |    44.4     |            |           [config](../yolov5/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py)            |                               [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_215044-58865c19.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_215044.log.json)                               |
-| YOLOv5-m |  P6  | 1280 |     No      |  Yes   | Yes |   19.1   |    51.3     |            |           [config](../yolov5/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco.py)            |                               [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_230453-49564d58.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_230453.log.json)                               |
-| YOLOv5-l |  P6  | 1280 |     No      |  Yes   | Yes |   30.5   |    53.7     |            |           [config](../yolov5/yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco.py)            |                               [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_234308-7a2ba6bf.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_234308.log.json)                               |
+| Backbone  | Arch | size | Mask Refine | SyncBN | AMP | Mem (GB) |   box AP    | TTA box AP |                                     Config                                      |                                                                                                                                                                                                       Download                                                                                                                                                                                                       |
+| :-------: | :--: | :--: | :---------: | :----: | :-: | :------: | :---------: | :--------: | :-----------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| YOLOv5-n  |  P5  | 640  |     No      |  Yes   | Yes |   1.5    |    28.0     |    30.7    |             [config](./yolov5_n-v61_syncbn_fast_8xb16-300e_coco.py)             |                                     [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco/yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739-b804c1ad.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco/yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739.log.json)                                     |
+| YOLOv5-n  |  P5  | 640  |     Yes     |  Yes   | Yes |   1.5    |    28.0     |            | [config](./mask_refine/yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_152706-712fb1b2.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_152706.log.json) |
+| YOLOv5u-n |  P5  | 640  |     Yes     |  Yes   | Yes |          |             |            | [config](./yolov5/yolov5u/yolov5_n_mask-refine_syncbn_fast_8xb16-300e_coco.py)  |                                                                                                                                                                                               [model](<>) \| [log](<>)                                                                                                                                                                                               |
+| YOLOv5-s  |  P5  | 640  |     No      |  Yes   | Yes |   2.7    |    37.7     |    40.2    |             [config](./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) \| [log](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.log.json)                                     |
+| YOLOv5-s  |  P5  | 640  |     Yes     |  Yes   | Yes |   2.7    | 38.0 (+0.3) |            | [config](./mask_refine/yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230304_033134-8e0cd271.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230304_033134.log.json) |
+| YOLOv5u-s |  P5  | 640  |     Yes     |  Yes   | Yes |          |             |            | [config](./yolov5/yolov5u/yolov5_s_mask-refine_syncbn_fast_8xb16-300e_coco.py)  |                                                                                                                                                                                               [model](<>) \| [log](<>)                                                                                                                                                                                               |
+| YOLOv5-m  |  P5  | 640  |     No      |  Yes   | Yes |   5.0    |    45.3     |    46.9    |             [config](./yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py)             |                                     [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944.log.json)                                     |
+| YOLOv5-m  |  P5  | 640  |     Yes     |  Yes   | Yes |   5.0    |    45.3     |            | [config](./mask_refine/yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_153946-44e96155.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_153946.log.json) |
+| YOLOv5u-m |  P5  | 640  |     Yes     |  Yes   | Yes |          |             |            | [config](./yolov5/yolov5u/yolov5_m_mask-refine_syncbn_fast_8xb16-300e_coco.py)  |                                                                                                                                                                                               [model](<>) \| [log](<>)                                                                                                                                                                                               |
+| YOLOv5-l  |  P5  | 640  |     No      |  Yes   | Yes |   8.1    |    48.8     |    49.9    |             [config](./yolov5_l-v61_syncbn_fast_8xb16-300e_coco.py)             |                                     [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco/yolov5_l-v61_syncbn_fast_8xb16-300e_coco_20220917_031007-096ef0eb.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco/yolov5_l-v61_syncbn_fast_8xb16-300e_coco_20220917_031007.log.json)                                     |
+| YOLOv5-l  |  P5  | 640  |     Yes     |  Yes   | Yes |   8.1    | 49.3 (+0.5) |            | [config](./mask_refine/yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_154301-2c1d912a.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_154301.log.json) |
+| YOLOv5u-l |  P5  | 640  |     Yes     |  Yes   | Yes |          |             |            | [config](./yolov5/yolov5u/yolov5_l_mask-refine_syncbn_fast_8xb16-300e_coco.py)  |                                                                                                                                                                                               [model](<>) \| [log](<>)                                                                                                                                                                                               |
+| YOLOv5-x  |  P5  | 640  |     No      |  Yes   | Yes |   12.2   |    50.2     |            |             [config](./yolov5_x-v61_syncbn_fast_8xb16-300e_coco.py)             |                                     [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_x-v61_syncbn_fast_8xb16-300e_coco/yolov5_x-v61_syncbn_fast_8xb16-300e_coco_20230305_152943-00776a4b.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_x-v61_syncbn_fast_8xb16-300e_coco/yolov5_x-v61_syncbn_fast_8xb16-300e_coco_20230305_152943.log.json)                                     |
+| YOLOv5-x  |  P5  | 640  |     Yes     |  Yes   | Yes |   12.2   | 50.9 (+0.7) |            | [config](./mask_refine/yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_154321-07edeb62.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/mask_refine/yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco/yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco_20230305_154321.log.json) |
+| YOLOv5u-x |  P5  | 640  |     Yes     |  Yes   | Yes |          |             |            | [config](./yolov5/yolov5u/yolov5_x_mask-refine_syncbn_fast_8xb16-300e_coco.py)  |                                                                                                                                                                                               [model](<>) \| [log](<>)                                                                                                                                                                                               |
+| YOLOv5-n  |  P6  | 1280 |     No      |  Yes   | Yes |   5.8    |    35.9     |            |           [config](./yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco.py)            |                               [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_224705-d493c5f3.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_224705.log.json)                               |
+| YOLOv5-s  |  P6  | 1280 |     No      |  Yes   | Yes |   10.5   |    44.4     |            |           [config](./yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py)            |                               [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_215044-58865c19.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_215044.log.json)                               |
+| YOLOv5-m  |  P6  | 1280 |     No      |  Yes   | Yes |   19.1   |    51.3     |            |           [config](./yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco.py)            |                               [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_230453-49564d58.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_230453.log.json)                               |
+| YOLOv5-l  |  P6  | 1280 |     No      |  Yes   | Yes |   30.5   |    53.7     |            |           [config](./yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco.py)            |                               [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_234308-7a2ba6bf.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco/yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco_20221027_234308.log.json)                               |

 **Note**:

@ -46,15 +51,16 @@ YOLOv5-l-P6 model structure
 5. The performance is unstable and may fluctuate by about 0.4 mAP and the highest performance weight in `COCO` training in `YOLOv5` may not be the last epoch.
 6. `TTA` means that Test Time Augmentation. It's perform 3 multi-scaling transformations on the image, followed by 2 flipping transformations (flipping and not flipping). You only need to specify `--tta` when testing to enable.  see [TTA](https://github.com/open-mmlab/mmyolo/blob/dev/docs/en/common_usage/tta.md) for details.
 7. The performance of `Mask Refine` training is for the weight performance officially released by YOLOv5. `Mask Refine` means refining bbox by mask while loading annotations and transforming after `YOLOv5RandomAffine`, `Copy Paste` means using `YOLOv5CopyPaste`.
+8. `YOLOv5u` models use the same loss functions and split Detect head as `YOLOv8` models for improved performance, but only requires 300 epochs.

 ### VOC

-| Backbone | size | Batchsize | AMP | Mem (GB) | box AP(COCO metric) |                                                     Config                                                     |                                                                                                                                                 Download                                                                                                                                                 |
-| :------: | :--: | :-------: | :-: | :------: | :-----------------: | :------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| YOLOv5-n | 512  |    64     | Yes |   3.5    |        51.2         | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov5/voc/yolov5_n-v61_fast_1xb64-50e_voc.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_fast_1xb64-50e_voc/yolov5_n-v61_fast_1xb64-50e_voc_20221017_234254-f1493430.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_fast_1xb64-50e_voc/yolov5_n-v61_fast_1xb64-50e_voc_20221017_234254.log.json) |
-| YOLOv5-s | 512  |    64     | Yes |   6.5    |        62.7         | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_fast_1xb64-50e_voc/yolov5_s-v61_fast_1xb64-50e_voc_20221017_234156-0009b33e.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_fast_1xb64-50e_voc/yolov5_s-v61_fast_1xb64-50e_voc_20221017_234156.log.json) |
-| YOLOv5-m | 512  |    64     | Yes |   12.0   |        70.1         | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov5/voc/yolov5_m-v61_fast_1xb64-50e_voc.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_fast_1xb64-50e_voc/yolov5_m-v61_fast_1xb64-50e_voc_20221017_114138-815c143a.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_fast_1xb64-50e_voc/yolov5_m-v61_fast_1xb64-50e_voc_20221017_114138.log.json) |
-| YOLOv5-l | 512  |    32     | Yes |   10.0   |        73.1         | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov5/voc/yolov5_l-v61_fast_1xb32-50e_voc.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_fast_1xb32-50e_voc/yolov5_l-v61_fast_1xb32-50e_voc_20221017_045500-edc7e0d8.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_fast_1xb32-50e_voc/yolov5_l-v61_fast_1xb32-50e_voc_20221017_045500.log.json) |
+| Backbone | size | Batchsize | AMP | Mem (GB) | box AP(COCO metric) |                          Config                           |                                                                                                                                                 Download                                                                                                                                                 |
+| :------: | :--: | :-------: | :-: | :------: | :-----------------: | :-------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| YOLOv5-n | 512  |    64     | Yes |   3.5    |        51.2         | [config](./yolov5/voc/yolov5_n-v61_fast_1xb64-50e_voc.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_fast_1xb64-50e_voc/yolov5_n-v61_fast_1xb64-50e_voc_20221017_234254-f1493430.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_fast_1xb64-50e_voc/yolov5_n-v61_fast_1xb64-50e_voc_20221017_234254.log.json) |
+| YOLOv5-s | 512  |    64     | Yes |   6.5    |        62.7         | [config](./yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_fast_1xb64-50e_voc/yolov5_s-v61_fast_1xb64-50e_voc_20221017_234156-0009b33e.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_fast_1xb64-50e_voc/yolov5_s-v61_fast_1xb64-50e_voc_20221017_234156.log.json) |
+| YOLOv5-m | 512  |    64     | Yes |   12.0   |        70.1         | [config](./yolov5/voc/yolov5_m-v61_fast_1xb64-50e_voc.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_fast_1xb64-50e_voc/yolov5_m-v61_fast_1xb64-50e_voc_20221017_114138-815c143a.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_fast_1xb64-50e_voc/yolov5_m-v61_fast_1xb64-50e_voc_20221017_114138.log.json) |
+| YOLOv5-l | 512  |    32     | Yes |   10.0   |        73.1         | [config](./yolov5/voc/yolov5_l-v61_fast_1xb32-50e_voc.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_fast_1xb32-50e_voc/yolov5_l-v61_fast_1xb32-50e_voc_20221017_045500-edc7e0d8.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_fast_1xb32-50e_voc/yolov5_l-v61_fast_1xb32-50e_voc_20221017_045500.log.json) |

 **Note**:

@ -68,10 +74,10 @@ YOLOv5-l-P6 model structure

 Since the `iscrowd` annotation of the COCO dataset is not equivalent to `ignore`, we use the CrowdHuman dataset to verify that the YOLOv5 ignore logic is correct.

-| Backbone | size | SyncBN | AMP | Mem (GB) | ignore_iof_thr | box AP50(CrowDHuman Metric) |  MR  |  JI   |                                                             Config                                                              | Download |
-| :------: | :--: | :----: | :-: | :------: | :------------: | :-------------------------: | :--: | :---: | :-----------------------------------------------------------------------------------------------------------------------------: | :------: |
-| YOLOv5-s | 640  |  Yes   | Yes |   2.6    |       -1       |            85.79            | 48.7 | 75.33 |  [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov5/crowdhuman/yolov5_s-v61_fast_8xb16-300e_crowdhuman.py)  |          |
-| YOLOv5-s | 640  |  Yes   | Yes |   2.6    |      0.5       |            86.17            | 48.8 | 75.87 | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov5/crowdhuman/yolov5_s-v61_8xb16-300e_ignore_crowdhuman.py) |          |
+| Backbone | size | SyncBN | AMP | Mem (GB) | ignore_iof_thr | box AP50(CrowDHuman Metric) |  MR  |  JI   |                                   Config                                   | Download |
+| :------: | :--: | :----: | :-: | :------: | :------------: | :-------------------------: | :--: | :---: | :------------------------------------------------------------------------: | :------: |
+| YOLOv5-s | 640  |  Yes   | Yes |   2.6    |       -1       |            85.79            | 48.7 | 75.33 |  [config](./yolov5/crowdhuman/yolov5_s-v61_fast_8xb16-300e_crowdhuman.py)  |          |
+| YOLOv5-s | 640  |  Yes   | Yes |   2.6    |      0.5       |            86.17            | 48.8 | 75.87 | [config](./yolov5/crowdhuman/yolov5_s-v61_8xb16-300e_ignore_crowdhuman.py) |          |

 **Note**:

--- a/configs/yolov5/yolov5u/yolov5u_l_mask-refine_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5u/yolov5u_l_mask-refine_syncbn_fast_8xb16-300e_coco.py
@ -0,0 +1,59 @@
+_base_ = './yolov5u_m_mask-refine_syncbn_fast_8xb16-300e_coco.py'
+
+# This config will refine bbox by mask while loading annotations and
+# transforming after `YOLOv5RandomAffine`
+
+# ========================modified parameters======================
+deepen_factor = 1.00
+widen_factor = 1.00
+
+mixup_prob = 0.15
+copypaste_prob = 0.3
+
+# =======================Unmodified in most cases==================
+img_scale = _base_.img_scale
+pre_transform = _base_.pre_transform
+last_transform = _base_.last_transform
+affine_scale = _base_.affine_scale
+
+model = dict(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
+
+mosaic_affine_transform = [
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        pad_val=114.0,
+        pre_transform=pre_transform),
+    dict(type='YOLOv5CopyPaste', prob=copypaste_prob),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        max_aspect_ratio=100.,
+        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
+        # img_scale is (width, height)
+        border=(-img_scale[0] // 2, -img_scale[1] // 2),
+        border_val=(114, 114, 114),
+        min_area_ratio=_base_.min_area_ratio,
+        use_mask_refine=_base_.use_mask2refine)
+]
+
+train_pipeline = [
+    *pre_transform, *mosaic_affine_transform,
+    dict(
+        type='YOLOv5MixUp',
+        prob=mixup_prob,
+        pre_transform=[*pre_transform, *mosaic_affine_transform]),
+    *last_transform
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
--- a/configs/yolov5/yolov5u/yolov5u_l_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5u/yolov5u_l_syncbn_fast_8xb16-300e_coco.py
@ -0,0 +1,18 @@
+_base_ = './yolov5u_s_syncbn_fast_8xb16-300e_coco.py'
+
+# ========================modified parameters======================
+# TODO: Update the training hyperparameters
+deepen_factor = 1.0
+widen_factor = 1.0
+
+# =======================Unmodified in most cases==================
+model = dict(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
--- a/configs/yolov5/yolov5u/yolov5u_m_mask-refine_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5u/yolov5u_m_mask-refine_syncbn_fast_8xb16-300e_coco.py
@ -0,0 +1,79 @@
+_base_ = './yolov5u_s_mask-refine_syncbn_fast_8xb16-300e_coco.py'
+
+# This config will refine bbox by mask while loading annotations and
+# transforming after `YOLOv5RandomAffine`
+
+# ========================modified parameters======================
+deepen_factor = 0.67
+widen_factor = 0.75
+
+affine_scale = 0.9
+mixup_prob = 0.1
+copypaste_prob = 0.1
+
+# =======================Unmodified in most cases==================
+img_scale = _base_.img_scale
+pre_transform = _base_.pre_transform
+last_transform = _base_.last_transform
+
+model = dict(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
+
+mosaic_affine_transform = [
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        pad_val=114.0,
+        pre_transform=pre_transform),
+    dict(type='YOLOv5CopyPaste', prob=copypaste_prob),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        max_aspect_ratio=100.,
+        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
+        # img_scale is (width, height)
+        border=(-img_scale[0] // 2, -img_scale[1] // 2),
+        border_val=(114, 114, 114),
+        min_area_ratio=_base_.min_area_ratio,
+        use_mask_refine=_base_.use_mask2refine)
+]
+
+train_pipeline = [
+    *pre_transform, *mosaic_affine_transform,
+    dict(
+        type='YOLOv5MixUp',
+        prob=mixup_prob,
+        pre_transform=[*pre_transform, *mosaic_affine_transform]),
+    *last_transform
+]
+
+train_pipeline_stage2 = [
+    *pre_transform,
+    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
+    dict(
+        type='LetterResize',
+        scale=img_scale,
+        allow_scale_up=True,
+        pad_val=dict(img=114.0)),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
+        max_aspect_ratio=_base_.max_aspect_ratio,
+        border_val=(114, 114, 114),
+        min_area_ratio=_base_.min_area_ratio,
+        use_mask_refine=_base_.use_mask2refine), *last_transform
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
+_base_.custom_hooks[1].switch_pipeline = train_pipeline_stage2
--- a/configs/yolov5/yolov5u/yolov5u_m_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5u/yolov5u_m_syncbn_fast_8xb16-300e_coco.py
@ -0,0 +1,18 @@
+_base_ = './yolov5u_s_syncbn_fast_8xb16-300e_coco.py'
+
+# ========================modified parameters======================
+# TODO: Update the training hyperparameters
+deepen_factor = 0.67
+widen_factor = 0.75
+
+# =======================Unmodified in most cases==================
+model = dict(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
--- a/configs/yolov5/yolov5u/yolov5u_n_mask-refine_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5u/yolov5u_n_mask-refine_syncbn_fast_8xb16-300e_coco.py
@ -0,0 +1,20 @@
+_base_ = './yolov5u_s_mask-refine_syncbn_fast_8xb16-300e_coco.py'
+
+# This config will refine bbox by mask while loading annotations and
+# transforming after `YOLOv5RandomAffine`
+
+# ========================modified parameters======================
+deepen_factor = 0.33
+widen_factor = 0.25
+
+# ===============================Unmodified in most cases====================
+model = dict(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
--- a/configs/yolov5/yolov5u/yolov5u_n_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5u/yolov5u_n_syncbn_fast_8xb16-300e_coco.py
@ -0,0 +1,17 @@
+_base_ = './yolov5u_s_syncbn_fast_8xb16-300e_coco.py'
+
+# ========================modified parameters======================
+deepen_factor = 0.33
+widen_factor = 0.25
+
+# =======================Unmodified in most cases==================
+model = dict(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
--- a/configs/yolov5/yolov5u/yolov5u_s_mask-refine_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5u/yolov5u_s_mask-refine_syncbn_fast_8xb16-300e_coco.py
@ -0,0 +1,80 @@
+_base_ = './yolov5u_s_syncbn_fast_8xb16-300e_coco.py'
+
+# This config will refine bbox by mask while loading annotations and
+# transforming after `YOLOv5RandomAffine`
+
+# ========================modified parameters======================
+use_mask2refine = True
+min_area_ratio = 0.01  # YOLOv5RandomAffine
+
+# ===============================Unmodified in most cases====================
+pre_transform = [
+    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        mask2bbox=use_mask2refine)
+]
+
+last_transform = [
+    # Delete gt_masks to avoid more computation
+    dict(type='RemoveDataElement', keys=['gt_masks']),
+    dict(
+        type='mmdet.Albu',
+        transforms=_base_.albu_train_transforms,
+        bbox_params=dict(
+            type='BboxParams',
+            format='pascal_voc',
+            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
+        keymap={
+            'img': 'image',
+            'gt_bboxes': 'bboxes'
+        }),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                   'flip_direction'))
+]
+
+train_pipeline = [
+    *pre_transform,
+    dict(
+        type='Mosaic',
+        img_scale=_base_.img_scale,
+        pad_val=114.0,
+        pre_transform=pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        # img_scale is (width, height)
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114),
+        min_area_ratio=min_area_ratio,
+        use_mask_refine=use_mask2refine),
+    *last_transform
+]
+
+train_pipeline_stage2 = [
+    *pre_transform,
+    dict(type='YOLOv5KeepRatioResize', scale=_base_.img_scale),
+    dict(
+        type='LetterResize',
+        scale=_base_.img_scale,
+        allow_scale_up=True,
+        pad_val=dict(img=114.0)),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        max_aspect_ratio=_base_.max_aspect_ratio,
+        border_val=(114, 114, 114)), *last_transform
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
+_base_.custom_hooks[1].switch_pipeline = train_pipeline_stage2
--- a/configs/yolov5/yolov5u/yolov5u_s_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5u/yolov5u_s_syncbn_fast_8xb16-300e_coco.py
@ -0,0 +1,326 @@
+_base_ = ['../../_base_/default_runtime.py', '../../_base_/det_p5_tta.py']
+
+# ========================Frequently modified parameters======================
+# -----data related-----
+data_root = 'data/coco/'  # Root path of data
+# Path of train annotation file
+train_ann_file = 'annotations/instances_train2017.json'
+train_data_prefix = 'train2017/'  # Prefix of train image path
+# Path of val annotation file
+val_ann_file = 'annotations/instances_val2017.json'
+val_data_prefix = 'val2017/'  # Prefix of val image path
+
+num_classes = 80  # Number of classes for classification
+# Batch size of a single GPU during training
+train_batch_size_per_gpu = 16
+# Worker to pre-fetch data for each single GPU during training
+train_num_workers = 8
+# persistent_workers must be False if num_workers is 0
+persistent_workers = True
+
+# -----train val related-----
+# Base learning rate for optim_wrapper. Corresponding to 8xb16=128 bs
+base_lr = 0.01
+max_epochs = 300  # Maximum training epochs
+# Disable mosaic augmentation for final 10 epochs (stage 2)
+close_mosaic_epochs = 10
+
+model_test_cfg = dict(
+    # The config of multi-label for multi-class prediction.
+    multi_label=True,
+    # The number of boxes before NMS
+    nms_pre=30000,
+    score_thr=0.001,  # Threshold to filter out boxes.
+    nms=dict(type='nms', iou_threshold=0.7),  # NMS type and threshold
+    max_per_img=300)  # Max number of detections of each image
+
+# ========================Possible modified parameters========================
+# -----data related-----
+img_scale = (640, 640)  # width, height
+# Dataset type, this will be used to define the dataset
+dataset_type = 'YOLOv5CocoDataset'
+# Batch size of a single GPU during validation
+val_batch_size_per_gpu = 1
+# Worker to pre-fetch data for each single GPU during validation
+val_num_workers = 2
+
+# Config of batch shapes. Only on val.
+# It means not used if batch_shapes_cfg is None.
+batch_shapes_cfg = dict(
+    type='BatchShapePolicy',
+    batch_size=val_batch_size_per_gpu,
+    img_size=img_scale[0],
+    # The image scale of padding should be divided by pad_size_divisor
+    size_divisor=32,
+    # Additional paddings for pixel scale
+    extra_pad_ratio=0.5)
+
+# -----model related-----
+# The scaling factor that controls the depth of the network structure
+deepen_factor = 0.33
+# The scaling factor that controls the width of the network structure
+widen_factor = 0.5
+# Strides of multi-scale prior box
+strides = [8, 16, 32]
+num_det_layers = 3  # The number of model output scales
+norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)  # Normalization config
+
+# -----train val related-----
+tal_topk = 10  # Number of bbox selected in each level
+tal_alpha = 0.5  # A Hyper-parameter related to alignment_metrics
+tal_beta = 6.0  # A Hyper-parameter related to alignment_metrics
+
+affine_scale = 0.5  # YOLOv5RandomAffine scaling ratio
+# YOLOv5RandomAffine aspect ratio of width and height thres to filter bboxes
+max_aspect_ratio = 100
+# TODO: Automatically scale loss_weight based on number of detection layers
+loss_cls_weight = 0.5
+loss_bbox_weight = 7.5
+# Since the dfloss is implemented differently in the official
+# and mmdet, we're going to divide loss_weight by 4.
+loss_dfl_weight = 1.5 / 4
+lr_factor = 0.01  # Learning rate scaling factor
+weight_decay = 0.001
+# Save model checkpoint and validation intervals
+save_checkpoint_intervals = 10
+# The maximum checkpoints to keep.
+max_keep_ckpts = 3
+# Single-scale training is recommended to
+# be turned on, which can speed up training.
+env_cfg = dict(cudnn_benchmark=True)
+
+# ===============================Unmodified in most cases====================
+model = dict(
+    type='YOLODetector',
+    data_preprocessor=dict(
+        type='YOLOv5DetDataPreprocessor',
+        mean=[0., 0., 0.],
+        std=[255., 255., 255.],
+        bgr_to_rgb=True),
+    backbone=dict(
+        type='YOLOv5CSPDarknet',
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        norm_cfg=norm_cfg,
+        act_cfg=dict(type='SiLU', inplace=True)),
+    neck=dict(
+        type='YOLOv5PAFPN',
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        in_channels=[256, 512, 1024],
+        out_channels=[256, 512, 1024],
+        num_csp_blocks=3,
+        norm_cfg=norm_cfg,
+        act_cfg=dict(type='SiLU', inplace=True)),
+    bbox_head=dict(
+        type='YOLOv8Head',
+        head_module=dict(
+            type='YOLOv8HeadModule',
+            num_classes=num_classes,
+            in_channels=[256, 512, 1024],
+            widen_factor=widen_factor,
+            reg_max=16,
+            norm_cfg=norm_cfg,
+            act_cfg=dict(type='SiLU', inplace=True),
+            featmap_strides=strides),
+        prior_generator=dict(
+            type='mmdet.MlvlPointGenerator', offset=0.5, strides=strides),
+        bbox_coder=dict(type='DistancePointBBoxCoder'),
+        # scaled based on number of detection layers
+        loss_cls=dict(
+            type='mmdet.CrossEntropyLoss',
+            use_sigmoid=True,
+            reduction='none',
+            loss_weight=loss_cls_weight),
+        loss_bbox=dict(
+            type='IoULoss',
+            iou_mode='ciou',
+            bbox_format='xyxy',
+            reduction='sum',
+            loss_weight=loss_bbox_weight,
+            return_iou=False),
+        loss_dfl=dict(
+            type='mmdet.DistributionFocalLoss',
+            reduction='mean',
+            loss_weight=loss_dfl_weight)),
+    train_cfg=dict(
+        assigner=dict(
+            type='BatchTaskAlignedAssigner',
+            num_classes=num_classes,
+            use_ciou=True,
+            topk=tal_topk,
+            alpha=tal_alpha,
+            beta=tal_beta,
+            eps=1e-9)),
+    test_cfg=model_test_cfg)
+
+albu_train_transforms = [
+    dict(type='Blur', p=0.01),
+    dict(type='MedianBlur', p=0.01),
+    dict(type='ToGray', p=0.01),
+    dict(type='CLAHE', p=0.01)
+]
+
+pre_transform = [
+    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
+    dict(type='LoadAnnotations', with_bbox=True)
+]
+
+last_transform = [
+    dict(
+        type='mmdet.Albu',
+        transforms=albu_train_transforms,
+        bbox_params=dict(
+            type='BboxParams',
+            format='pascal_voc',
+            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
+        keymap={
+            'img': 'image',
+            'gt_bboxes': 'bboxes'
+        }),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                   'flip_direction'))
+]
+
+train_pipeline = [
+    *pre_transform,
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        pad_val=114.0,
+        pre_transform=pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
+        max_aspect_ratio=max_aspect_ratio,
+        # img_scale is (width, height)
+        border=(-img_scale[0] // 2, -img_scale[1] // 2),
+        border_val=(114, 114, 114)),
+    *last_transform
+]
+
+train_pipeline_stage2 = [
+    *pre_transform,
+    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
+    dict(
+        type='LetterResize',
+        scale=img_scale,
+        allow_scale_up=True,
+        pad_val=dict(img=114.0)),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
+        max_aspect_ratio=max_aspect_ratio,
+        border_val=(114, 114, 114)), *last_transform
+]
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    persistent_workers=persistent_workers,
+    pin_memory=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    collate_fn=dict(type='yolov5_collate'),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=train_ann_file,
+        data_prefix=dict(img=train_data_prefix),
+        filter_cfg=dict(filter_empty_gt=False, min_size=32),
+        pipeline=train_pipeline))
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
+    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
+    dict(
+        type='LetterResize',
+        scale=img_scale,
+        allow_scale_up=False,
+        pad_val=dict(img=114)),
+    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'pad_param'))
+]
+
+val_dataloader = dict(
+    batch_size=val_batch_size_per_gpu,
+    num_workers=val_num_workers,
+    persistent_workers=persistent_workers,
+    pin_memory=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        test_mode=True,
+        data_prefix=dict(img=val_data_prefix),
+        ann_file=val_ann_file,
+        pipeline=test_pipeline,
+        batch_shapes_cfg=batch_shapes_cfg))
+
+test_dataloader = val_dataloader
+
+param_scheduler = None
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(
+        type='SGD',
+        lr=base_lr,
+        momentum=0.937,
+        weight_decay=weight_decay,
+        nesterov=True,
+        batch_size_per_gpu=train_batch_size_per_gpu),
+    constructor='YOLOv5OptimizerConstructor')
+
+default_hooks = dict(
+    param_scheduler=dict(
+        type='YOLOv5ParamSchedulerHook',
+        scheduler_type='linear',
+        lr_factor=lr_factor,
+        max_epochs=max_epochs,
+        warmup_epochs=3.0,
+        warmup_momentum=0.8,
+        warmup_bias_lr=0.1),
+    checkpoint=dict(
+        type='CheckpointHook',
+        interval=save_checkpoint_intervals,
+        save_best='auto',
+        max_keep_ckpts=max_keep_ckpts))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0001,
+        update_buffers=True,
+        strict_load=False,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - close_mosaic_epochs,
+        switch_pipeline=train_pipeline_stage2)
+]
+
+val_evaluator = dict(
+    type='mmdet.CocoMetric',
+    proposal_nums=(100, 1, 10),
+    ann_file=data_root + val_ann_file,
+    metric='bbox')
+test_evaluator = val_evaluator
+
+train_cfg = dict(
+    type='EpochBasedTrainLoop',
+    max_epochs=max_epochs,
+    val_interval=save_checkpoint_intervals)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
--- a/configs/yolov5/yolov5u/yolov5u_x_mask-refine_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5u/yolov5u_x_mask-refine_syncbn_fast_8xb16-300e_coco.py
@ -0,0 +1,17 @@
+_base_ = './yolov5u_l_mask-refine_syncbn_fast_8xb16-300e_coco.py'
+
+# ========================modified parameters======================
+deepen_factor = 1.33
+widen_factor = 1.25
+
+# =======================Unmodified in most cases==================
+model = dict(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
--- a/configs/yolov5/yolov5u/yolov5u_x_syncbn_fast_8xb16-300e_coco.py
+++ b/configs/yolov5/yolov5u/yolov5u_x_syncbn_fast_8xb16-300e_coco.py
@ -0,0 +1,18 @@
+_base_ = './yolov5u_l_syncbn_fast_8xb16-300e_coco.py'
+
+# ========================modified parameters======================
+# TODO: Update the training hyperparameters
+deepen_factor = 1.33
+widen_factor = 1.25
+
+# =======================Unmodified in most cases==================
+model = dict(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
--- a/configs/yolov6/README.md
+++ b/configs/yolov6/README.md
@ -26,13 +26,13 @@ YOLOv6-l model structure

 ### COCO

-| 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_8xb32-300e_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) |
+| 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_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_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_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_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_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/configs/yolov7/README.md
+++ b/configs/yolov7/README.md
@ -21,13 +21,13 @@ YOLOv7-l-P5 model structure

 ### COCO

-|  Backbone   | Arch | Size | SyncBN | AMP | Mem (GB) | Box AP |                                                       Config                                                       |                                                                                                                                                                 Download                                                                                                                                                                 |
-| :---------: | :--: | :--: | :----: | :-: | :------: | :----: | :----------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| YOLOv7-tiny |  P5  | 640  |  Yes   | Yes |   2.7    |  37.5  | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov7/yolov7_tiny_syncbn_fast_8x16b-300e_coco.py) | [model](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) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_tiny_syncbn_fast_8x16b-300e_coco/yolov7_tiny_syncbn_fast_8x16b-300e_coco_20221126_102719.log.json) |
-|  YOLOv7-l   |  P5  | 640  |  Yes   | Yes |   10.3   |  50.9  |  [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco.py)   |       [model](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco/yolov7_l_syncbn_fast_8x16b-300e_coco_20221123_023601-8113c0eb.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco/yolov7_l_syncbn_fast_8x16b-300e_coco_20221123_023601.log.json)       |
-|  YOLOv7-x   |  P5  | 640  |  Yes   | Yes |   13.7   |  52.8  |  [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov7/yolov7_x_syncbn_fast_8x16b-300e_coco.py)   |       [model](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_x_syncbn_fast_8x16b-300e_coco/yolov7_x_syncbn_fast_8x16b-300e_coco_20221124_215331-ef949a68.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_x_syncbn_fast_8x16b-300e_coco/yolov7_x_syncbn_fast_8x16b-300e_coco_20221124_215331.log.json)       |
-|  YOLOv7-w   |  P6  | 1280 |  Yes   | Yes |   27.0   |  54.1  | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov7/yolov7_w-p6_syncbn_fast_8x16b-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_w-p6_syncbn_fast_8x16b-300e_coco/yolov7_w-p6_syncbn_fast_8x16b-300e_coco_20221123_053031-a68ef9d2.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_w-p6_syncbn_fast_8x16b-300e_coco/yolov7_w-p6_syncbn_fast_8x16b-300e_coco_20221123_053031.log.json) |
-|  YOLOv7-e   |  P6  | 1280 |  Yes   | Yes |   42.5   |  55.1  | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov7/yolov7_e-p6_syncbn_fast_8x16b-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_e-p6_syncbn_fast_8x16b-300e_coco/yolov7_e-p6_syncbn_fast_8x16b-300e_coco_20221126_102636-34425033.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_e-p6_syncbn_fast_8x16b-300e_coco/yolov7_e-p6_syncbn_fast_8x16b-300e_coco_20221126_102636.log.json) |
+|  Backbone   | Arch | Size | SyncBN | AMP | Mem (GB) | Box AP |                         Config                         |                                                                                                                                                                 Download                                                                                                                                                                 |
+| :---------: | :--: | :--: | :----: | :-: | :------: | :----: | :----------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| YOLOv7-tiny |  P5  | 640  |  Yes   | Yes |   2.7    |  37.5  | [config](./yolov7_tiny_syncbn_fast_8x16b-300e_coco.py) | [model](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) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_tiny_syncbn_fast_8x16b-300e_coco/yolov7_tiny_syncbn_fast_8x16b-300e_coco_20221126_102719.log.json) |
+|  YOLOv7-l   |  P5  | 640  |  Yes   | Yes |   10.3   |  50.9  |  [config](./yolov7_l_syncbn_fast_8x16b-300e_coco.py)   |       [model](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco/yolov7_l_syncbn_fast_8x16b-300e_coco_20221123_023601-8113c0eb.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco/yolov7_l_syncbn_fast_8x16b-300e_coco_20221123_023601.log.json)       |
+|  YOLOv7-x   |  P5  | 640  |  Yes   | Yes |   13.7   |  52.8  |  [config](./yolov7_x_syncbn_fast_8x16b-300e_coco.py)   |       [model](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_x_syncbn_fast_8x16b-300e_coco/yolov7_x_syncbn_fast_8x16b-300e_coco_20221124_215331-ef949a68.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_x_syncbn_fast_8x16b-300e_coco/yolov7_x_syncbn_fast_8x16b-300e_coco_20221124_215331.log.json)       |
+|  YOLOv7-w   |  P6  | 1280 |  Yes   | Yes |   27.0   |  54.1  | [config](./yolov7_w-p6_syncbn_fast_8x16b-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_w-p6_syncbn_fast_8x16b-300e_coco/yolov7_w-p6_syncbn_fast_8x16b-300e_coco_20221123_053031-a68ef9d2.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_w-p6_syncbn_fast_8x16b-300e_coco/yolov7_w-p6_syncbn_fast_8x16b-300e_coco_20221123_053031.log.json) |
+|  YOLOv7-e   |  P6  | 1280 |  Yes   | Yes |   42.5   |  55.1  | [config](./yolov7_e-p6_syncbn_fast_8x16b-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_e-p6_syncbn_fast_8x16b-300e_coco/yolov7_e-p6_syncbn_fast_8x16b-300e_coco_20221126_102636-34425033.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_e-p6_syncbn_fast_8x16b-300e_coco/yolov7_e-p6_syncbn_fast_8x16b-300e_coco_20221126_102636.log.json) |

 **Note**:
 In the official YOLOv7 code, the `random_perspective` data augmentation in COCO object detection task training uses mask annotation information, which leads to higher performance. Object detection should not use mask annotation, so only box annotation information is used in `MMYOLO`. We will use the mask annotation information in the instance segmentation task.
--- a/configs/yolov8/README.md
+++ b/configs/yolov8/README.md
@ -20,18 +20,18 @@ YOLOv8-P5 model structure

 ### COCO

-| Backbone | Arch | size | Mask Refine | SyncBN | AMP | Mem (GB) |   box AP    | TTA box AP |                                 Config                                  |                                                                                                                                                                                   Download                                                                                                                                                                                   |
-| :------: | :--: | :--: | :---------: | :----: | :-: | :------: | :---------: | :--------: | :---------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| YOLOv8-n |  P5  | 640  |     No      |  Yes   | Yes |   2.8    |    37.2     |            |       [config](../yolov8/yolov8_n_syncbn_fast_8xb16-500e_coco.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_n_syncbn_fast_8xb16-500e_coco/yolov8_n_syncbn_fast_8xb16-500e_coco_20230114_131804-88c11cdb.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_n_syncbn_fast_8xb16-500e_coco/yolov8_n_syncbn_fast_8xb16-500e_coco_20230114_131804.log.json)                         |
-| YOLOv8-n |  P5  | 640  |     Yes     |  Yes   | Yes |   2.5    | 37.4 (+0.2) |    39.9    | [config](../yolov8/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_101206-b975b1cd.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_101206.log.json) |
-| YOLOv8-s |  P5  | 640  |     No      |  Yes   | Yes |   4.0    |    44.2     |            |       [config](../yolov8/yolov8_s_syncbn_fast_8xb16-500e_coco.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_s_syncbn_fast_8xb16-500e_coco/yolov8_s_syncbn_fast_8xb16-500e_coco_20230117_180101-5aa5f0f1.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_s_syncbn_fast_8xb16-500e_coco/yolov8_s_syncbn_fast_8xb16-500e_coco_20230117_180101.log.json)                         |
-| YOLOv8-s |  P5  | 640  |     Yes     |  Yes   | Yes |   4.0    | 45.1 (+0.9) |    46.8    | [config](../yolov8/yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_095938-ce3c1b3f.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_095938.log.json) |
-| YOLOv8-m |  P5  | 640  |     No      |  Yes   | Yes |   7.2    |    49.8     |            |       [config](../yolov8/yolov8_m_syncbn_fast_8xb16-500e_coco.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_m_syncbn_fast_8xb16-500e_coco/yolov8_m_syncbn_fast_8xb16-500e_coco_20230115_192200-c22e560a.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_m_syncbn_fast_8xb16-500e_coco/yolov8_m_syncbn_fast_8xb16-500e_coco_20230115_192200.log.json)                         |
-| YOLOv8-m |  P5  | 640  |     Yes     |  Yes   | Yes |   7.0    | 50.6 (+0.8) |    52.3    | [config](../yolov8/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_223400-f40abfcd.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_223400.log.json) |
-| YOLOv8-l |  P5  | 640  |     No      |  Yes   | Yes |   9.8    |    52.1     |            |       [config](../yolov8/yolov8_l_syncbn_fast_8xb16-500e_coco.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_l_syncbn_fast_8xb16-500e_coco/yolov8_l_syncbn_fast_8xb16-500e_coco_20230217_182526-189611b6.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_l_syncbn_fast_8xb16-500e_coco/yolov8_l_syncbn_fast_8xb16-500e_coco_20230217_182526.log.json)                         |
-| YOLOv8-l |  P5  | 640  |     Yes     |  Yes   | Yes |   9.1    | 53.0 (+0.9) |    54.4    | [config](../yolov8/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco_20230217_120100-5881dec4.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco_20230217_120100.log.json) |
-| YOLOv8-x |  P5  | 640  |     No      |  Yes   | Yes |   12.2   |    52.7     |            |       [config](../yolov8/yolov8_x_syncbn_fast_8xb16-500e_coco.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_x_syncbn_fast_8xb16-500e_coco/yolov8_x_syncbn_fast_8xb16-500e_coco_20230218_023338-5674673c.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_x_syncbn_fast_8xb16-500e_coco/yolov8_x_syncbn_fast_8xb16-500e_coco_20230218_023338.log.json)                         |
-| YOLOv8-x |  P5  | 640  |     Yes     |  Yes   | Yes |   12.4   | 54.0 (+1.3) |    55.0    | [config](../yolov8/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco_20230217_120411-079ca8d1.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco_20230217_120411.log.json) |
+| Backbone | Arch | size | Mask Refine | SyncBN | AMP | Mem (GB) |   box AP    | TTA box AP |                             Config                              |                                                                                                                                                                                   Download                                                                                                                                                                                   |
+| :------: | :--: | :--: | :---------: | :----: | :-: | :------: | :---------: | :--------: | :-------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| YOLOv8-n |  P5  | 640  |     No      |  Yes   | Yes |   2.8    |    37.2     |            |       [config](./yolov8_n_syncbn_fast_8xb16-500e_coco.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_n_syncbn_fast_8xb16-500e_coco/yolov8_n_syncbn_fast_8xb16-500e_coco_20230114_131804-88c11cdb.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_n_syncbn_fast_8xb16-500e_coco/yolov8_n_syncbn_fast_8xb16-500e_coco_20230114_131804.log.json)                         |
+| YOLOv8-n |  P5  | 640  |     Yes     |  Yes   | Yes |   2.5    | 37.4 (+0.2) |    39.9    | [config](./yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_101206-b975b1cd.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_101206.log.json) |
+| YOLOv8-s |  P5  | 640  |     No      |  Yes   | Yes |   4.0    |    44.2     |            |       [config](./yolov8_s_syncbn_fast_8xb16-500e_coco.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_s_syncbn_fast_8xb16-500e_coco/yolov8_s_syncbn_fast_8xb16-500e_coco_20230117_180101-5aa5f0f1.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_s_syncbn_fast_8xb16-500e_coco/yolov8_s_syncbn_fast_8xb16-500e_coco_20230117_180101.log.json)                         |
+| YOLOv8-s |  P5  | 640  |     Yes     |  Yes   | Yes |   4.0    | 45.1 (+0.9) |    46.8    | [config](./yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_095938-ce3c1b3f.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_095938.log.json) |
+| YOLOv8-m |  P5  | 640  |     No      |  Yes   | Yes |   7.2    |    49.8     |            |       [config](./yolov8_m_syncbn_fast_8xb16-500e_coco.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_m_syncbn_fast_8xb16-500e_coco/yolov8_m_syncbn_fast_8xb16-500e_coco_20230115_192200-c22e560a.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_m_syncbn_fast_8xb16-500e_coco/yolov8_m_syncbn_fast_8xb16-500e_coco_20230115_192200.log.json)                         |
+| YOLOv8-m |  P5  | 640  |     Yes     |  Yes   | Yes |   7.0    | 50.6 (+0.8) |    52.3    | [config](./yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_223400-f40abfcd.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco_20230216_223400.log.json) |
+| YOLOv8-l |  P5  | 640  |     No      |  Yes   | Yes |   9.8    |    52.1     |            |       [config](./yolov8_l_syncbn_fast_8xb16-500e_coco.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_l_syncbn_fast_8xb16-500e_coco/yolov8_l_syncbn_fast_8xb16-500e_coco_20230217_182526-189611b6.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_l_syncbn_fast_8xb16-500e_coco/yolov8_l_syncbn_fast_8xb16-500e_coco_20230217_182526.log.json)                         |
+| YOLOv8-l |  P5  | 640  |     Yes     |  Yes   | Yes |   9.1    | 53.0 (+0.9) |    54.4    | [config](./yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco_20230217_120100-5881dec4.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco_20230217_120100.log.json) |
+| YOLOv8-x |  P5  | 640  |     No      |  Yes   | Yes |   12.2   |    52.7     |            |       [config](./yolov8_x_syncbn_fast_8xb16-500e_coco.py)       |                         [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_x_syncbn_fast_8xb16-500e_coco/yolov8_x_syncbn_fast_8xb16-500e_coco_20230218_023338-5674673c.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_x_syncbn_fast_8xb16-500e_coco/yolov8_x_syncbn_fast_8xb16-500e_coco_20230218_023338.log.json)                         |
+| YOLOv8-x |  P5  | 640  |     Yes     |  Yes   | Yes |   12.4   | 54.0 (+1.3) |    55.0    | [config](./yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco_20230217_120411-079ca8d1.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco_20230217_120411.log.json) |

 **Note**

--- a/configs/yolox/README.md
+++ b/configs/yolox/README.md
@ -19,16 +19,16 @@ YOLOX-l model structure

 ## 🥳 🚀 Results and Models

-|  Backbone  | Size | Batch Size | AMP | RTMDet-Hyp | Mem (GB) |   Box AP    |                                                       Config                                                        |                                                                                                                                                                      Download                                                                                                                                                                      |
-| :--------: | :--: | :--------: | :-: | :--------: | :------: | :---------: | :-----------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| YOLOX-tiny | 416  |    8xb8    | No  |     No     |   2.8    |    32.7     |       [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py)       |                                   [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908-0e40a6fc.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908.log.json)                                   |
-| YOLOX-tiny | 416  |   8xb32    | Yes |    Yes     |   4.9    | 34.3 (+1.6) | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637-4c338102.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637.log.json) |
-|  YOLOX-s   | 640  |    8xb8    | Yes |     No     |   2.9    |    40.7     |        [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_s_fast_8xb8-300e_coco.py)         |                               [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb8-300e_coco/yolox_s_fast_8xb8-300e_coco_20230213_142600-2b224d8b.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb8-300e_coco/yolox_s_fast_8xb8-300e_coco_20230213_142600.log.json)                               |
-|  YOLOX-s   | 640  |   8xb32    | Yes |    Yes     |   9.8    | 41.9 (+1.2) |  [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py)   |       [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645-3a8dfbd7.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645.log.json)       |
-|  YOLOX-m   | 640  |    8xb8    | Yes |     No     |   4.9    |    46.9     |        [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_m_fast_8xb8-300e_coco.py)         |                               [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb8-300e_coco/yolox_m_fast_8xb8-300e_coco_20230213_160218-a71a6b25.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb8-300e_coco/yolox_m_fast_8xb8-300e_coco_20230213_160218.log.json)                               |
-|  YOLOX-m   | 640  |   8xb32    | Yes |    Yes     |   17.6   | 47.5 (+0.6) |  [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py)   |       [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328-e657e182.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328.log.json)       |
-|  YOLOX-l   | 640  |    8xb8    | Yes |     No     |   8.0    |    50.1     |        [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_l_fast_8xb8-300e_coco.py)         |                              [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_l_fast__8xb8-300e_coco/yolox_l_fast_8xb8-300e_coco_20230213_160715-c731eb1c.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_l_fast_8xb8-300e_coco/yolox_l_fast_8xb8-300e_coco_20230213_160715.log.json)                               |
-|  YOLOX-x   | 640  |    8xb8    | Yes |     No     |   9.8    |    51.4     |        [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_x_fast_8xb8-300e_coco.py)         |                               [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_x_fast_8xb8-300e_coco/yolox_x_fast_8xb8-300e_coco_20230215_133950-1d509fab.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_x_fast_8xb8-300e_coco/yolox_x_fast_8xb8-300e_coco_20230215_133950.log.json)                               |
+|  Backbone  | Size | Batch Size | AMP | RTMDet-Hyp | Mem (GB) |   Box AP    |                          Config                           |                                                                                                                                                                      Download                                                                                                                                                                      |
+| :--------: | :--: | :--------: | :-: | :--------: | :------: | :---------: | :-------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| YOLOX-tiny | 416  |    8xb8    | No  |     No     |   2.8    |    32.7     |       [config](./yolox_tiny_fast_8xb8-300e_coco.py)       |                                   [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908-0e40a6fc.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908.log.json)                                   |
+| YOLOX-tiny | 416  |   8xb32    | Yes |    Yes     |   4.9    | 34.3 (+1.6) | [config](./yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637-4c338102.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637.log.json) |
+|  YOLOX-s   | 640  |    8xb8    | Yes |     No     |   2.9    |    40.7     |        [config](./yolox_s_fast_8xb8-300e_coco.py)         |                               [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb8-300e_coco/yolox_s_fast_8xb8-300e_coco_20230213_142600-2b224d8b.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb8-300e_coco/yolox_s_fast_8xb8-300e_coco_20230213_142600.log.json)                               |
+|  YOLOX-s   | 640  |   8xb32    | Yes |    Yes     |   9.8    | 41.9 (+1.2) |  [config](./yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py)   |       [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645-3a8dfbd7.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645.log.json)       |
+|  YOLOX-m   | 640  |    8xb8    | Yes |     No     |   4.9    |    46.9     |        [config](./yolox_m_fast_8xb8-300e_coco.py)         |                               [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb8-300e_coco/yolox_m_fast_8xb8-300e_coco_20230213_160218-a71a6b25.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb8-300e_coco/yolox_m_fast_8xb8-300e_coco_20230213_160218.log.json)                               |
+|  YOLOX-m   | 640  |   8xb32    | Yes |    Yes     |   17.6   | 47.5 (+0.6) |  [config](./yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py)   |       [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328-e657e182.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328.log.json)       |
+|  YOLOX-l   | 640  |    8xb8    | Yes |     No     |   8.0    |    50.1     |        [config](./yolox_l_fast_8xb8-300e_coco.py)         |                              [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_l_fast__8xb8-300e_coco/yolox_l_fast_8xb8-300e_coco_20230213_160715-c731eb1c.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_l_fast_8xb8-300e_coco/yolox_l_fast_8xb8-300e_coco_20230213_160715.log.json)                               |
+|  YOLOX-x   | 640  |    8xb8    | Yes |     No     |   9.8    |    51.4     |        [config](./yolox_x_fast_8xb8-300e_coco.py)         |                               [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_x_fast_8xb8-300e_coco/yolox_x_fast_8xb8-300e_coco_20230215_133950-1d509fab.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_x_fast_8xb8-300e_coco/yolox_x_fast_8xb8-300e_coco_20230215_133950.log.json)                               |

 YOLOX uses a default training configuration of `8xbs8` which results in a long training time, we expect it to use `8xbs32` to speed up the training and not cause a decrease in mAP. We modified `train_batch_size_per_gpu` from 8 to 32, `batch_augments_interval` from 10 to 1 and `base_lr` from 0.01 to 0.04 under YOLOX-s default configuration based on the linear scaling rule, which resulted in mAP degradation. Finally, I found that using RTMDet's training hyperparameter can improve performance in YOLOX Tiny/S/M, which also validates the superiority of RTMDet's training hyperparameter.

--- a/tools/model_converters/yolov5u_to_mmyolo.py
+++ b/tools/model_converters/yolov5u_to_mmyolo.py
@ -0,0 +1,88 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+from collections import OrderedDict
+
+import torch
+
+convert_dict_p5 = {
+    'model.0': 'backbone.stem',
+    'model.1': 'backbone.stage1.0',
+    'model.2': 'backbone.stage1.1',
+    'model.3': 'backbone.stage2.0',
+    'model.4': 'backbone.stage2.1',
+    'model.5': 'backbone.stage3.0',
+    'model.6': 'backbone.stage3.1',
+    'model.7': 'backbone.stage4.0',
+    'model.8': 'backbone.stage4.1',
+    'model.9': 'backbone.stage4.2',
+    'model.10': 'neck.reduce_layers.2',
+    'model.13': 'neck.top_down_layers.0.0',
+    'model.14': 'neck.top_down_layers.0.1',
+    'model.17': 'neck.top_down_layers.1',
+    'model.18': 'neck.downsample_layers.0',
+    'model.20': 'neck.bottom_up_layers.0',
+    'model.21': 'neck.downsample_layers.1',
+    'model.23': 'neck.bottom_up_layers.1',
+    'model.24': 'bbox_head.head_module',
+}
+
+
+def convert(src, dst):
+    """Convert keys in pretrained YOLOv5u models to mmyolo style."""
+    convert_dict = convert_dict_p5
+
+    print('Converting P5 model')
+    try:
+        yolov5_model = torch.load(src)['model']
+        blobs = yolov5_model.state_dict()
+    except ModuleNotFoundError:
+        raise RuntimeError(
+            'This script must be placed under the ultralytics repo,'
+            ' because loading the official pretrained model need'
+            ' `model.py` to build model.')
+    state_dict = OrderedDict()
+
+    for key, weight in blobs.items():
+
+        num, module = key.split('.')[1:3]
+        prefix = f'model.{num}'
+        new_key = key.replace(prefix, convert_dict[prefix])
+
+        if '.m.' in new_key:
+            new_key = new_key.replace('.m.', '.blocks.')
+            new_key = new_key.replace('.cv', '.conv')
+        elif 'bbox_head.head_module' in new_key:
+            new_key = new_key.replace('.cv2', '.reg_preds')
+            new_key = new_key.replace('.cv3', '.cls_preds')
+        elif 'backbone.stage4.2' in new_key:
+            new_key = new_key.replace('.cv', '.conv')
+        else:
+            new_key = new_key.replace('.cv1', '.main_conv')
+            new_key = new_key.replace('.cv2', '.short_conv')
+            new_key = new_key.replace('.cv3', '.final_conv')
+
+        if 'bbox_head.head_module.dfl.conv.weight' == new_key:
+            print('Drop "bbox_head.head_module.dfl.conv.weight", '
+                  'because it is useless')
+            continue
+        state_dict[new_key] = weight
+        print(f'Convert {key} to {new_key}')
+
+    # save checkpoint
+    checkpoint = dict()
+    checkpoint['state_dict'] = state_dict
+    torch.save(checkpoint, dst)
+
+
+# Note: This script must be placed under the ultralytics repo to run.
+def main():
+    parser = argparse.ArgumentParser(description='Convert model keys')
+    parser.add_argument(
+        '--src', default='yolov5su.pt', help='src yolov5u model path')
+    parser.add_argument('--dst', default='mmyolov5su.pth', help='save path')
+    args = parser.parse_args()
+    convert(args.src, args.dst)
+
+
+if __name__ == '__main__':
+    main()
--- a/tools/model_converters/yolov8_to_mmyolo.py
+++ b/tools/model_converters/yolov8_to_mmyolo.py
@ -75,7 +75,7 @@ def convert(src, dst):
    torch.save(checkpoint, dst)


-# Note: This script must be placed under the YOLOv8 repo to run.
+# Note: This script must be placed under the ultralytics repo to run.
 def main():
    parser = argparse.ArgumentParser(description='Convert model keys')
    parser.add_argument(