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[Feature] YOLOv5 supports using mask annotation to optimize bbox (#565)
* add v5 config and readme * fix config * update config * add remove mask * update * update * fix * update --------- Co-authored-by: huanghaian <huanghaian@sensetime.com>
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@ -20,19 +20,24 @@ YOLOv5-l-P6 model structure
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### COCO
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### COCO
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| Backbone | Arch | size | SyncBN | AMP | Mem (GB) | box AP | TTA box AP | Config | Download |
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| Backbone | Arch | size | Mask Refine | SyncBN | AMP | Mem (GB) | box AP | TTA box AP | Config | Download |
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| :------: | :--: | :--: | :----: | :-: | :------: | :----: | :--------: | :--------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| :------: | :--: | :--: | :---------: | :----: | :-: | :------: | :---------: | :--------: | :-------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| YOLOv5-n | P5 | 640 | Yes | Yes | 1.5 | 28.0 | 30.7 | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/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) |
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| 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) |
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| YOLOv5-s | P5 | 640 | Yes | Yes | 2.7 | 37.7 | 40.2 | [config](https://github.com/open-mmlab/mmyolo/tree/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) |
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| 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) |
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| YOLOv5-m | P5 | 640 | Yes | Yes | 5.0 | 45.3 | 46.9 | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/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) |
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| 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) |
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| YOLOv5-l | P5 | 640 | Yes | Yes | 8.1 | 48.8 | 49.9 | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/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) |
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| 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) |
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| YOLOv5-n | P6 | 1280 | Yes | Yes | 5.8 | 35.9 | | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/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) |
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| 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) |
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| YOLOv5-s | P6 | 1280 | Yes | Yes | 10.5 | 44.4 | | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/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) |
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| 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) |
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| YOLOv5-m | P6 | 1280 | Yes | Yes | 19.1 | 51.3 | | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/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) |
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| 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) |
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| YOLOv5-l | P6 | 1280 | Yes | Yes | 30.5 | 53.7 | | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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**Note**:
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**Note**:
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In the official YOLOv5 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. See https://github.com/ultralytics/yolov5/issues/9917 for details.
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1. `fast` means that `YOLOv5DetDataPreprocessor` and `yolov5_collate` are used for data preprocessing, which is faster for training, but less flexible for multitasking. Recommended to use fast version config if you only care about object detection.
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1. `fast` means that `YOLOv5DetDataPreprocessor` and `yolov5_collate` are used for data preprocessing, which is faster for training, but less flexible for multitasking. Recommended to use fast version config if you only care about object detection.
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2. `detect` means that the network input is fixed to `640x640` and the post-processing thresholds is modified.
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2. `detect` means that the network input is fixed to `640x640` and the post-processing thresholds is modified.
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@ -40,6 +45,7 @@ In the official YOLOv5 code, the `random_perspective` data augmentation in COCO
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4. We use 8x A100 for training, and the single-GPU batch size is 16. This is different from the official code.
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4. We use 8x A100 for training, and the single-GPU batch size is 16. This is different from the official code.
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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.
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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.
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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.
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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.
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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`.
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### VOC
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### VOC
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@ -0,0 +1,77 @@
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_base_ = './yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py'
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# This config use refining bbox and `YOLOv5CopyPaste`.
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# Refining bbox means refining bbox by mask while loading annotations and
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# transforming after `YOLOv5RandomAffine`
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# ========================modified parameters======================
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deepen_factor = 1.0
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widen_factor = 1.0
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mixup_prob = 0.1
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copypaste_prob = 0.1
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# =======================Unmodified in most cases==================
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img_scale = _base_.img_scale
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model = dict(
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backbone=dict(
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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),
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neck=dict(
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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),
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bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
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pre_transform = _base_.pre_transform
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albu_train_transforms = _base_.albu_train_transforms
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mosaic_affine_pipeline = [
|
||||||
|
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,
|
||||||
|
scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.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),
|
||||||
|
dict(type='RemoveDataElement', keys=['gt_masks'])
|
||||||
|
]
|
||||||
|
|
||||||
|
# enable mixup and copypaste
|
||||||
|
train_pipeline = [
|
||||||
|
*pre_transform, *mosaic_affine_pipeline,
|
||||||
|
dict(
|
||||||
|
type='YOLOv5MixUp',
|
||||||
|
prob=mixup_prob,
|
||||||
|
pre_transform=[*pre_transform, *mosaic_affine_pipeline]),
|
||||||
|
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_dataloader = dict(dataset=dict(pipeline=train_pipeline))
|
||||||
@ -0,0 +1,86 @@
|
|||||||
|
_base_ = './yolov5_s_mask-refine-v61_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
|
||||||
|
lr_factor = 0.1
|
||||||
|
loss_cls_weight = 0.3
|
||||||
|
loss_obj_weight = 0.7
|
||||||
|
|
||||||
|
affine_scale = 0.9
|
||||||
|
mixup_prob = 0.1
|
||||||
|
|
||||||
|
# =======================Unmodified in most cases==================
|
||||||
|
num_classes = _base_.num_classes
|
||||||
|
num_det_layers = _base_.num_det_layers
|
||||||
|
img_scale = _base_.img_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),
|
||||||
|
loss_cls=dict(loss_weight=loss_cls_weight *
|
||||||
|
(num_classes / 80 * 3 / num_det_layers)),
|
||||||
|
loss_obj=dict(loss_weight=loss_obj_weight *
|
||||||
|
((img_scale[0] / 640)**2 * 3 / num_det_layers))))
|
||||||
|
|
||||||
|
pre_transform = _base_.pre_transform
|
||||||
|
albu_train_transforms = _base_.albu_train_transforms
|
||||||
|
|
||||||
|
mosaic_affine_pipeline = [
|
||||||
|
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),
|
||||||
|
# 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),
|
||||||
|
dict(type='RemoveDataElement', keys=['gt_masks'])
|
||||||
|
]
|
||||||
|
|
||||||
|
# enable mixup
|
||||||
|
train_pipeline = [
|
||||||
|
*pre_transform, *mosaic_affine_pipeline,
|
||||||
|
dict(
|
||||||
|
type='YOLOv5MixUp',
|
||||||
|
prob=mixup_prob,
|
||||||
|
pre_transform=[*pre_transform, *mosaic_affine_pipeline]),
|
||||||
|
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_dataloader = dict(dataset=dict(pipeline=train_pipeline))
|
||||||
|
default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))
|
||||||
@ -0,0 +1,20 @@
|
|||||||
|
_base_ = './yolov5_s_mask-refine-v61_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)))
|
||||||
@ -0,0 +1,62 @@
|
|||||||
|
_base_ = '../yolov5_s-v61_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_dataloader = dict(dataset=dict(pipeline=train_pipeline))
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
_base_ = './yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py'
|
||||||
|
|
||||||
|
# This config use refining bbox and `YOLOv5CopyPaste`.
|
||||||
|
# Refining bbox means refining bbox by mask while loading annotations and
|
||||||
|
# transforming after `YOLOv5RandomAffine`
|
||||||
|
|
||||||
|
# ========================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)))
|
||||||
@ -80,6 +80,18 @@ Models:
|
|||||||
Metrics:
|
Metrics:
|
||||||
box AP: 48.8
|
box AP: 48.8
|
||||||
Weights: 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
|
Weights: 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
|
||||||
|
- Name: yolov5_x-v61_syncbn_fast_8xb16-300e_coco
|
||||||
|
In Collection: YOLOv5
|
||||||
|
Config: configs/yolov5/yolov5_x-v61_syncbn_fast_8xb16-300e_coco.py
|
||||||
|
Metadata:
|
||||||
|
Training Memory (GB): 12.2
|
||||||
|
Epochs: 300
|
||||||
|
Results:
|
||||||
|
- Task: Object Detection
|
||||||
|
Dataset: COCO
|
||||||
|
Metrics:
|
||||||
|
box AP: 50.2
|
||||||
|
Weights: 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
|
||||||
- Name: yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco
|
- Name: yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco
|
||||||
In Collection: YOLOv5
|
In Collection: YOLOv5
|
||||||
Config: configs/yolov5/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco.py
|
Config: configs/yolov5/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco.py
|
||||||
@ -176,3 +188,63 @@ Models:
|
|||||||
Metrics:
|
Metrics:
|
||||||
box AP: 73.1
|
box AP: 73.1
|
||||||
Weights: 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
|
Weights: 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
|
||||||
|
- Name: yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco
|
||||||
|
In Collection: YOLOv5
|
||||||
|
Config: configs/yolov5/mask_refine/yolov5_n_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py
|
||||||
|
Metadata:
|
||||||
|
Training Memory (GB): 1.5
|
||||||
|
Epochs: 300
|
||||||
|
Results:
|
||||||
|
- Task: Object Detection
|
||||||
|
Dataset: COCO
|
||||||
|
Metrics:
|
||||||
|
box AP: 28.0
|
||||||
|
Weights: 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
|
||||||
|
- Name: yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco
|
||||||
|
In Collection: YOLOv5
|
||||||
|
Config: configs/yolov5/mask_refine/yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py
|
||||||
|
Metadata:
|
||||||
|
Training Memory (GB): 2.7
|
||||||
|
Epochs: 300
|
||||||
|
Results:
|
||||||
|
- Task: Object Detection
|
||||||
|
Dataset: COCO
|
||||||
|
Metrics:
|
||||||
|
box AP: 38.0
|
||||||
|
Weights: 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
|
||||||
|
- Name: yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco
|
||||||
|
In Collection: YOLOv5
|
||||||
|
Config: configs/yolov5/mask_refine/yolov5_m_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py
|
||||||
|
Metadata:
|
||||||
|
Training Memory (GB): 5.0
|
||||||
|
Epochs: 300
|
||||||
|
Results:
|
||||||
|
- Task: Object Detection
|
||||||
|
Dataset: COCO
|
||||||
|
Metrics:
|
||||||
|
box AP: 45.3
|
||||||
|
Weights: 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
|
||||||
|
- Name: yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco
|
||||||
|
In Collection: YOLOv5
|
||||||
|
Config: configs/yolov5/mask_refine/yolov5_l_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py
|
||||||
|
Metadata:
|
||||||
|
Training Memory (GB): 8.1
|
||||||
|
Epochs: 300
|
||||||
|
Results:
|
||||||
|
- Task: Object Detection
|
||||||
|
Dataset: COCO
|
||||||
|
Metrics:
|
||||||
|
box AP: 49.3
|
||||||
|
Weights: 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
|
||||||
|
- Name: yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco
|
||||||
|
In Collection: YOLOv5
|
||||||
|
Config: configs/yolov5/mask_refine/yolov5_x_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py
|
||||||
|
Metadata:
|
||||||
|
Training Memory (GB): 12.2
|
||||||
|
Epochs: 300
|
||||||
|
Results:
|
||||||
|
- Task: Object Detection
|
||||||
|
Dataset: COCO
|
||||||
|
Metrics:
|
||||||
|
box AP: 50.9
|
||||||
|
Weights: 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
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