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# YOLOv5
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## Abstract
YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
## Results and models
### COCO
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| Backbone | size | SyncBN | AMP | Mem (GB) | box AP | Config | Download |
| :------: | :--: | :----: | :-: | :------: | :----: | :-------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| YOLOv5-s | 640 | Yes | Yes | 2.7 | 37.7 | [config ](https://github.com/open-mmlab/mmyolo/tree/master/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-m | 640 | Yes | Yes | 5.0 | 45.3 | [config ](https://github.com/open-mmlab/mmyolo/tree/master/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 ) |
| YOLOv5-l | 640 | Yes | Yes | 8.1 | 48.8 | [config ](https://github.com/open-mmlab/mmyolo/tree/master/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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**Note**:
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.
2. `SyncBN` means use SyncBN, `AMP` indicates training with mixed precision.
3. 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. The performance is unstable and may fluctuate by about 0.4 mAP.
