# 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.
<div align=center>
<img src="https://user-images.githubusercontent.com/27466624/200000324-70ae078f-cea7-4189-8baa-440656797dad.jpg"/>
YOLOv5-l-P5 model structure
</div>
<div align=center>
<img src="https://user-images.githubusercontent.com/27466624/211143533-1725c1b2-6189-4c3a-a046-ad968e03cb9d.jpg"/>
YOLOv5-l-P6 model structure
</div>
## Results and models
### 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_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_8xb16-3
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yolov8系列--OpenMMLab YOLO series toolbox and benchmark. Imp.zip
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yolov8系列--OpenMMLab YOLO series toolbox and benchmark. Imp.zip (652个子文件)
make.bat 760B
make.bat 760B
setup.cfg 861B
nvdsparsebbox_mmyolo.cpp 4KB
readthedocs.css 141B
readthedocs.css 141B
Dockerfile 1KB
Dockerfile 527B
Dockerfile_deployment 3KB
.gitignore 1KB
MANIFEST.in 210B
pytest.ini 293B
15_minutes_object_detection.ipynb 42KB
15_minutes_instance_segmentation.ipynb 29KB
zhihu_qrcode.jpg 388KB
demo.jpg 254KB
qq_group_qrcode.jpg 203KB
large_image.jpg 168KB
dog.jpg 160KB
color.jpg 35KB
000001.jpg 16KB
000001.jpg 10KB
coco_sample_color.json 4KB
coco_sample.json 4KB
multi_scale.json 270B
single_scale.json 260B
LICENSE 34KB
Makefile 634B
Makefile 634B
labeling_to_deployment_tutorials.md 63KB
labeling_to_deployment_tutorials.md 62KB
registries_info.md 47KB
rtmdet_description.md 34KB
yolov5_description.md 33KB
config.md 33KB
config.md 31KB
yolov5_description.md 29KB
code_style.md 26KB
README_zh-CN.md 26KB
README.md 25KB
visualization.md 25KB
yolov6_description.md 24KB
training_testing_tricks.md 23KB
15_minutes_object_detection.md 23KB
15_minutes_object_detection.md 22KB
README.md 20KB
ionogram_detection.md 20KB
mmdeploy_yolov5.md 20KB
mmdeploy_yolov5.md 20KB
training_testing_tricks.md 19KB
ionogram_detection.md 19KB
mmdeploy_guide.md 18KB
yolov8_description.md 17KB
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README.md 17KB
visualization.md 16KB
changelog.md 16KB
changelog.md 15KB
yolov8_description.md 15KB
article.md 15KB
rotated_detection.md 15KB
15_minutes_instance_segmentation.md 14KB
contributing.md 13KB
contributing.md 13KB
15_minutes_instance_segmentation.md 13KB
replace_backbone.md 13KB
replace_backbone.md 12KB
README.md 11KB
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troubleshooting_steps.md 9KB
faq.md 9KB
model_zoo.md 9KB
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model_zoo.md 9KB
single_multi_channel_applications.md 9KB
module_combination.md 8KB
README.md 8KB
single_multi_channel_applications.md 8KB
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complexity_analysis.md 7KB
complexity_analysis.md 7KB
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