<div align="center">
<p>
<a align="left" href="https://ultralytics.com/yolov5" target="_blank">
<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
</p>
<br>
<div>
<a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
<a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
</div>
<br>
<p>
YOLOv5 馃殌 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
</p>
<div align="center">
<a href="https://github.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.linkedin.com/company/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://twitter.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.producthunt.com/@glenn_jocher">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://youtube.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.facebook.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.instagram.com/ultralytics/">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
</a>
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<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
-->
</div>
## <div align="center">Documentation</div>
See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
## <div align="center">Quick Start Examples</div>
<details open>
<summary>Install</summary>
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
[**Python>=3.7.0**](https://www.python.org/) environment, including
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
```bash
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
```
</details>
<details open>
<summary>Inference</summary>
Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)
. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```python
import torch
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom
# Images
img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
# Inference
results = model(img)
# Results
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
```
</details>
<details>
<summary>Inference with detect.py</summary>
`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from
the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
```bash
python detect.py --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
```
</details>
<details>
<summary>Training</summary>
The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
largest `--batch-size` possible, or pass `--batch-size -1` for
YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
```bash
python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
yolov5s 64
yolov5m 40
yolov5l 24
yolov5x 16
```
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
</details>
<details open>
<summary>Tutorials</summary>
- [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)聽 馃殌 RECOMMENDED
- [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)聽 鈽橈笍
RECOMMENDED
- [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)聽 馃専 NEW
- [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)聽 馃専 NEW
- [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)聽 猸� NEW
- [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 馃殌
- [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
- [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
- [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
- [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
- [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)聽 猸� NEW
- [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998)聽 猸� NEW
</details>
## <div align="center">Environments</div>
Get started in seconds with our verified environments. Click each icon below for details.
<div align="center">
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
<img src="https://github.com/ultralytics/yolov5/releas
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用YOLOV5实现人脸检测的代码,配置好系统可直接运行
(108个子文件)
setup.cfg 2KB
Dockerfile 2KB
Dockerfile 821B
Dockerfile-cpu 1KB
.dockerignore 4KB
.gitattributes 75B
.gitignore 4KB
tutorial.ipynb 55KB
bus.jpg 476KB
zidane.jpg 165KB
LICENSE 34KB
README.md 15KB
README.md 11KB
CODE_OF_CONDUCT.md 5KB
CONTRIBUTING.md 5KB
README.md 2KB
PULL_REQUEST_TEMPLATE.md 693B
SECURITY.md 359B
datasets.py 46KB
general.py 39KB
train.py 34KB
common.py 33KB
export.py 29KB
wandb_utils.py 27KB
tf.py 21KB
plots.py 21KB
val.py 19KB
yolo.py 15KB
metrics.py 14KB
torch_utils.py 13KB
detect.py 13KB
augmentations.py 12KB
loss.py 10KB
__init__.py 8KB
autoanchor.py 7KB
downloads.py 6KB
hubconf.py 6KB
benchmarks.py 6KB
experimental.py 5KB
activations.py 3KB
callbacks.py 2KB
autobatch.py 2KB
restapi.py 1KB
sweep.py 1KB
resume.py 1KB
__init__.py 1KB
log_dataset.py 1KB
example_request.py 368B
__init__.py 0B
__init__.py 0B
__init__.py 0B
userdata.sh 1KB
get_coco.sh 900B
mime.sh 780B
get_coco128.sh 615B
download_weights.sh 523B
requirements.txt 924B
additional_requirements.txt 105B
Objects365.yaml 8KB
xView.yaml 5KB
VOC.yaml 3KB
anchors.yaml 3KB
VisDrone.yaml 3KB
Argoverse.yaml 3KB
sweep.yaml 2KB
SKU-110K.yaml 2KB
coco.yaml 2KB
yolov5-p7.yaml 2KB
GlobalWheat2020.yaml 2KB
yolov5m6.yaml 2KB
yolov5x6.yaml 2KB
yolov5s6.yaml 2KB
yolov5n6.yaml 2KB
yolov5l6.yaml 2KB
yolov5-p6.yaml 2KB
coco128.yaml 2KB
hyp.scratch-low.yaml 2KB
yolov5-p2.yaml 2KB
hyp.scratch-med.yaml 2KB
hyp.scratch-high.yaml 2KB
.pre-commit-config.yaml 2KB
yolov3-spp.yaml 2KB
yolov3.yaml 2KB
yolov5s-ghost.yaml 1KB
yolov5s-transformer.yaml 1KB
yolov5-bifpn.yaml 1KB
yolov5-panet.yaml 1KB
yolov5n.yaml 1KB
yolov5s.yaml 1KB
yolov5x.yaml 1KB
yolov5m.yaml 1KB
yolov5l.yaml 1KB
yolov5-p34.yaml 1KB
yolov3-tiny.yaml 1KB
yolov5-fpn.yaml 1KB
hyp.VOC.yaml 1KB
hyp.Objects365.yaml 673B
app.yaml 174B
greetings.yml 5KB
ci-testing.yml 3KB
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