<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>
English | [ç®ä½ä¸æ](.github/README_cn.md)
<br>
<div>
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/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 ð 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" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-twitter.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-producthunt.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-youtube.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-facebook.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-instagram.png" width="2%" alt="" /></a>
</div>
<!--
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<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>
YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [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 yolov5n - yolov5x6, 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)Â ð RECOMMENDED
- [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) âï¸
RECOMMENDED
- [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ð NEW
- [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) ð
- [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)
- [Hyperpar
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
人工智能学习总结成果,希望可以帮到大家,有疑问欢迎随时沟通~ 人工智能学习总结成果,希望可以帮到大家,有疑问欢迎随时沟通~ 人工智能学习总结成果,希望可以帮到大家,有疑问欢迎随时沟通~ 人工智能学习总结成果,希望可以帮到大家,有疑问欢迎随时沟通~ 人工智能学习总结成果,希望可以帮到大家,有疑问欢迎随时沟通~
资源推荐
资源详情
资源评论
收起资源包目录
《人工智能》--高能物理人工智能平台HepAI的镜像仓库.zip (411个子文件)
make.bat 765B
.buildinfo 230B
setup.cfg 2KB
server.crt 1KB
theme.css 129KB
basic.css 14KB
pygments.css 5KB
badge_only.css 3KB
copybutton.css 2KB
Dockerfile 2KB
Dockerfile 821B
Dockerfile 230B
Dockerfile-arm64 2KB
Dockerfile-cpu 2KB
.dockerignore 4KB
index.doctree 6KB
algorithm_lib.doctree 2KB
getting_started.doctree 2KB
fontawesome-webfont.eot 162KB
.gitattributes 75B
.gitignore 4KB
.gitignore 2KB
.gitignore 70B
index.html 6KB
getting_started.html 5KB
algorithm_lib.html 4KB
search.html 4KB
genindex.html 4KB
MANIFEST.in 118B
objects.inv 297B
tutorial.ipynb 58KB
bus.jpg 476KB
zidane.jpg 165KB
QuarkGluon_dataset.jpg 113KB
Default_dataset.jpg 35KB
jquery-3.6.0.js 282KB
jquery.js 87KB
underscore-1.13.1.js 67KB
underscore.js 19KB
searchtools.js 18KB
clipboard.min.js 9KB
copybutton.js 8KB
theme.js 5KB
language_data.js 5KB
sphinx_highlight.js 5KB
doctools.js 4KB
_sphinx_javascript_frameworks_compat.js 4KB
html5shiv-printshiv.min.js 4KB
html5shiv.min.js 3KB
copybutton_funcs.js 3KB
searchindex.js 1KB
badge_only.js 934B
documentation_options.js 425B
datasets.json 2KB
hub_models.json 960B
server.key 2KB
LICENSE 34KB
LICENSE 34KB
Makefile 634B
README.md 29KB
README_cn.md 28KB
README.md 11KB
README.md 10KB
remote.md 9KB
README.md 7KB
developer_doc.md 6KB
quickstart_hpc.md 6KB
CODE_OF_CONDUCT.md 5KB
datasets.md 5KB
CONTRIBUTING.md 5KB
README.md 3KB
hepai_api_sam.md 3KB
hepai_worker.md 3KB
tutorial.md 3KB
gpt4.md 2KB
README.md 2KB
hepai_api.md 2KB
npu_power_doc.md 2KB
readme.md 1KB
PULL_REQUEST_TEMPLATE.md 693B
algorithm_zoo.md 625B
install.md 533B
quickstart.md 501B
update_log.md 478B
SECURITY.md 359B
README.md 268B
getting_started.md 191B
readme.md 191B
README_template.md 109B
README.md 62B
README.md 50B
README.md 49B
README.md 48B
model_zoo.md 5B
environment.pickle 17KB
JetClass_dataset.png 366KB
image.png 72KB
file.png 286B
plus.png 90B
minus.png 90B
共 411 条
- 1
- 2
- 3
- 4
- 5
资源评论
季风泯灭的季节
- 粉丝: 601
- 资源: 2920
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功