<div align="center">
<p>
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<img width="850" src="https://github.com/ultralytics/assets/blob/master/yolov5/v62/splash_readme.png"></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="YOLOv5 CI"></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://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
<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>
</div>
<br>
<p>
YOLOv5 ð is the world's most loved vision AI, representing <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.
<br><br>
To request a commercial license please complete the form at <a href="https://ultralytics.com/license">Ultralytics Licensing</a>.
<br><br>
</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>
</div>
## <div align="center">Documentation</div>
See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. See below for quickstart examples.
<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>
<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
screen # screenshot
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 --epochs 300 --weights '' --cfg yolov5n.yaml --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
- [NVIDIA Jetson Nano Deployment](https://github.com/ultralytics/yolov5/issues/9627) ð NEW
- [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
- [Model Ensembling]
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
【探索人工智能的宝藏之地】 无论您是计算机相关专业的在校学生、老师,还是企业界的探索者,这个项目都是为您量身打造的。无论您是初入此领域的小白,还是寻求更高层次进阶的资深人士,这里都有您需要的宝藏。不仅如此,它还可以作为毕设项目、课程设计、作业、甚至项目初期的立项演示。 【人工智能的深度探索】 人工智能——模拟人类智能的技术和理论,使其在计算机上展现出类似人类的思考、判断、决策、学习和交流能力。这不仅是一门技术,更是一种前沿的科学探索。 【实战项目与源码分享】 我们深入探讨了深度学习的基本原理、神经网络的应用、自然语言处理、语言模型、文本分类、信息检索等领域。更有深度学习、机器学习、自然语言处理和计算机视觉的实战项目源码,助您从理论走向实践,如果您已有一定基础,您可以基于这些源码进行修改和扩展,实现更多功能。 【期待与您同行】 我们真诚地邀请您下载并使用这些资源,与我们一起在人工智能的海洋中航行。同时,我们也期待与您的沟通交流,共同学习,共同进步。让我们在这个充满挑战和机遇的领域中共同探索未来!
资源推荐
资源详情
资源评论
收起资源包目录
人工智能项目资料-基于yolov5的fps游戏图像识别技术.zip (1866个子文件)
events.out.tfevents.1705561538.kaguya.42324.0 957KB
events.out.tfevents.1705334623.kaguya.41968.0 920KB
events.out.tfevents.1705561473.kaguya.20836.0 88B
events.out.tfevents.1705559816.kaguya.3504.0 88B
events.out.tfevents.1705561388.kaguya.3244.0 88B
events.out.tfevents.1705558970.kaguya.14164.0 88B
events.out.tfevents.1705558805.kaguya.17136.0 88B
events.out.tfevents.1705559704.kaguya.37908.0 88B
events.out.tfevents.1705559113.kaguya.6788.0 88B
events.out.tfevents.1705559047.kaguya.35744.0 88B
train.cache 163KB
result.cache 12KB
val.cache 8KB
setup.cfg 2KB
results.csv 29KB
results.csv 29KB
logitech.driver.dll 36KB
Dockerfile 2KB
Dockerfile 821B
Dockerfile-arm64 2KB
Dockerfile-cpu 2KB
tutorial.ipynb 53KB
tutorial.ipynb 49KB
999cbaa3e30953509f72bf1466439b7.jpg 930KB
train_batch0.jpg 584KB
val_batch0_pred.jpg 571KB
train_batch1.jpg 563KB
val_batch0_labels.jpg 555KB
train_batch2.jpg 528KB
999cbaa3e30953509f72bf1466439b7.jpg 482KB
bus.jpg 476KB
val_batch0_pred.jpg 433KB
val_batch0_labels.jpg 432KB
train_batch1.jpg 395KB
train_batch0.jpg 395KB
train_batch2.jpg 354KB
CT-19.jpg 300KB
CT-18.jpg 298KB
CT-17.jpg 297KB
CT-16.jpg 295KB
CT-15.jpg 294KB
CT-14.jpg 292KB
CT-13.jpg 290KB
CT-12.jpg 288KB
CT-11.jpg 286KB
CT-10.jpg 284KB
CT-9.jpg 281KB
CT-8.jpg 279KB
CT-7.jpg 277KB
CT-6.jpg 275KB
CT-5.jpg 272KB
c725db0fb294278dcfe9c6ec4c1528f.jpg 272KB
CT-4.jpg 270KB
CT-3.jpg 267KB
TTH-19.jpg 266KB
CT-2.jpg 264KB
TTH-18.jpg 264KB
TTH-17.jpg 263KB
CT-1.jpg 261KB
TTH-16.jpg 261KB
TTH-15.jpg 260KB
TTH-14.jpg 257KB
TTH-13.jpg 255KB
TTH-12.jpg 253KB
TTH-11.jpg 251KB
TTH-10.jpg 248KB
TTH-9.jpg 246KB
TTH-8.jpg 244KB
TTH-7.jpg 242KB
TTH-6.jpg 239KB
TTH-5.jpg 236KB
TTH-4.jpg 234KB
TTH-3.jpg 231KB
TTH-2.jpg 228KB
TTH-1.jpg 225KB
labels_correlogram.jpg 221KB
labels_correlogram.jpg 208KB
zidane.jpg 165KB
labels.jpg 149KB
c725db0fb294278dcfe9c6ec4c1528f.jpg 145KB
314.jpg 120KB
140.jpg 120KB
138.jpg 120KB
128.jpg 120KB
318.jpg 120KB
322.jpg 120KB
139.jpg 120KB
142.jpg 120KB
145.jpg 120KB
126.jpg 120KB
134.jpg 120KB
143.jpg 120KB
129.jpg 120KB
323.jpg 120KB
136.jpg 120KB
144.jpg 120KB
316.jpg 120KB
306.jpg 120KB
319.jpg 120KB
53.jpg 120KB
共 1866 条
- 1
- 2
- 3
- 4
- 5
- 6
- 19
资源评论
妄北y
- 粉丝: 1w+
- 资源: 1万+
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