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<p>
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[English](README.md) | [ç®ä½ä¸æ](README.zh-CN.md)
<br>
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<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>
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<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>
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</div>
<br>
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.
We hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5 <a href="https://docs.ultralytics.com/yolov5">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/yolov5/issues/new/choose">GitHub</a> for support, and join our <a href="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).
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<br>
## <div align="center">YOLOv8 ð NEW</div>
We are thrilled to announce the launch of Ultralytics YOLOv8 ð, our NEW cutting-edge, state-of-the-art (SOTA) model
released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**.
YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of
object detection, image segmentation and image classification tasks.
See the [YOLOv8 Docs](https://docs.ultralytics.com) for details and get started with:
[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
```bash
pip install ultralytics
```
<div align="center">
<a href="https://ultralytics.com/yolov8" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
</div>
## <div align="center">Documentation</div>
See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5) 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>=3.8.0**](https://www.python.org/) environment, including
[**PyTorch>=1.8**](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://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 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 --weights yolov5s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/LNwODJXcvt4' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
```
</details>
<details>
<summary>Training</summary>
The commands below reproduce YOLOv5 [COCO](https://github.com/ultral
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【基于Ubuntu下Yolov5的目标识别】保姆级教程 - 虚拟机安装 - Ubuntu安装 - 环境配置 (150个子文件)
CITATION.cff 393B
setup.cfg 2KB
Dockerfile 3KB
Dockerfile 821B
Dockerfile-arm64 2KB
Dockerfile-cpu 2KB
基于Ubuntu操作系统下YoloV5的目标识别.docx 30.95MB
tutorial.ipynb 101KB
tutorial.ipynb 42KB
tutorial.ipynb 40KB
bus.jpg 480KB
bus.jpg 476KB
zidane.jpg 244KB
zidane.jpg 165KB
optimizer_config.json 3KB
LICENSE 34KB
README.md 41KB
README.zh-CN.md 40KB
README.md 11KB
README.md 11KB
CONTRIBUTING.md 5KB
README.md 2KB
基于Ubuntu操作系统下YoloV5的目标识别.pdf 8.52MB
yolov5x.pt 166.05MB
yolov5l.pt 89.29MB
yolov5m.pt 40.82MB
yolov5s.pt 14.12MB
yolov5n.pt 3.87MB
dataloaders.py 55KB
general.py 45KB
common.py 41KB
export.py 40KB
train.py 34KB
train.py 33KB
tf.py 26KB
val.py 23KB
val.py 20KB
torch_utils.py 19KB
__init__.py 18KB
plots.py 18KB
yolo.py 17KB
augmentations.py 17KB
__init__.py 16KB
train.py 16KB
predict.py 15KB
detect.py 15KB
metrics.py 14KB
dataloaders.py 14KB
predict.py 11KB
loss.py 10KB
loss.py 8KB
wandb_utils.py 8KB
val.py 8KB
clearml_utils.py 8KB
benchmarks.py 8KB
hubconf.py 8KB
autoanchor.py 7KB
hpo.py 6KB
plots.py 6KB
general.py 6KB
metrics.py 5KB
hpo.py 5KB
downloads.py 5KB
comet_utils.py 5KB
experimental.py 4KB
augmentations.py 4KB
triton.py 4KB
activations.py 3KB
autobatch.py 3KB
callbacks.py 3KB
__init__.py 3KB
restapi.py 1KB
resume.py 1KB
example_request.py 369B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
dataloaders.cpython-38.pyc 42KB
general.cpython-38.pyc 37KB
common.cpython-38.pyc 37KB
export.cpython-38.pyc 30KB
plots.cpython-38.pyc 17KB
torch_utils.cpython-38.pyc 16KB
yolo.cpython-38.pyc 16KB
augmentations.cpython-38.pyc 13KB
metrics.cpython-38.pyc 11KB
autoanchor.cpython-38.pyc 6KB
experimental.cpython-38.pyc 5KB
downloads.cpython-38.pyc 4KB
__init__.cpython-38.pyc 3KB
__init__.cpython-38.pyc 134B
get_imagenet.sh 2KB
get_coco.sh 2KB
userdata.sh 1KB
mime.sh 780B
download_weights.sh 641B
get_coco128.sh 619B
install_yolo.sh 130B
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