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[涓枃](https://docs.ultralytics.com/zh/) | [頃滉淡鞏碷(https://docs.ultralytics.com/ko/) | [鏃ユ湰瑾瀅(https://docs.ultralytics.com/ja/) | [袪褍褋褋泻懈泄](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Fran莽ais](https://docs.ultralytics.com/fr/) | [Espa帽ol](https://docs.ultralytics.com/es/) | [Portugu锚s](https://docs.ultralytics.com/pt/) | [啶灌た啶ㄠ啶︵](https://docs.ultralytics.com/hi/) | [丕賱毓乇亘賷丞](https://docs.ultralytics.com/ar/)
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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>
<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>
YOLOv5 馃殌 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 馃殌 NEW</div>
We are thrilled to announce the launch of Ultralytics YOLOv8 馃殌, 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
```
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<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
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温馨提示
基于YOLOV5 对硬纸板缺陷目标检测数据集(1类别)的目标检测实战项目,包含代码、数据集、训练好的权重参数,经测试,代码可以直接使用 图像分辨率为1080*1440的RGB图片,数据为纸板的破损、起泡、划痕等缺陷检测 【数据集介绍】硬纸板缺陷图像数据,1类别:detect 数据分为分为训练集和验证集 训练集datasets-images-train:845张图片和845个标签txt文件组成 验证集datasets-images-val:211张图片和211个标签txt文件组成 【yolov5】项目总大小:204MB 项目迭代了100个epoch,在runs目录下保存了训练结果,训练最好的精度map0.5=0.94,map0.5:0.95=0.66。训练过程中会生成验证集的混淆矩阵,PR曲线、F1曲线等等runs/detect目录下保存了网络推理训练集的全部结果,推理效果很好 更多yolov5改进介绍、或者如何训练,请参考: https://blog.csdn.net/qq_44886601/category_12605353.html
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YOLOV5 实战项目:硬纸板缺陷目标检测数据集(1类别) (2000个子文件)
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