<div align="center">
<p>
<a href="https://yolovision.ultralytics.com/" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
</p>
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)
<br>
<div>
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
<a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/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/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>
<br>
[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/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).
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
<div align="center">
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="Ultralytics Instagram"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
</div>
</div>
## <div align="center">Documentation</div>
See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
<details open>
<summary>Install</summary>
Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
[![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
```
For alternative installation methods including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and Git, please refer to the [Quickstart Guide](https://docs.ultralytics.com/quickstart).
</details>
<details open>
<summary>Usage</summary>
#### CLI
YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
```bash
yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
```
`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli) for examples.
#### Python
YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Use the model
model.train(data="coco128.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
path = model.export(format="onnx") # export the model to ONNX format
```
See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
</details>
## <div align="center">Models</div>
YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
All [Models](https://github.
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
1、yolov8破损纸板检测,包含损纸板检测权重,以及PR曲线,loss曲线等等,在破损纸板检测数据集中训练得到的权重,目标类别为break_board共1个类别,并附破损纸板检测数据集,标签格式为txt和xml两种,分别保存在两个文件夹中 2、数据集和检测结果参考:https://blog.csdn.net/zhiqingAI/article/details/124230743 3、采用pytrch框架,python代码,
资源推荐
资源详情
资源评论
收起资源包目录
yolov8破损纸板检测 ultralytics-main-yolov8-sts-break-board-data.zip (2000个子文件)
labels.cache 248KB
inference.cpp 12KB
inference.cpp 6KB
main.cpp 4KB
main.cpp 2KB
style.css 1KB
inference.h 2KB
inference.h 2KB
comments.html 2KB
source-file.html 858B
0767.jpg 228KB
0459.jpg 172KB
0404.jpg 157KB
0057.jpg 157KB
0044.jpg 156KB
0070.jpg 151KB
0066.jpg 150KB
0337.jpg 149KB
0415.jpg 149KB
0590.jpg 149KB
0069.jpg 149KB
0067.jpg 148KB
0094.jpg 144KB
0457.jpg 144KB
0560.jpg 142KB
1020.jpg 142KB
0568.jpg 140KB
0273.jpg 140KB
0344.jpg 140KB
0955.jpg 139KB
0762.jpg 139KB
0679.jpg 139KB
0293.jpg 138KB
0384.jpg 138KB
0047.jpg 138KB
0385.jpg 137KB
0602.jpg 136KB
0473.jpg 136KB
0092.jpg 135KB
0471.jpg 135KB
0586.jpg 134KB
0059.jpg 133KB
0505.jpg 133KB
0455.jpg 133KB
0054.jpg 132KB
0984.jpg 132KB
1019.jpg 132KB
0233.jpg 132KB
0725.jpg 132KB
0081.jpg 131KB
0618.jpg 131KB
0239.jpg 130KB
0528.jpg 130KB
0401.jpg 128KB
0089.jpg 128KB
0750.jpg 128KB
0941.jpg 128KB
1007.jpg 128KB
0578.jpg 128KB
0751.jpg 128KB
0018.jpg 128KB
0274.jpg 127KB
0721.jpg 127KB
0645.jpg 127KB
0353.jpg 126KB
0686.jpg 126KB
0587.jpg 126KB
0584.jpg 126KB
0064.jpg 125KB
0564.jpg 125KB
0242.jpg 124KB
0511.jpg 124KB
0500.jpg 123KB
0982.jpg 123KB
0063.jpg 123KB
0639.jpg 123KB
0535.jpg 122KB
0271.jpg 122KB
0159.jpg 122KB
0499.jpg 120KB
0483.jpg 120KB
1056.jpg 120KB
0939.jpg 120KB
0540.jpg 120KB
0777.jpg 119KB
0723.jpg 119KB
0218.jpg 118KB
0677.jpg 117KB
0802.jpg 117KB
1031.jpg 117KB
0631.jpg 117KB
0128.jpg 117KB
0748.jpg 117KB
0970.jpg 116KB
1012.jpg 116KB
0630.jpg 115KB
0745.jpg 115KB
0525.jpg 114KB
0194.jpg 114KB
0009.jpg 114KB
共 2000 条
- 1
- 2
- 3
- 4
- 5
- 6
- 20
资源评论
stsdddd
- 粉丝: 2w+
- 资源: 749
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
最新资源
- 鼎科 烧录工具 Gprobe 最新版本
- 零跑C10车载B站软件安装
- waterfall.js
- 729705932303174springboot员工工资管理系统03j8q.zip
- ST3414-VB一款SOT23封装N-Channel场效应MOS管
- ImageMetadataReader-example.rar
- ST3414S23RG-VB一款SOT23封装N-Channel场效应MOS管
- java房屋租赁管理系统用户管理、房屋管理、租赁交易、支付、评价反馈、通知提醒、报表统计、管理后台和安全隐私
- 基于opencv的人脸识别项目
- ST3413-VB一款SOT23封装P-Channel场效应MOS管
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
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