<div align="center">
<p>
<a href="https://github.com/ultralytics/assets/releases/tag/v8.2.0" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="YOLO Vision banner"></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/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [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://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics 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="Ultralytics Docker Pulls"></a>
<a href="https://ultralytics.com/discord"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
<a href="https://community.ultralytics.com"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fblue-sea-697d.quartiers047.workers.dev%3A443%2Fhttps%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run Ultralytics 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 Ultralytics In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics 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" alt="YOLOv8 performance plots"></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%" alt="space">
<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%" alt="space">
<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%" alt="space">
<a href="https://youtube.com/ultralytics?sub_confirmation=1"><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%" alt="space">
<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%" alt="space">
<a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<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/pyproject.toml) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
[](https://pypi.org/project/ultralytics/) [](https://pepy.tech/project/ultralytics) [](https://pypi.org/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).
[](https://anaconda.org/conda-forge/ultralytics) [](https://hub.docker.com/r/ultralytics/ultralytics)
</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="coco8.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
yolov8YOLOV8动物检测(代码+动物检测数据集+训练好的模型+图形化系统) YOLO系列目前已经更新到了V10,并且YOLO系列模型已经目前稳定运行了一段时间。经过一段时间的准备,我们选择在暑期的这个时间点更新YOLOV8模型的教程,从原理、数据标注和环境配置一一展开讲解,帮助小伙伴们掌握YOLOv8的基本内容。注意本次的教程除了支持v8模型的训练,还适用v3、v5、v9、v10等一系列模型的训练。 资源中包含的内容有标注好的一份动物检测的数据集(大约5000张图像),可以训练和验证的代码、训练好的yolo系列的模型和一份图形化界面,以及我们的联系方式,如果调试遇到问题可以找我来进行交流,对应的视频放置在这个位置https://www.bilibili.com/video/BV1rxHLeoE8D/
资源推荐
资源详情
资源评论




























收起资源包目录





































































































共 2000 条
- 1
- 2
- 3
- 4
- 5
- 6
- 20


肆十二
- 粉丝: 2w+
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
- 【Android应用源码】网上绝无仅有的Log分析教程及例子.zip
- 【Android应用源码】微博客户端源代码.zip
- 【Android应用源码】网易新闻.zip
- 【Android应用源码】文本框可输入字符数量源码.zip
- 【Android应用源码】文件管理器源码,文件拖曳,list弹性,root ,zip压缩解.zip
- 【Android应用源码】我也模仿了Path效果,效果更接近iphone.zip
- 【Android应用源码】无线点餐系统.zip
- 【Android应用源码】五种不同的Toast效果.zip
- 【Android应用源码】五种效果的Toast.zip
- 【Android应用源码】物理传感器游戏-小球快跑源码.zip
- 【Android应用源码】系统原理与开 发要点详解_培训课件.zip
- 【Android应用源码】下拉刷新2.zip
- 【Android应用源码】下拉刷新.zip
- 【Android应用源码】下的加密信息客户端 WhisperSystems-TextSecure.zip
- 溴化锂吸收制冷技术中单效与双效系统的模拟研究及其应用
- 基于组态王与西门子S7-200 PLC的六层电梯控制系统设计与实现
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

- 1
- 2
- 3
- 4
- 5
- 6
前往页