ultralytics/yolov5

ultralytics

YOLOv5是Ultralytics推出的开源视觉AI模型，支持目标检测、图像分割与分类任务，具备快速准确的推理能力，可导出为ONNX、CoreML、TFLite等多种格式，适用于多场景计算机视觉应用，Docker镜像提供便捷部署环境。

76 次收藏下载次数: 0状态：社区镜像维护者：ultralytics仓库类型：镜像最近更新：7 天前

让 AI 帮你使用轩辕镜像？ · 展开查看说明

如果你使用 DeepSeek、元宝 AI、Copilot、ChatGPT 等 AI 工具，协助配置轩辕镜像、编写 docker pull 命令、修改 Docker Compose 镜像地址、配置镜像加速、排查镜像拉取失败、分析报错日志等问题，请先让 AI 阅读并遵守轩辕镜像的规则文档。

只需在 AI 对话中先发送下面这段话即可：

请先阅读并遵守：https://xuanyuan.cloud/agents.md

未读文档前不要生成 pull 命令或排错方案。

查看 agents.md 用法指南与完整示范。国内用户首推元宝 AI、DeepSeek 的深度思考模式，不推荐豆包 AI；Cursor 等编辑器可在对话 @ 该链接，或加入 User Rules。若 AI 无法访问外链，可打开说明文档复制全文粘贴。文档会随站点更新，复制内容可能过期，建议定期检查。

DockerHub 官方简介

轩辕镜像中文简介

下载命令

镜像标签列表与下载命令

轩辕镜像，不浪费每一次拉取。

点击查看

https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml https://zenodo.org/badge/latestdoi/264818686 https://hub.docker.com/r/ultralytics/yolov5

https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb

YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics 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 Docs for details, raise an issue on https://github.com/ultralytics/yolov5/issues/new/choose for support, and join our *** community for questions and discussions!

To request an Enterprise License please complete the form at Ultralytics Licensing.

https://github.com/ultralytics

YOLOv8 🚀 NEW

We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model released at 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 for details and get started with:

bash
pip install ultralytics

Documentation

See the YOLOv5 Docs for full documentation on training, testing and deployment. See below for quickstart examples.

Install

Clone repo and install https://github.com/ultralytics/yolov5/blob/master/requirements.txt in a https://www.python.org/ environment, including https://pytorch.org/get-started/locally/.

bash
git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Inference

YOLOv5 PyTorch Hub inference. https://github.com/ultralytics/yolov5/tree/master/models download automatically from the latest YOLOv5 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.

Inference with detect.py

detect.py runs inference on a variety of sources, downloading https://github.com/ultralytics/yolov5/tree/master/models automatically from the latest YOLOv5 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

Training

The commands below reproduce YOLOv5 https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh results. https://github.com/ultralytics/yolov5/tree/master/models and https://github.com/ultralytics/yolov5/tree/master/data download automatically from the latest YOLOv5 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 times faster). Use the largest --batch-size possible, or pass --batch-size -1 for YOLOv5 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

Tutorials

Train Custom Data 🚀 RECOMMENDED
Tips for Best Training Results ☘️
Multi-GPU Training
PyTorch Hub 🌟 NEW
TFLite, ONNX, CoreML, TensorRT Export 🚀
NVIDIA Jetson platform Deployment 🌟 NEW
Test-Time Augmentation (TTA)
Model Ensembling
Model Pruning/Sparsity
Hyperparameter Evolution
Transfer Learning with Frozen Layers
Architecture Summary 🌟 NEW
Roboflow for Datasets, Labeling, and Active Learning
ClearML Logging 🌟 NEW
YOLOv5 with Neural Magic's Deepsparse 🌟 NEW
Comet Logging 🌟 NEW

Integrations

Roboflow	ClearML ⭐ NEW	Comet ⭐ NEW	Neural Magic ⭐ NEW
Label and export your custom datasets directly to YOLOv5 for training with Roboflow	Automatically track, visualize and even remotely train YOLOv5 using ClearML (open-source!)	Free forever, Comet lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions	Run YOLOv5 inference up to 6x faster with Neural Magic DeepSparse

Ultralytics HUB

Experience seamless AI with Ultralytics HUB ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly Ultralytics App. Start your journey for Free now!

Environments

Get started in seconds with our verified environments. Click each icon below for details.

https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb https://hub.docker.com/r/ultralytics/yolov5

Contribute

We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our Contributing Guide to get started, and fill out the YOLOv5 Survey to send us feedback on your experiences. Thank you to all our contributors!

https://github.com/ultralytics/yolov5/graphs/contributors

License

Ultralytics offers two licensing options to accommodate diverse use cases:

AGPL-3.0 License: This https://opensource.org/licenses/ open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the https://github.com/ultralytics/yolov5/blob/master/LICENSE file for more details.
Enterprise License: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through Ultralytics Licensing.

Contact

For YOLOv5 bug reports and feature requests please visit https://github.com/ultralytics/yolov5/issues, and join our *** community for questions and discussions!

https://github.com/ultralytics

镜像拉取方式

您可以使用以下命令拉取该镜像。请将 <标签> 替换为具体的标签版本。如需查看所有可用标签版本，请访问标签列表页面。

轩辕镜像加速拉取命令点我查看更多 yolov5 镜像标签

docker pull docker.xuanyuan.run/ultralytics/yolov5:<标签>

使用方法：

DockerHub 原生拉取命令

docker pull ultralytics/yolov5:<标签>

轩辕镜像配置手册

按平台快速找到配置文档

一键安装

一键安装 Docker

Linux Docker 一键安装

AI

用 AI 使用轩辕镜像

agents.md · AI 对话 · 提示词

Docker

登录仓库拉取

登录认证 · 私有仓库

专属域名拉取

免登录 · 高速拉取

Linux

Docker 镜像配置

Windows / Mac

Docker Desktop 配置

MacOS OrbStack

OrbStack 容器

Apple Container

macOS 原生容器

Docker Compose

Compose 项目配置

NAS

群晖

Synology 配置

飞牛

fnOS 镜像配置

绿联

绿联 NAS

威联通

QNAP 配置

极空间

极空间 NAS

Unraid

Unraid NAS

企业仓库

其他仓库

ghcr · Quay · nvcr

Harbor 镜像源

Proxy Repository 对接

Portainer 镜像源

Registries 配置

Nexus 镜像源

Docker Proxy 缓存

开发工具

Dev Containers

VS Code 开发容器

Podman

Podman 配置指南

Singularity / Apptainer

HPC 科学计算容器

Kubernetes

K8s Containerd

Kubernetes · Containerd

K3s

轻量级集群

面板 / 网络

爱快路由

爱快 4.0 · iKuai 镜像加速

宝塔面板

一键配置镜像源

需要其他帮助？请查看我们的常见问题Docker 镜像访问常见问题解答或提交工单

镜像拉取常见问题

功能

版本功能对比

功能对比 · 版本选择

支持的镜像仓库

Docker Hub · GCR · GHCR

专属域名用法

专属域名 · 开启停用 · 多仓库

新手拉取配置

docker search 限制

专属域名 · Hub 搜索

不支持 push

仅支持 pull · 不支持

拉取速度原因

带宽 · 缓存 · 冷热镜像

错误码

402 与流量用尽

402 · 流量包 · 充值

401 认证失败

401 · docker login

manifest unknown

标签错误 · 镜像不存在

410 Gone 排查

410 · Docker 升级

429 限流

免费版 · 专业版 · 企业版 · 请求频率

其他报错

DNS 超时

DNS 解析 · 网络超时

TLS 证书失败

no matching manifest（架构）

docker.sock / daemon

账号

失败是否计费

manifest · blob · 计费

申请开票（企业 / 个人）

开票 · 发票 · 工单

修改登录密码

网站 · 仓库 · 重置

注销账户

工单 · 数据 · 注销

原理

mirrors 不生效

daemon.json · 重启

去掉域名前缀

docker tag · 重命名

指定架构拉取

ARM64 · AMD64 · 多架构

latest 与「最新」

digest · 版本号 · 标签

查看全部问题→

用户好评

来自真实用户的反馈，见证轩辕镜像的优质服务

oldzhang

运维工程师

Linux服务器

"Docker访问体验非常流畅，大镜像也能快速完成下载。"