From a1fe9b0212de25e55b7be166d11bd027ea8ee07e Mon Sep 17 00:00:00 2001 From: wehub-resource-sync Date: Mon, 13 Jul 2026 10:42:43 +0000 Subject: [PATCH] docs: make Chinese README the default --- README.md | 238 +++++++++++++++++++++++++++--------------------------- 1 file changed, 119 insertions(+), 119 deletions(-) diff --git a/README.md b/README.md index 3835641..c9cc08c 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,11 @@ + +> [!NOTE] +> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。 +> [English](./README.en.md) · [原始项目](https://github.com/cvat-ai/cvat) · [上游 README](https://github.com/cvat-ai/cvat/blob/HEAD/README.md) +> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。 + [![CVAT Community header](site/content/en/images/cvat_github_header.webp)](https://app.cvat.ai) -# CVAT: Computer Vision Annotation Tool +# CVAT：计算机视觉标注工具（Computer Vision Annotation Tool） [![Release][release-img]][release-url] [![GitHub stars][stars-img]][stars-url] @@ -20,61 +26,61 @@ [Academy](https://www.cvat.ai/resources/academy) · [Blog](https://www.cvat.ai/resources/blog) -## What is CVAT Community? +## 什么是 CVAT Community？ -**CVAT Community** is the free, self-hosted open-source edition of [CVAT](https://www.cvat.ai/) — one of -the most widely used data annotation platforms for building high-quality visual datasets for -computer vision and visual AI. -Since 2018, CVAT has become one of the best-known data annotation tools in computer vision, with a -large open-source community, millions of Docker pulls, and broad adoption across research and -production AI teams. +**CVAT Community** 是 [CVAT](https://www.cvat.ai/) 的免费、可自托管开源版本，也是 +面向计算机视觉与视觉 AI 构建高质量视觉数据集时 +最广泛使用的数据标注平台之一。 +自 2018 年以来，CVAT 已成为计算机视觉领域最知名的数据标注工具之一，拥有 +庞大的开源社区、数百万次 Docker 拉取，并在科研与 +生产 AI 团队中得到广泛采用。 -CVAT Community supports image, video, and 3D annotation, dataset management, team collaboration, cloud storage -integration, developer-friendly SDKs and APIs, and gives your team full control over your data -and annotation infrastructure. -The platform serves as the foundation of -[CVAT Online](https://www.cvat.ai/pricing/cvat-online) and -[CVAT Enterprise](https://www.cvat.ai/enterprise), and is actively maintained by the CVAT engineering team. +CVAT Community 支持图像、视频和 3D 标注、数据集管理、团队协作、云存储 +集成、对开发者友好的 SDK 与 API，让你的团队完全掌控数据 +与标注基础设施。 +该平台是 +[CVAT Online](https://www.cvat.ai/pricing/cvat-online) 与 +[CVAT Enterprise](https://www.cvat.ai/enterprise), 的基础，并由 CVAT 工程团队积极维护。 -Why teams choose CVAT Community: +团队为何选择 CVAT Community： -- **Own your data:** Run entirely within your own infrastructure. No data leaves your environment. -- **AI-powered annotation:** Connect your own ML models for detection, segmentation, and tracking to speed up labeling. -- **Team collaboration:** Multi-user and multi-organization support with roles, task assignments, - and review workflows. -- **MIT-licensed core:** Use, modify, and distribute CVAT Community under the permissive MIT License. Some serverless -assets and dependencies may have separate licenses. -- **Production-grade:** The foundation of all CVAT commercial products — battle-tested at scale. -- **True open-source:** Transparent development, active community, on GitHub since 2018. +- **掌控你的数据：** 完全在你的自有基础设施中运行。数据不会离开你的环境。 +- **AI 驱动的标注：** 接入你自己的 ML 模型，用于检测、分割和跟踪，以加快标注速度。 +- **团队协作：** 支持多用户与多组织，提供角色、任务分配 + 与审核工作流。 +- **MIT 许可的核心：** 在宽松的 MIT License 下使用、修改和分发 CVAT Community。部分 serverless + 资产与依赖可能适用单独许可证。 +- **生产级：** 所有 CVAT 商业产品的基础——经大规模实战检验。 +- **真正的开源：** 开发过程透明、社区活跃，自 2018 年起托管于 GitHub。 -This repository contains the source code and deployment assets for CVAT Community. +本仓库包含 CVAT Community 的源代码与部署资源。 -For a fully managed setup, annotation services, or enterprise features, see +如需全托管部署、标注服务或企业级功能，请参阅 [CVAT Online](https://www.cvat.ai/pricing/cvat-online), -[CVAT Enterprise](https://www.cvat.ai/enterprise) and +[CVAT Enterprise](https://www.cvat.ai/enterprise) 与 [CVAT Labeling Services](https://www.cvat.ai/annotation-services). -## Getting Started +## 快速开始 -> 💡 Want to explore CVAT before deploying anything? -> **[Try CVAT Online (Free plan)](https://app.cvat.ai)** directly in your browser. -> Feature availability and usage limits vary by plan; see -> [CVAT Online pricing](https://www.cvat.ai/pricing/cvat-online) for details. +> 💡 想在部署任何东西之前先体验 CVAT 吗？ +> **[免费试用 CVAT Online](https://app.cvat.ai)**，直接在浏览器中使用。 +> 功能可用性与使用限制因套餐而异；详见 +> [CVAT Online 定价](https://www.cvat.ai/pricing/cvat-online)。 -### Installation +### 安装 -**Prerequisites:** +**前置条件：** - [Docker Engine](https://docs.docker.com/engine/install/) - [Docker Compose](https://docs.docker.com/compose/install/) - [Git](https://git-scm.com/) -> 💡 CVAT is primarily tested with Chromium-based browsers (Google Chrome, Microsoft Edge). -> Firefox may work with some caveats; Safari/WebKit is not supported. +> 💡 CVAT 主要在基于 Chromium 的浏览器（Google Chrome、Microsoft Edge）上测试。 +> Firefox 可能可用，但存在一些限制；不支持 Safari/WebKit。 -**1. Start the default stack** +**1. 启动默认技术栈** -Clone the repository and launch the services. +克隆仓库并启动服务。 ```bash git clone https://github.com/cvat-ai/cvat @@ -86,69 +92,68 @@ cd cvat docker compose up -d ``` -**2. Create an admin account** +**2. 创建管理员账户** ```bash docker exec -it cvat_server bash -ic 'python3 ~/manage.py createsuperuser' ``` -See the [Installation Guide](https://docs.cvat.ai/docs/administration/community/basics/installation/) for full -instructions and OS-specific setup. +完整说明与各操作系统专属设置请参阅 [安装指南](https://docs.cvat.ai/docs/administration/community/basics/installation/)。 -**3. Sign in and start labeling** +**3. 登录并开始标注** -- Open [http://localhost:8080](http://localhost:8080) (or your `CVAT_HOST`) in your browser. -- Log in with your superuser account. -- Create a project or task, upload your data (images, videos, or point clouds), and define labels to start annotating. +- 在浏览器中打开 [http://localhost:8080](http://localhost:8080)（或你的 `CVAT_HOST`）。 +- 使用超级用户账户登录。 +- 创建项目或任务，上传数据（图像、视频或点云），并定义标签以开始标注。 -Learn more about annotation tools and workflows in the [CVAT Documentation](https://docs.cvat.ai/docs/) or -take our free course – [CVAT Academy](https://www.cvat.ai/resources/academy). +在 [CVAT 文档](https://docs.cvat.ai/docs/) 中了解更多标注工具与工作流，或 +参加我们的免费课程——[CVAT Academy](https://www.cvat.ai/resources/academy). -_For alternative deployments (AWS, Kubernetes, external PostgreSQL, backups, upgrades), see the [Deployment Guides](https://docs.cvat.ai/docs/administration/community/advanced/)._ +_如需其他部署方式（AWS、Kubernetes、外部 PostgreSQL、备份、升级），请参阅 [部署指南](https://docs.cvat.ai/docs/administration/community/advanced/).__ -## Key Capabilities +## 核心能力 -- **[Manual & Auto-labeling](https://docs.cvat.ai/docs/annotation/manual-annotation/):** Annotate images, videos, and - 3D point clouds with bounding boxes, polygons, masks, keypoints, cuboids, tags, and more. Speed up labeling - by connecting your own models for automatic annotation. -- **[Task Management](https://docs.cvat.ai/docs/workspace/):** Organize datasets into projects, split them into tasks - and jobs, assign work to annotators, and track progress in real time. -- **[Collaboration](https://docs.cvat.ai/docs/account_management/user-roles/):** Create organizations, invite teammates, - assign roles, and collaborate on annotations with comments and issues. -- **[Quality Control](https://docs.cvat.ai/docs/qa-analytics/manual-qa/):** Review annotations, flag issues, compare - results across annotators with consensus, and run Ground Truth and Honeypot checks through the server API. -- **[Analytics](https://docs.cvat.ai/docs/administration/community/advanced/analytics/):** Monitor user activity, - working time by job, events, and server logs with Grafana dashboards. -- **[Data Ops & Integrations](https://docs.cvat.ai/docs/dataset_management/export-datasets/):** Export/import in 20+ - formats (COCO, YOLO, Pascal VOC, KITTI, etc.), connect to cloud storage (S3, Azure, Google Cloud), and automate - via REST API and Python SDK. +- **[手动与自动标注](https://docs.cvat.ai/docs/annotation/manual-annotation/):** 对图像、视频和 + 3D 点云进行边界框、多边形、掩码、关键点、立方体、标签等标注。接入你自己的模型进行自动标注， + 以加快标注速度。 +- **[任务管理](https://docs.cvat.ai/docs/workspace/):** 将数据集组织为项目，拆分为任务 + 与作业，分配给标注员，并实时跟踪进度。 +- **[协作](https://docs.cvat.ai/docs/account_management/user-roles/):** 创建组织、邀请队友、 + 分配角色，并通过评论与 issue 协作完成标注。 +- **[质量控制](https://docs.cvat.ai/docs/qa-analytics/manual-qa/):** 审核标注、标记问题，通过共识（consensus）比较 + 不同标注员的结果，并通过服务器 API 运行 Ground Truth 与 Honeypot 检查。 +- **[分析](https://docs.cvat.ai/docs/administration/community/advanced/analytics/):** 通过 Grafana 仪表板监控用户活动、 + 各作业工时、事件与服务器日志。 +- **[数据运维与集成](https://docs.cvat.ai/docs/dataset_management/export-datasets/):** 支持 20+ + 种格式导入/导出（COCO、YOLO、Pascal VOC、KITTI 等），连接云存储（S3、Azure、Google Cloud），并通过 + REST API 与 Python SDK 实现自动化。 -Advanced capabilities such as advanced project analytics, quality control UI, built-in auto-labeling with SAM 2 - and SAM 3, AI agents, SSO, and more are available in [CVAT Online](https://www.cvat.ai/pricing/cvat-online) - paid plans (Solo, Team) and [CVAT Enterprise](https://www.cvat.ai/enterprise). +高级项目分析、质量控制 UI、内置 SAM 2 + 与 SAM 3 自动标注、AI agents、SSO 等高级能力可在 [CVAT Online](https://www.cvat.ai/pricing/cvat-online) + 付费套餐（Solo、Team）与 [CVAT Enterprise](https://www.cvat.ai/enterprise). 中获取。 -## Developer Tools +## 开发者工具 -CVAT is designed for automation. Beyond the Web UI, you can integrate it into your pipelines using: +CVAT 面向自动化而设计。除 Web UI 外，你还可以通过以下方式将其集成到流水线中： -- [Python SDK](https://docs.cvat.ai/docs/api_sdk/sdk/): install with `pip install cvat-sdk` and automate task creation, -uploads, and exports from Python. -- [Command line tool](https://docs.cvat.ai/docs/api_sdk/cli/): install with `pip install cvat-cli` -and script common CVAT workflows from the terminal. -- [REST API](https://docs.cvat.ai/docs/api_sdk/api/): full programmatic control over CVAT. +- [Python SDK](https://docs.cvat.ai/docs/api_sdk/sdk/): 使用 `pip install cvat-sdk` 安装，从 Python 自动化创建任务、 + 上传与导出。 +- [命令行工具](https://docs.cvat.ai/docs/api_sdk/cli/): 使用 `pip install cvat-cli` + 安装，在终端中编写常见 CVAT 工作流脚本。 +- [REST API](https://docs.cvat.ai/docs/api_sdk/api/): 对 CVAT 进行完整的程序化控制。 -## Data and Formats +## 数据与格式 -CVAT Community supports image, video, and 3D (point cloud) annotation workflows. You can move data in and out using 20+ -industry-standard formats: CVAT (XML), COCO (JSON), YOLO (TXT), Ultralytics YOLO (TXT/YAML), Pascal VOC (XML), -KITTI (TXT), MOT (TXT), and more. +CVAT Community 支持图像、视频和 3D（点云）标注工作流。你可以使用 20+ + 种行业标准格式导入和导出数据：CVAT (XML)、COCO (JSON)、YOLO (TXT)、Ultralytics YOLO (TXT/YAML)、Pascal VOC (XML)、 + KITTI (TXT)、MOT (TXT) 等。 -[Full list of supported formats.](https://docs.cvat.ai/docs/dataset_management/formats/) +[支持格式的完整列表。](https://docs.cvat.ai/docs/dataset_management/formats/) -## ML and AI Models +## ML 与 AI 模型 -CVAT Community supports automatic annotation via pre-built serverless models powered by Nuclio, -covering detection, segmentation, pose estimation, and tracking: +CVAT Community 通过由 Nuclio 驱动的预构建 serverless 模型支持自动标注， +涵盖检测、分割、姿态估计与跟踪： | Model | Framework | Type | | --- | --- | --- | @@ -162,69 +167,64 @@ covering detection, segmentation, pose estimation, and tracking: | [Face Detection 0205](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/omz/intel/face-detection-0205/nuclio) | OpenVINO | Detector | | [Faster RCNN Inception v2](https://github.com/cvat-ai/cvat/tree/develop/serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio) | TensorFlow | Detector | -To enable automatic annotation, add the serverless component to your deployment: +要启用自动标注，请在你的部署中添加 serverless 组件： ```bash docker compose -f docker-compose.yml -f components/serverless/docker-compose.serverless.yml up -d ``` -This starts the serverless infrastructure. To make models available in CVAT, install `nuctl` and deploy -the functions you need, for example SAM or YOLO, as described in the [Automatic Annotation Guide](https://docs.cvat.ai/docs/annotation/auto-annotation/automatic-annotation/). +这将启动 serverless 基础设施。若要让模型在 CVAT 中可用，请安装 `nuctl` 并部署 +你所需的函数，例如 SAM 或 YOLO，详见[自动标注指南](https://docs.cvat.ai/docs/annotation/auto-annotation/automatic-annotation/). -## Which CVAT edition should I choose? +## 我应该选择哪个 CVAT 版本？ -- **CVAT Online**: the fastest way to try CVAT and start labeling without deployment. Use it to evaluate CVAT in -the browser, explore managed features, and move to cost-efficient paid plans when you need more capacity or team -workflows. -- **CVAT Community**: the MIT-licensed self-hosted edition for teams that want to run CVAT themselves, customize the -stack, and control their infrastructure. -- **CVAT Enterprise**: for organizations that need CVAT in their own cloud or internal environment, enterprise support, -security controls such as SSO, paid platform features, and SLAs. -- **Labeling Services**: for teams that want to outsource annotation work to CVAT.ai’s experienced labeling team instead -of building an internal labeling operation. Customers get trial access to CVAT Online during the project. +- **CVAT Online**：无需部署即可最快试用 CVAT 并开始标注的方式。可用于在浏览器中评估 CVAT、探索托管功能，并在需要更大容量或团队协作工作流时迁移到高性价比的付费方案。 +- **CVAT Community**：采用 MIT 许可证的自托管版本，适合希望自行运行 CVAT、定制技术栈并掌控基础设施的团队。 +- **CVAT Enterprise**：面向需要在自有云或内部环境运行 CVAT 的组织，提供企业级支持、SSO 等安全控制、付费平台功能以及 SLA。 +- **Labeling Services（标注服务）**：面向希望将标注工作外包给 CVAT.ai 经验丰富的标注团队、而非自建内部标注团队的客户。客户在项目期间可获得 CVAT Online 的试用访问权限。 -For detailed plan limits and feature availability, see [CVAT Online pricing](https://www.cvat.ai/pricing/cvat-online), - [CVAT Enterprise](https://www.cvat.ai/enterprise), and [Labeling Services](https://www.cvat.ai/annotation-services). +有关各方案的详细额度限制与功能可用性，请参阅 [CVAT Online 定价](https://www.cvat.ai/pricing/cvat-online), + [CVAT Enterprise](https://www.cvat.ai/enterprise), 和 [Labeling Services（标注服务）](https://www.cvat.ai/annotation-services). -## Support +## 支持 -- **Usage questions:** ask the community on [Discord](https://discord.com/invite/fNR3eXfk6C) or -Stack Overflow with the `cvat` tag. -- **Bugs and feature requests:** use [GitHub Issues](https://github.com/cvat-ai/cvat/issues). -- **FAQ:** [Installation, upgrades, troubleshooting](https://docs.cvat.ai/docs/faq/). +- **使用问题：** 可在 [Discord](https://discord.com/invite/fNR3eXfk6C) 向社区提问，或在 +Stack Overflow 上使用 `cvat` 标签提问。 +- **缺陷与功能请求：** 请使用 [GitHub Issues](https://github.com/cvat-ai/cvat/issues). +- **常见问题：** [安装、升级、故障排除](https://docs.cvat.ai/docs/faq/). -For dedicated support, SLAs, or advanced deployments, consider [CVAT Enterprise](https://www.cvat.ai/enterprise). +如需专属支持、SLA 或高级部署，请考虑 [CVAT Enterprise](https://www.cvat.ai/enterprise). -## Contributing +## 贡献 -We welcome all contributions: bug reports, documentation fixes, integrations, and code. +我们欢迎各类贡献：缺陷报告、文档修复、集成与代码。 -- If you'd like to contribute to CVAT, please refer to our - [contribution documentation](https://docs.cvat.ai/docs/contributing/). -- For bug reports or feature requests, please use the [GitHub Issues](https://github.com/cvat-ai/cvat/issues) tracker. +- 若要为 CVAT 做贡献，请参阅我们的 + [贡献文档](https://docs.cvat.ai/docs/contributing/). +- 缺陷报告或功能请求请使用 [GitHub Issues](https://github.com/cvat-ai/cvat/issues) 跟踪器。 -## Security +## 安全 -- Please review our [Security Policy](https://github.com/cvat-ai/cvat/security/policy) before reporting vulnerabilities. -- For sensitive issues, contact: [secure@cvat.ai](mailto:secure@cvat.ai). +- 在报告漏洞前，请先阅读我们的[安全政策](https://github.com/cvat-ai/cvat/security/policy)。 +- 敏感问题请联系：[secure@cvat.ai](mailto:secure@cvat.ai)。 -## License +## 许可证 -CVAT Community is released under the MIT License. +CVAT Community 在 MIT 许可证下发布。 -- Code in `/serverless` is also MIT-licensed, but may use third-party assets under separate licenses (including - non-commercial). Review those licenses before use. -- This software uses FFmpeg libraries under LGPL/GPL. See the Dockerfile and - [FFmpeg legal info](https://www.ffmpeg.org/legal.html) for details. +- `/serverless` 中的代码同样采用 MIT 许可证，但可能包含依据单独许可证（包括 + 非商业用途限制）的第三方资源。使用前请查阅相关许可证。 +- 本软件在 LGPL/GPL 下使用 FFmpeg 库。详见 Dockerfile 与 + [FFmpeg 法律信息](https://www.ffmpeg.org/legal.html)。 -## Additional Resources +## 更多资源 -For the latest product releases, feature walkthroughs, and all things CVAT see: +要了解最新产品发布、功能演示及 CVAT 相关资讯，请参阅： - + - +
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