[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-supervisely--supervisely":3,"tool-supervisely--supervisely":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",143909,2,"2026-04-07T11:33:18",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107888,"2026-04-06T11:32:50",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":10,"last_commit_at":59,"category_tags":60,"status":17},4487,"LLMs-from-scratch","rasbt\u002FLLMs-from-scratch","LLMs-from-scratch 是一个基于 PyTorch 的开源教育项目，旨在引导用户从零开始一步步构建一个类似 ChatGPT 的大型语言模型（LLM）。它不仅是同名技术著作的官方代码库，更提供了一套完整的实践方案，涵盖模型开发、预训练及微调的全过程。\n\n该项目主要解决了大模型领域“黑盒化”的学习痛点。许多开发者虽能调用现成模型，却难以深入理解其内部架构与训练机制。通过亲手编写每一行核心代码，用户能够透彻掌握 Transformer 架构、注意力机制等关键原理，从而真正理解大模型是如何“思考”的。此外，项目还包含了加载大型预训练权重进行微调的代码，帮助用户将理论知识延伸至实际应用。\n\nLLMs-from-scratch 特别适合希望深入底层原理的 AI 开发者、研究人员以及计算机专业的学生。对于不满足于仅使用 API，而是渴望探究模型构建细节的技术人员而言，这是极佳的学习资源。其独特的技术亮点在于“循序渐进”的教学设计：将复杂的系统工程拆解为清晰的步骤，配合详细的图表与示例，让构建一个虽小但功能完备的大模型变得触手可及。无论你是想夯实理论基础，还是为未来研发更大规模的模型做准备",90106,"2026-04-06T11:19:32",[35,15,13,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":64,"owner_name":72,"owner_avatar_url":73,"owner_bio":74,"owner_company":75,"owner_location":75,"owner_email":76,"owner_twitter":75,"owner_website":77,"owner_url":78,"languages":79,"stars":107,"forks":108,"last_commit_at":109,"license":110,"difficulty_score":111,"env_os":112,"env_gpu":112,"env_ram":112,"env_deps":113,"category_tags":118,"github_topics":119,"view_count":111,"oss_zip_url":75,"oss_zip_packed_at":75,"status":17,"created_at":124,"updated_at":125,"faqs":126,"releases":157},3692,"supervisely\u002Fsupervisely","supervisely","Supervisely SDK for Python - convenient way to automate, customize and extend Supervisely Platform for your computer vision task ","Supervisely 是一个专为计算机视觉任务打造的开源平台及其 Python SDK，旨在帮助开发者高效地自动化、定制和扩展数据标注与模型训练流程。它解决了传统视觉项目中数据处理繁琐、工具链分散以及协作困难等痛点，提供了一站式的解决方案。\n\n这款工具非常适合计算机视觉工程师、算法研究人员以及需要构建定制化数据管道的开发团队使用。通过 Supervisely，用户不仅能利用其强大的在线平台进行图像和视频的智能标注与管理，还能借助简洁易用的 Python SDK 编写脚本，轻松实现从数据清洗到模型评估的全流程自动化。\n\n其独特的技术亮点在于极具灵活性的应用开发生态。开发者可以从简单的 REST API 调用起步，逐步升级为拥有独立交互界面甚至嵌入标注工具内部的复杂应用。Supervisely 提供了丰富的现成 UI 组件，让开发者能快速构建带有图形界面的工具，并支持一键部署和可靠的版本管理。无论是私有内部工具还是公开共享的应用，都能在该生态中轻松实现，极大地提升了计算机视觉项目的开发效率与协作体验。","\u003Ch1 align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fsupervisely.com\">\u003Cimg alt=\"Supervisely\" title=\"Supervisely\" src=\"https:\u002F\u002Fi.imgur.com\u002FB276eMS.png\">\u003C\u002Fa>\n\u003C\u002Fh1>\n\n\u003Ch3 align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fsupervisely.com\">Computer Vision Platform\u003C\u002Fa>, \n\u003Ca href=\"https:\u002F\u002Fecosystem.supervisely.com\u002F\">Open Ecosystem of Apps\u003C\u002Fa>,\n\u003Ca href=\"https:\u002F\u002Fdeveloper.supervisely.com\u002F\">SDK for Python\u003C\u002Fa>\n\u003C\u002Fh3>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fsupervisely\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_b69da936003d.png\" alt=\"Package version\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fhub.docker.com\u002Fr\u002Fsupervisely\u002Fagent\u002Ftags\" target=\"_blank\">\n    \u003Cimg alt=\"Docker Pulls\" src=\"https:\u002F\u002Fimg.shields.io\u002Fdocker\u002Fpulls\u002Fsupervisely\u002Fagent?label=docker%20pulls%20-%20supervisely%2Fagent\">\n  \u003C\u002Fa>\n  \u003Cbr\u002F>\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fsupervisely\" target=\"_blank\">\n    \u003Cimg alt=\"PyPI - Python Version\" src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Fsupervisely?color=4ec528\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fsupervisely.com\u002Fslack\" target=\"_blank\"> \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fslack-chat-green.svg?logo=slack&color=4ec528\" alt=\"Slack\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fsupervisely\" target=\"_blank\"> \n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fsupervisely?color=4ec528&label=pypi%20package\" alt=\"Package version\"> \n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fdeveloper.supervisely.com\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_13d664e1afd7.png\">\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\n---\n\n**Website**: [https:\u002F\u002Fsupervisely.com](https:\u002F\u002Fsupervisely.com\u002F)\n\n**Supervisely Ecosystem**: [https:\u002F\u002Fecosystem.supervisely.com](https:\u002F\u002Fecosystem.supervisely.com\u002F)\n\n**Dev Documentation**: [https:\u002F\u002Fdeveloper.supervisely.com](https:\u002F\u002Fdeveloper.supervisely.com\u002F)\n\n**Source Code of SDK for Python**: [https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely](https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely)\n\n**Supervisely Ecosystem on GitHub**: [https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem](https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem)\n\n**Complete video course on YouTube**: [What is Supervisely?](https:\u002F\u002Fsupervisely.com\u002Fwhat-is-supervisely\u002F#0)\n---\n\n## Table of contents\n\n- [**Complete video course on YouTube**: What is Supervisely?](#complete-video-course-on-youtube-what-is-supervisely)\n- [Table of contents](#table-of-contents)\n- [Introduction](#introduction)\n  - [Supervisely Platform 🔥](#supervisely-platform-)\n  - [Supervisely Ecosystem 🎉](#supervisely-ecosystem-)\n- [Development 🧑‍💻](#development-)\n  - [What developers can do](#what-developers-can-do)\n    - [Level 1. HTTP REST API](#level-1-http-rest-api)\n    - [Level 2. Python scripts for automation and integration](#level-2-python-scripts-for-automation-and-integration)\n    - [Level 3. Headless apps (without UI)](#level-3-headless-apps-without-ui)\n    - [Level 4. Apps with interactive UIs](#level-4-apps-with-interactive-uis)\n    - [Level 5. Apps with UI integrated into labeling tools](#level-5-apps-with-ui-integrated-into-labeling-tools)\n  - [Principles 🧭](#principles-)\n- [Main features 💎](#main-features-)\n  - [Start in a minute](#start-in-a-minute)\n  - [Magically simple API](#magically-simple-api)\n  - [Customization is everywhere](#customization-is-everywhere)\n  - [Interactive GUI is a game-changer](#interactive-gui-is-a-game-changer)\n  - [Develop fast with ready UI widgets](#develop-fast-with-ready-ui-widgets)\n  - [Convenient debugging](#convenient-debugging)\n  - [Apps can be both private and public](#apps-can-be-both-private-and-public)\n  - [Single-click deployment](#single-click-deployment)\n  - [Reliable versioning - releases and branches](#reliable-versioning---releases-and-branches)\n  - [Supports both Github and Gitlab](#supports-both-github-and-gitlab)\n  - [App is just a web server, use any technology you love](#app-is-just-a-web-server-use-any-technology-you-love)\n  - [Built-in cloud development environment (coming soon)](#built-in-cloud-development-environment-coming-soon)\n  - [Trusted by Fortune 500. Used by 65 000 researchers, developers, and companies worldwide](#trusted-by-fortune-500-used-by-65-000-researchers-developers-and-companies-worldwide)\n- [Community 🌎](#community-)\n    - [Have an idea or ask for help?](#have-an-idea-or-ask-for-help)\n- [Contribution 👏](#contribution-)\n- [Partnership 🤝](#partnership-)\n- [Cite this Project](#cite-this-project)\n\n\n\n## Introduction\n\nEvery company wants to be sure that its current and future AI tasks are solvable.\n\nThe main issue with most solutions on the market is that they build as products. It's a black box developing by some company you don't really have an impact on. As soon as your requirements go beyond basic features offered and you want to customize your experience, add something that is not in line with the software owner development plans or won't benefit other customers, you're out of luck.\n\nThat is why **Supervisely is building a platform** instead of a product.\n\n### [Supervisely Platform 🔥](https:\u002F\u002Fsupervisely.com\u002F)\n\n\u003Ca href=\"https:\u002F\u002Fsupervisely.com\u002F\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_dca5902e035d.png\" style=\"max-width:100%;\"\n  alt=\"Supervisely Platform\">\n\u003C\u002Fa>\n\nYou can think of [Supervisely](https:\u002F\u002Fsupervisely.com\u002F) as an Operating System available via Web Browser to help you solve Computer Vision tasks. The idea is to unify all the relevant tools within a single [Ecosystem](https:\u002F\u002Fecosystem.supervisely.com\u002F) of apps, tools, UI widgets and services that may be needed to make the AI development process as smooth and fast as possible.\n\nMore concretely, Supervisely includes the following functionality:\n\n* Data labeling for images, videos, 3D point cloud and volumetric medical images (dicom)\n* Data visualization and quality control\n* State-Of-The-Art Deep Learning models for segmentation, detection, classification and other tasks\n* Interactive tools for model performance analysis\n* Specialized Deep Learning models to speed up data labeling (aka AI-assisted labeling)\n* Synthetic data generation tools\n* Instruments to make it easier to collaborate for data scientists, data labelers, domain experts and software engineers\n\n### [Supervisely Ecosystem](https:\u002F\u002Fecosystem.supervisely.com\u002F) 🎉\n\n\n\u003Ca href=\"https:\u002F\u002Fecosystem.supervisely.com\u002F\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_610a6166fedd.png\" style=\"max-width:100%;\"\n  alt=\"Supervisely Platform\">\n\u003C\u002Fa>\n\nThe simplicity of creating Supervisely Apps has already led to the development of [hundreds of applications](https:\u002F\u002Fecosystem.supervisely.com\u002F), ready to be run within a single click in a web browser and get the job done.\n\nLabel your data, perform quality assurance, inspect every aspect of your data, collaborate easily, train and apply state-of-the-art neural networks, integrate custom models, automate routine tasks and more - like in a real AppStore, there should be an app for everything.\n\n## [Development](https:\u002F\u002Fdeveloper.supervisely.com\u002F) 🧑‍💻\n\nSupervisely provides the foundation for integration, customization, development and running computer vision applications to address your custom tasks - just like in OS, like Windows or MacOS.\n\n### What developers can do\n\nThere are different levels of integration, customization, and automation:\n\n1. [HTTP REST API](#level-1-http-rest-api)\n2. [Python scripts for automation and integration](#level-2-python-scripts-for-automation-and-integration)\n3. [Headless apps (without UI)](#level-3-headless-apps-without-ui)\n4. [Apps with interactive UIs](#level-4-apps-with-interactive-uis)\n5. [Apps with UIs integrated into labeling tools](#level-5-apps-with-ui-integrated-into-labeling-tools)\n\n#### Level 1. HTTP REST API\n\nSupervisely has a rich [HTTP REST API](https:\u002F\u002Fapi.docs.supervisely.com\u002F) that covers basically every action, you can do manually. You can use **any programming language** and **any development environment** to extend and customize your Supervisely experience.\n\nℹ️ For Python developers, we recommend using our [Python SDK](https:\u002F\u002Fsupervisely.readthedocs.io\u002Fen\u002Flatest\u002Fsdk\\_packages.html) because it wraps up all API methods and can save you a lot of time with built-in error handling, network re-connection, response validation, request pagination, and so on.\n\n\u003Cdetails>\n\n\u003Csummary>cURL example\u003C\u002Fsummary>\n\nThere's no easier way to kick the tires than through [cURL](http:\u002F\u002Fcurl.haxx.se\u002F). If you are using an alternative client, note that you are required to send a valid header in your request.\n\nExample:\n\n```bash\ncurl -H \"x-api-key: \u003Cyour-token-here>\" https:\u002F\u002Fapp.supervisely.com\u002Fpublic\u002Fapi\u002Fv3\u002Fprojects.list\n```\n\nAs you can see, URL starts with `https:\u002F\u002Fapp.supervisely.com`. It is for Community Edition. For Enterprise Edition you have to use your custom server address.\n\n\u003C\u002Fdetails>\n\n#### Level 2. Python scripts for automation and integration\n\n[Supervisely SDK for Python](https:\u002F\u002Fsupervisely.readthedocs.io\u002Fen\u002Flatest\u002Fsdk\\_packages.html) is specially designed to speed up development, reduce boilerplate, and lets you do anything in a few lines of Python code with Supervisely Annotatation JSON format, communicate with the platform, import and export data, manage members, upload predictions from your models, etc.\n\n\u003Cdetails>\n\n\u003Csummary>Python SDK example\u003C\u002Fsummary>\n\nLook how it is simple to communicate with the platform from your python script.\n\n```python\nimport supervisely as sly\n\n# authenticate with your personal API token\napi = sly.Api.from_env()\n\n# create project and dataset\nproject = api.project.create(workspace_id=123, name=\"demo project\")\ndataset = api.dataset.create(project.id, \"dataset-01\")\n\n# upload data\nimage_info = api.image.upload_path(dataset.id, \"img.png\", \"\u002FUsers\u002Fmax\u002Fimg.png\")\napi.annotation.upload_path(image_info.id, \"\u002FUsers\u002Fmax\u002Fann.json\")\n\n# download data\nimg = api.image.download_np(image_info.id)\nann = api.annotation.download_json(image_info.id)\n```\n\n\u003C\u002Fdetails>\n\n#### Level 3. Headless apps (without UI)\n\nCreate python apps to automate routine and repetitive tasks, share them within your organization,  and provide an easy way to use them for end-users without coding background.  Headless apps are just python scripts that can be run from a context menu.\n\n![run app from context menu](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_0985ec216249.png)\n\nIt is simple and suitable for the most basic tasks and use-cases, for example:\n\n* import and export in custom format ([example1](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fimport-images-groups), [example2](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fexport-as-masks), [example3](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fexport-to-pascal-voc), [example4](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Frender-video-labels-to-mp4))\n* assets transformation ([example1](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Frasterize-objects-on-images), [example2](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fresize-images), [example3](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fchange-video-framerate), [example4](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fconvert\\_ptc\\_to\\_ptc\\_episodes))\n* users management ([example1](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Finvite-users-to-team-from-csv), [example2](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fcreate-users-from-csv), [example3](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fexport-activity-as-csv))\n* deploy special models for AI-assisted labeling ([example1](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fritm-interactive-segmentation%2Fsupervisely), [example2](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Ftrans-t%2Fsupervisely%2Fserve), [example3](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fvolume-interpolation))\n\n#### Level 4. Apps with interactive UIs\n\nInteractive interfaces and visualizations are the keys to building and improving AI solutions: from custom data labeling to model training. Such apps open up opportunities to customize Supervisely platform to any type of task in Computer Vision, implement data and models workflows that fit your organization's needs, and even build vertical solutions for specific industries on top of it.\n\n\u003Ca href=\"https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fdev-smart-tool-batched\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_324e7e2db160.gif\" style=\"max-width:100%;\"\n  alt=\"[This interface is completely based on python in combination with easy-to-use Supervisely UI widgets (Batched SmartTool app for AI assisted object segmentations)\">\n\u003C\u002Fa>\n\nHere are several examples:\n\n* custom labeling interfaces with AI assistance for [images](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fdev-smart-tool-batched) and [videos](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fbatched-smart-tool-for-videos)\n* [interactive model performance analysis](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsemantic-segmentation-metrics-dashboard)\n* [interactive NN training dashboard](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fmmsegmentation%2Ftrain)\n* [data exploration](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Faction-recognition-stats) and [visualization](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fobjects-thumbnails-preview-by-class) apps\n* [vertical solution](https:\u002F\u002Fecosystem.supervisely.com\u002Fcollections\u002Fsupervisely-ecosystem%2Fgl-metric-learning%2Fsupervisely%2Fretail-collection) for labeling products on shelves in retail\n* inference interfaces [in labeling tools](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fnn-image-labeling%2Fannotation-tool); for [images](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fnn-image-labeling%2Fproject-dataset), [videos](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fapply-nn-to-videos-project) and [point clouds](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fapply-det3d-to-project-dataset); for [model ensembles](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fapply-det-and-cls-models-to-project)\n\n#### Level 5. Apps with UI integrated into labeling tools\n\nThere is no single labeling tool that fits all tasks. Labeling tool has to be designed and customized for a specific task to make the job done in an efficient manner. Supervisely apps can be smoothly integrated into labeling tools to deliver amazing user experience (including multi tenancy) and annotation performance.\n\n\u003Ca href=\"https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%252Fgl-metric-learning%252Fsupervisely%252Flabeling-tool\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_42fbbaf38c97.png\" style=\"max-width:100%;\"\n  alt=\"[AI assisted retail labeling app is integrated into labeling tool and can communicate with it via web sockets)\">\n\u003C\u002Fa>\n\nHere are several examples:\n\n* apps designed for custom labeling workflows ([example1](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fvisual-tagging), [example2](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Freview-labels-side-by-side))\n* NN inference is integrated for labeling automation and model predictions analysis ([example](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fnn-image-labeling%2Fannotation-tool))\n* industry-specific labeling tool: annotation of thousands of product types on shelves with AI assistance ([retail collection](https:\u002F\u002Fecosystem.supervisely.com\u002Fcollections\u002Fsupervisely-ecosystem%2Fgl-metric-learning%2Fsupervisely%2Fretail-collection), [labeling app](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fai-assisted-classification))\n\n### Principles 🧭\n\nDevelopment for Supervisely builds upon these five principles:\n\n* All in **pure Python** and build on top of your favourites libraries (opencv, requests, fastapi, pytorch, imgaug, etc ...) - easy for python developers and data scientists to build and share apps with teammates and the ML community.\n* No front‑end experience is required -  build **powerful** and **interactive** web-based GUI apps using the comprehensive library of ready-to-use UI widgets and components.\n* **Easy to learn, fast to code,** and **ready for production**.  SDK provides a simple and intuitive API by having complexity \"under the hood\". Every action can be done just in a few lines of code. You focus on your task, Supervisely will handle everything else - interfaces, databases, permissions, security, cloud or self-hosted deployment, networking, data storage, and many more. Supervisely has solid testing, documentation, and support.\n* Everything is **customizable** - from labeling interfaces to neural networks. The platform has to be customized and extended to perfectly fit your tasks and requirements, not vice versa. Hundreds of examples cover every scenario and can be found in our [ecosystem of apps](https:\u002F\u002Fecosystem.supervisely.com\u002F).\n* Apps can be both **open-sourced or private**. All apps made by Supervisely team are [open-sourced](https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem). Use them as examples, just fork and modify the way you want. At the same time, customers and community users can still develop private apps to protect their intellectual property.\n\n## Main features 💎\n\n- [**Complete video course on YouTube**: What is Supervisely?](#complete-video-course-on-youtube-what-is-supervisely)\n- [Table of contents](#table-of-contents)\n- [Introduction](#introduction)\n  - [Supervisely Platform 🔥](#supervisely-platform-)\n  - [Supervisely Ecosystem 🎉](#supervisely-ecosystem-)\n- [Development 🧑‍💻](#development-)\n  - [What developers can do](#what-developers-can-do)\n    - [Level 1. HTTP REST API](#level-1-http-rest-api)\n    - [Level 2. Python scripts for automation and integration](#level-2-python-scripts-for-automation-and-integration)\n    - [Level 3. Headless apps (without UI)](#level-3-headless-apps-without-ui)\n    - [Level 4. Apps with interactive UIs](#level-4-apps-with-interactive-uis)\n    - [Level 5. Apps with UI integrated into labeling tools](#level-5-apps-with-ui-integrated-into-labeling-tools)\n  - [Principles 🧭](#principles-)\n- [Main features 💎](#main-features-)\n  - [Start in a minute](#start-in-a-minute)\n  - [Magically simple API](#magically-simple-api)\n  - [Customization is everywhere](#customization-is-everywhere)\n  - [Interactive GUI is a game-changer](#interactive-gui-is-a-game-changer)\n  - [Develop fast with ready UI widgets](#develop-fast-with-ready-ui-widgets)\n  - [Convenient debugging](#convenient-debugging)\n  - [Apps can be both private and public](#apps-can-be-both-private-and-public)\n  - [Single-click deployment](#single-click-deployment)\n  - [Reliable versioning - releases and branches](#reliable-versioning---releases-and-branches)\n  - [Supports both Github and Gitlab](#supports-both-github-and-gitlab)\n  - [App is just a web server, use any technology you love](#app-is-just-a-web-server-use-any-technology-you-love)\n  - [Built-in cloud development environment (coming soon)](#built-in-cloud-development-environment-coming-soon)\n  - [Trusted by Fortune 500. Used by 65 000 researchers, developers, and companies worldwide](#trusted-by-fortune-500-used-by-65-000-researchers-developers-and-companies-worldwide)\n- [Community 🌎](#community-)\n    - [Have an idea or ask for help?](#have-an-idea-or-ask-for-help)\n- [Contribution 👏](#contribution-)\n- [Partnership 🤝](#partnership-)\n- [Cite this Project](#cite-this-project)\n\n### Start in a minute\n\nSupervisely's open-source SDK and app framework are straightforward to get started with. It's just a matter of:\n\n```\npip install supervisely\n```\n\n### Magically simple API\n\n[Supervisely SDK for Python](https:\u002F\u002Fsupervisely.readthedocs.io\u002Fen\u002Flatest\u002Fsdk\\_packages.html) is simple, intuitive, and can save you hours. Reduce boilerplate and build custom integrations in a few lines of code. It has never been so easy to communicate with the platform from python.\n\n```python\n# authenticate with your personal API token\napi = sly.Api.from_env()\n\n# create project and dataset\nproject = api.project.create(workspace_id=123, name=\"demo project\")\ndataset = api.dataset.create(project.id, \"dataset-01\")\n\n# upload data\nimage_info = api.image.upload_path(dataset.id, \"img.png\", \"\u002FUsers\u002Fmax\u002Fimg.png\")\napi.annotation.upload_path(image_info.id, \"\u002FUsers\u002Fmax\u002Fann.json\")\n\n# download data\nimg = api.image.download_np(image_info.id)\nann = api.annotation.download_json(image_info.id)\n```\n\n### Customization is everywhere\n\nCustomization is the only way to cover all tasks in Computer Vision. Supervisely allows to customizing everything from labeling interfaces and context menus to training dashboards and inference interfaces. Check out our [Ecosystem of apps](https:\u002F\u002Fecosystem.supervisely.com\u002F) to find inspiration and examples for your next ML tool.\n\n### Interactive GUI is a game-changer\n\nThe majority of Python programs are \"command line\" based. While highly experienced programmers don't have problems with it, other tech people and end-users do.  This creates a digital divide, a \"GUI Gap\".  App with graphic user interface (GUI) becomes more approachable and easy to use to a wider audience. And finally, some tasks are impossible to solve without a GUI at all.\n\nImagine, how it will be great if all ML tools and repositories have an interactive GUI with the RUN button ▶️. It will take minutes to start working with a top Deep Learning framework instead of spending weeks running it on your data.  \n\n🎯 Our ambitious goal is to make it possible.\n\n\n\u003Ca href=\"https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsemantic-segmentation-metrics-dashboard\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_8847958de0db.gif\" style=\"max-width:100%;\"\n  alt=\"Semantic segmentation metrics app\">\n\u003C\u002Fa>\n\n\n### Develop fast with ready UI widgets\n\nHundreds of interactive UI widgets and components are ready for you. Just add to your program and populate with the data. Python devs don't need to have any front‑end experience, in our developer portal you will find needed guides, examples, and tutorials. We support the following UI widgets:\n\n1. [Widgets made by Supervisely](https:\u002F\u002Fdeveloper.supervisely.com\u002Fapp-development\u002Fwidgets) specifically for computer vision tasks, like rendering galleries of images with annotations, playing videos forward and backward with labels, interactive confusion matrices, tables, charts, ...\n2. [Element widgets](https:\u002F\u002Felement.eleme.io\u002F1.4\u002F#\u002Fen-US\u002Fcomponent\u002Fbutton) - Vue 2.0 based component library\n3. [Plotly](https:\u002F\u002Fplotly.com\u002Fpython\u002F) Graphing Library for Python\n4. [Develop your own custom widgets](https:\u002F\u002Fdeveloper.supervisely.com\u002Fapp-development\u002Fadvanced\u002Fhow-to-make-your-own-widget)\n\nSupervisely team makes most of its apps publically available on [GitHub](https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem). Use them as examples for your future apps: fork, modify, and copy-paste code snippets.\n\n### Convenient debugging\n\nSupervisely is made by data scientists for data scientists. We trying to lower barriers and make a friendly development environment. Especially we care about debugging as one of the most crucial steps.\n\nEven in complex scenarios, like developing a GUI app integrated into a labeling tool, we keep it simple - use breakpoints in your favorite IDE to catch callbacks, step through the program and see live updates without page reload. As simple as that! Supervisely handles everything else -  WebSockets, authentication, Redis, RabitMQ, Postgres, ...\n\nWatch the video below, how we debug [the app](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fnn-image-labeling%2Fannotation-tool) that applies NN right inside the labeling interface.\n\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FfOnyL8YHOBM\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_fdd32b1d6a01.png\" style=\"max-width:100%;\">\n\u003C\u002Fa>\n\n### Apps can be both private and public\n\nAll apps made by Supervisely team are [open-source](https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem). Use them as examples: find on [GitHub](https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem), fork and modify them the way you want. At the same time, customers and community users can still develop private apps to protect their intellectual property.\n\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FKyuc-lZu_tg\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_6bc9a1175b73.png\" style=\"max-width:100%;\">\n\u003C\u002Fa>\n\n### Single-click deployment\n\nSupervisely app is a git repository. Just provide the link to your git repo, Supervisely will handle everything else. Now you can press `Run` button in front of your app and start it on any computer with [Supervisely Agent](https:\u002F\u002Fyoutu.be\u002FaDqQiYycqyk).\n\n### Reliable versioning - releases and branches\n\nUsers run your app on the latest stable release, and you can develop and test new features in parallel - just use git releases and branches. Supervisely automatically pull updates from git, even if the new version of an app has a bug, don't worry - users can select and run the previous version in a click.\n\n\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FngoHfM98R8k\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_b303dacbcbae.png\" style=\"max-width:100%;\">\n\u003C\u002Fa>\n\n### Supports both Github and Gitlab\n\nSince Supervisely app is just a git repository, we support public and private repos from the most popular hosting platforms in the world - GitHub and GitLab.\n\n### App is just a web server, use any technology you love \n\nSupervisely SDK for Python provides the simplest way for python developers and data scientists to build interactive GUI apps of any complexity. Python is a recommended language for developing Supervisely apps, but not the only one. You can use any language or any technology you love, any web server can be deployed on top of the platform.\n\nFor example, even [Visual Studio Code for web](https:\u002F\u002Fgithub.com\u002Fcoder\u002Fcode-server) can be run as an app (see video below). \n\n### Built-in cloud development environment (coming soon)\n\nIn addition to the common way of development in your favorite IDE on your local computer or laptop, cloud development support will be integrated into Supervisely and **released soon** to speed up development, standardize dev environments, and lower barriers for beginners. \n\nHow will it work? Just connect your computer to your Supervisely instance and run IDE app ([JupyterLab](https:\u002F\u002Fjupyter.org\u002F) and [Visual Studio Code for web](https:\u002F\u002Fgithub.com\u002Fcoder\u002Fcode-server)) to start coding in a minute. We will provide a large number of template apps that cover the most popular use cases.\n\n\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FptHJsdolHHk\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_1c8b56db4b4d.png\" style=\"max-width:100%;\">\n\u003C\u002Fa>\n\n### Trusted by Fortune 500. Used by 65 000 researchers, developers, and companies worldwide\n\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_d581eb80030b.png)\n\nSupervisely helps companies and researchers all over the world to build their computer vision solutions in various industries from self-driving and agriculture to medicine. Join our [Community Edition](https:\u002F\u002Fapp.supervisely.com\u002F) or request [Enterprise Edition](https:\u002F\u002Fsupervisely.com\u002Fenterprise) for your organization.\n\n## Community 🌎\n\nJoin our constantly growing [Supervisely community](https:\u002F\u002Fapp.supervisely.com\u002F) with more than 65k+ users.\n\n#### Have an idea or ask for help?\n\nIf you have any questions, ideas or feedback please:\n\n1. [Suggest a feature or idea](https:\u002F\u002Fideas.supervisely.com\u002F), or [give a technical feedback ](https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fissues)\n2. [Join our slack](https:\u002F\u002Fsupervisely.com\u002Fslack)\n3. [Contact us](https:\u002F\u002Fsupervisely.com\u002Fcontact-us)\n\nYour feedback 👍 helps us a lot and we appreciate it\n\n## Contribution 👏\n\nWant to help us bring Computer Vision R\\&D to the next level? We encourage you to participate and speed up R\\&D for thousands of researchers by\n\n* building and expanding Supervisely Ecosystem with us\n* integrating to Supervisley and sharing your ML tools and research with the entire ML community\n\n## Partnership 🤝\n\nWe are happy to expand and increase the value of Supervisely Ecosystem with additional technological partners, researchers, developers, and value-added resellers.\n\nFeel free to [contact us](https:\u002F\u002Fsupervisely.com\u002Fcontact-us) if you have\n\n* ML service or product\n* unique domain expertise\n* vertical solution\n* valuable repositories and tools that solve the task\n* custom NN models and data\n\nLet's discuss the ways of working together, particularly if we have joint interests, technologies and  customers.\n\n## Cite this Project\n\nIf you use this project in a research, please cite it using the following BibTeX:\n\n```\n@misc{ supervisely,\n    title = { Supervisely Computer Vision platform },\n    type = { Computer Vision Tools },\n    author = { Supervisely },\n    howpublished = { \\url{ https:\u002F\u002Fsupervisely.com } },\n    url = { https:\u002F\u002Fsupervisely.com },\n    journal = { Supervisely Ecosystem },\n    publisher = { Supervisely },\n    year = { 2023 },\n    month = { jul },\n    note = { visited on 2023-07-20 },\n}\n```\n","\u003Ch1 align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fsupervisely.com\">\u003Cimg alt=\"Supervisely\" title=\"Supervisely\" src=\"https:\u002F\u002Fi.imgur.com\u002FB276eMS.png\">\u003C\u002Fa>\n\u003C\u002Fh1>\n\n\u003Ch3 align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fsupervisely.com\">计算机视觉平台\u003C\u002Fa>, \n\u003Ca href=\"https:\u002F\u002Fecosystem.supervisely.com\u002F\">开放的应用生态系统\u003C\u002Fa>,\n\u003Ca href=\"https:\u002F\u002Fdeveloper.supervisely.com\u002F\">Python SDK\u003C\u002Fa>\n\u003C\u002Fh3>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fsupervisely\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_b69da936003d.png\" alt=\"软件包版本\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fhub.docker.com\u002Fr\u002Fsupervisely\u002Fagent\u002Ftags\" target=\"_blank\">\n    \u003Cimg alt=\"Docker 拉取次数\" src=\"https:\u002F\u002Fimg.shields.io\u002Fdocker\u002Fpulls\u002Fsupervisely\u002Fagent?label=docker%20pulls%20-%20supervisely%2Fagent\">\n  \u003C\u002Fa>\n  \u003Cbr\u002F>\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fsupervisely\" target=\"_blank\">\n    \u003Cimg alt=\"PyPI - Python 版本\" src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Fsupervisely?color=4ec528\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fsupervisely.com\u002Fslack\" target=\"_blank\"> \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fslack-chat-green.svg?logo=slack&color=4ec528\" alt=\"Slack\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fsupervisely\" target=\"_blank\"> \n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fsupervisely?color=4ec528&label=pypi%20package\" alt=\"软件包版本\"> \n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fdeveloper.supervisely.com\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_13d664e1afd7.png\">\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\n---\n\n**官网**: [https:\u002F\u002Fsupervisely.com](https:\u002F\u002Fsupervisely.com\u002F)\n\n**Supervisely 生态系统**: [https:\u002F\u002Fecosystem.supervisely.com](https:\u002F\u002Fecosystem.supervisely.com\u002F)\n\n**开发者文档**: [https:\u002F\u002Fdeveloper.supervisely.com](https:\u002F\u002Fdeveloper.supervisely.com\u002F)\n\n**Python SDK 源代码**: [https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely](https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely)\n\n**Supervisely 生态系统 GitHub 仓库**: [https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem](https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem)\n\n**YouTube 完整视频课程**: [什么是 Supervisely？](https:\u002F\u002Fsupervisely.com\u002Fwhat-is-supervisely\u002F#0)\n---\n\n## 目录\n\n- [**YouTube 完整视频课程**：什么是 Supervisely？](#complete-video-course-on-youtube-what-is-supervisely)\n- [目录](#table-of-contents)\n- [简介](#introduction)\n  - [Supervisely 平台 🔥](#supervisely-platform-)\n  - [Supervisely 生态系统 🎉](#supervisely-ecosystem-)\n- [开发 🧑‍💻](#development-)\n  - [开发者能做什么](#what-developers-can-do)\n    - [第 1 级：HTTP REST API](#level-1-http-rest-api)\n    - [第 2 级：用于自动化和集成的 Python 脚本](#level-2-python-scripts-for-automation-and-integration)\n    - [第 3 级：无界面应用（Headless）](#level-3-headless-apps-without-ui)\n    - [第 4 级：带有交互式 UI 的应用](#level-4-apps-with-interactive-uis)\n    - [第 5 级：UI 集成到标注工具中的应用](#level-5-apps-with-ui-integrated-into-labeling-tools)\n  - [原则 🧭](#principles-)\n- [核心功能 💎](#main-features-)\n  - [一分钟上手](#start-in-a-minute)\n  - [神奇简单的 API](#magically-simple-api)\n  - [随处可定制](#customization-is-everywhere)\n  - [交互式 GUI 是变革性因素](#interactive-gui-is-a-game-changer)\n  - [使用现成的 UI 组件快速开发](#develop-fast-with-ready-ui-widgets)\n  - [便捷调试](#convenient-debugging)\n  - [应用可以是私有的，也可以是公有的](#apps-can-be-both-private-and-public)\n  - [一键部署](#single-click-deployment)\n  - [可靠的版本控制——发布与分支](#reliable-versioning---releases-and-branches)\n  - [同时支持 GitHub 和 GitLab](#supports-both-github-and-gitlab)\n  - [应用本质上就是一个 Web 服务器，你可以使用任何你喜欢的技术](#app-is-just-a-web-server-use-any-technology-you-love)\n  - [内置云开发环境（即将推出）](#built-in-cloud-development-environment-coming-soon)\n  - [受到财富 500 强企业的信赖，全球已有 65,000 名研究人员、开发者和公司正在使用](#trusted-by-fortune-500-used-by-65-000-researchers-developers-and-companies-worldwide)\n- [社区 🌎](#community-)\n    - [有想法或需要帮助吗？](#have-an-idea-or-ask-for-help)\n- [贡献 👏](#contribution-)\n- [合作伙伴关系 🤝](#partnership-)\n- [引用本项目](#cite-this-project)\n\n\n\n## 简介\n\n每一家公司都希望确保其当前及未来的 AI 任务能够被解决。\n\n市场上大多数解决方案的主要问题在于，它们是以产品形式构建的。这就像一个黑盒，由某个你无法真正影响的公司开发。一旦你的需求超出了所提供的基本功能，想要自定义体验、添加一些不符合软件所有者开发计划或对其他客户没有益处的功能时，你就很难如愿了。\n\n这就是为什么 **Supervisely 构建的是一个平台**，而不是一款产品。\n\n### [Supervisely 平台 🔥](https:\u002F\u002Fsupervisely.com\u002F)\n\n\u003Ca href=\"https:\u002F\u002Fsupervisely.com\u002F\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_dca5902e035d.png\" style=\"max-width:100%;\"\n  alt=\"Supervisely 平台\">\n\u003C\u002Fa>\n\n你可以把 [Supervisely](https:\u002F\u002Fsupervisely.com\u002F) 看作是一个通过网页浏览器访问的操作系统，旨在帮助你解决计算机视觉任务。其理念是将所有相关的工具统一在一个 [生态系统](https:\u002F\u002Fecosystem.supervisely.com\u002F) 中，包括应用程序、工具、UI 组件和服务，以尽可能简化并加速 AI 开发流程。\n\n更具体地说，Supervisely 包含以下功能：\n\n* 图像、视频、3D 点云以及医学体积图像（DICOM）的数据标注\n* 数据可视化与质量控制\n* 用于分割、检测、分类等任务的最先进深度学习模型\n* 用于分析模型性能的交互式工具\n* 专门用于加速数据标注的深度学习模型（即 AI 辅助标注）\n* 合成数据生成工具\n* 便于数据科学家、数据标注员、领域专家和软件工程师协作的工具\n\n### [Supervisely 生态系统](https:\u002F\u002Fecosystem.supervisely.com\u002F) 🎉\n\n\n\u003Ca href=\"https:\u002F\u002Fecosystem.supervisely.com\u002F\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_610a6166fedd.png\" style=\"max-width:100%;\"\n  alt=\"Supervisely 平台\">\n\u003C\u002Fa>\n\n创建 Supervisely 应用的简便性已经催生了[数百个应用](https:\u002F\u002Fecosystem.supervisely.com\u002F)，只需在网页浏览器中点击一下即可运行，轻松完成任务。\n\n标记数据、进行质量保证、检查数据的各个方面、轻松协作、训练并应用最先进的神经网络、集成自定义模型、自动化日常任务等等——就像真正的 AppStore 一样，几乎每项需求都有相应的应用。\n\n## [开发](https:\u002F\u002Fdeveloper.supervisely.com\u002F) 🧑‍💻\n\nSupervisely 提供了集成、定制、开发和运行计算机视觉应用的基础，以满足您的特定需求——就像操作系统一样，比如 Windows 或 macOS。\n\n### 开发者可以做什么\n\n集成、定制和自动化有不同的层次：\n\n1. [HTTP REST API](#level-1-http-rest-api)\n2. [用于自动化和集成的 Python 脚本](#level-2-python-scripts-for-automation-and-integration)\n3. [无界面应用（无 UI）](#level-3-headless-apps-without-ui)\n4. [带有交互式 UI 的应用](#level-4-apps-with-interactive-uis)\n5. [与标注工具集成的 UI 应用](#level-5-apps-with-ui-integrated-into-labeling-tools)\n\n#### 第 1 层：HTTP REST API\n\nSupervisely 拥有丰富的 [HTTP REST API](https:\u002F\u002Fapi.docs.supervisely.com\u002F)，几乎涵盖了您手动执行的每一个操作。您可以使用**任何编程语言**和**任何开发环境**来扩展和定制您的 Supervisely 使用体验。\n\nℹ️ 对于 Python 开发者，我们推荐使用我们的 [Python SDK](https:\u002F\u002Fsupervisely.readthedocs.io\u002Fen\u002Flatest\u002Fsdk_packages.html)，因为它封装了所有 API 方法，并通过内置的错误处理、网络重连、响应验证、请求分页等功能，能够为您节省大量时间。\n\n\u003Cdetails>\n\n\u003Csummary>cURL 示例\u003C\u002Fsummary>\n\n要快速试用，最简单的方法就是通过 [cURL](http:\u002F\u002Fcurl.haxx.se\u002F)。如果您使用其他客户端，请注意必须在请求中发送有效的头部信息。\n\n示例：\n\n```bash\ncurl -H \"x-api-key: \u003Cyour-token-here>\" https:\u002F\u002Fapp.supervisely.com\u002Fpublic\u002Fapi\u002Fv3\u002Fprojects.list\n```\n\n如您所见，URL 以 `https:\u002F\u002Fapp.supervisely.com` 开头，这是社区版的地址。如果是企业版，则需要使用您自定义的服务器地址。\n\n\u003C\u002Fdetails>\n\n#### 第 2 层：用于自动化和集成的 Python 脚本\n\n[Supervisely 的 Python SDK](https:\u002F\u002Fsupervisely.readthedocs.io\u002Fen\u002Flatest\u002Fsdk_packages.html) 专为加速开发、减少样板代码而设计，让您只需几行 Python 代码就能完成各种操作，例如处理 Supervisely 标注 JSON 格式、与平台通信、导入和导出数据、管理成员、上传模型预测结果等。\n\n\u003Cdetails>\n\n\u003Csummary>Python SDK 示例\u003C\u002Fsummary>\n\n看看从您的 Python 脚本中与平台通信是多么简单。\n\n```python\nimport supervisely as sly\n\n# 使用个人 API 令牌进行身份验证\napi = sly.Api.from_env()\n\n# 创建项目和数据集\nproject = api.project.create(workspace_id=123, name=\"demo project\")\ndataset = api.dataset.create(project.id, \"dataset-01\")\n\n# 上传数据\nimage_info = api.image.upload_path(dataset.id, \"img.png\", \"\u002FUsers\u002Fmax\u002Fimg.png\")\napi.annotation.upload_path(image_info.id, \"\u002FUsers\u002Fmax\u002Fann.json\")\n\n# 下载数据\nimg = api.image.download_np(image_info.id)\nann = api.annotation.download_json(image_info.id)\n```\n\n\u003C\u002Fdetails>\n\n#### 第3级：无界面应用（无UI）\n\n创建Python应用程序来自动化常规和重复性任务，在组织内部共享，并为没有编程背景的最终用户提供一种简单的使用方式。无界面的应用程序只是可以从上下文菜单中运行的Python脚本。\n\n![从上下文菜单运行应用](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_0985ec216249.png)\n\n这种方法简单，适用于最基础的任务和用例，例如：\n\n* 以自定义格式导入和导出（[示例1](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fimport-images-groups)，[示例2](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fexport-as-masks)，[示例3](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fexport-to-pascal-voc)，[示例4](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Frender-video-labels-to-mp4)）\n* 资产转换（[示例1](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Frasterize-objects-on-images)，[示例2](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fresize-images)，[示例3](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fchange-video-framerate)，[示例4](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fconvert_ptc_to_ptc_episodes)）\n* 用户管理（[示例1](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Finvite-users-to-team-from-csv)，[示例2](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fcreate-users-from-csv)，[示例3](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fexport-activity-as-csv)）\n* 部署用于AI辅助标注的专用模型（[示例1](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fritm-interactive-segmentation%2Fsupervisely)，[示例2](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Ftrans-t%2Fsupervisely%2Fserve)，[示例3](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fvolume-interpolation)）\n\n#### 第4级：具有交互式UI的应用程序\n\n交互式界面和可视化是构建与改进AI解决方案的关键：从自定义数据标注到模型训练。这类应用为定制Supervisely平台以适应任何计算机视觉任务、实现符合组织需求的数据和模型工作流，甚至在其基础上构建特定行业的垂直解决方案提供了机会。\n\n\u003Ca href=\"https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fdev-smart-tool-batched\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_324e7e2db160.gif\" style=\"max-width:100%;\"\n  alt=\"[该界面完全基于Python，并结合易于使用的Supervisely UI组件（用于AI辅助目标分割的Batched SmartTool应用）]\">\n\u003C\u002Fa>\n\n以下是一些示例：\n\n* 带有AI辅助的自定义标注界面，适用于[图像](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fdev-smart-tool-batched)和[视频](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fbatched-smart-tool-for-videos)\n* [交互式模型性能分析](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsemantic-segmentation-metrics-dashboard)\n* [交互式NN训练仪表板](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fmmsegmentation%2Ftrain)\n* [数据探索](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Faction-recognition-stats)和[可视化](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fobjects-thumbnails-preview-by-class)应用\n* [垂直解决方案](https:\u002F\u002Fecosystem.supervisely.com\u002Fcollections\u002Fsupervisely-ecosystem%2Fgl-metric-learning%2Fsupervisely%2Fretail-collection)用于零售业货架上产品的标注\n* 推理界面[在标注工具中](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fnn-image-labeling%2Fannotation-tool)；适用于[图像](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fnn-image-labeling%2Fproject-dataset)，[视频](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fapply-nn-to-videos-project)和[点云](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fapply-det3d-to-project-dataset)；也适用于[模型集成](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fapply-det-and-cls-models-to-project)\n\n#### 第5级：UI集成到标注工具中的应用程序\n\n不存在一种适用于所有任务的通用标注工具。为了高效完成工作，标注工具必须针对特定任务进行设计和定制。Supervisely应用程序可以无缝集成到标注工具中，从而提供卓越的用户体验（包括多租户支持）和标注效率。\n\n\u003Ca href=\"https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%252Fgl-metric-learning%252Fsupervisely%252Flabeling-tool\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_42fbbaf38c97.png\" style=\"max-width:100%;\"\n  alt=\"[AI辅助零售标注应用集成到标注工具中，并可通过Web套接字与其通信]\">\n\u003C\u002Fa>\n\n以下是一些示例：\n\n* 专为自定义标注流程设计的应用程序（[示例1](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fvisual-tagging)，[示例2](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Freview-labels-side-by-side)）\n* 将NN推理集成用于标注自动化和模型预测分析（[示例](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fnn-image-labeling%2Fannotation-tool)）\n* 行业特定的标注工具：借助AI辅助对货架上的数千种产品类型进行标注（[零售系列](https:\u002F\u002Fecosystem.supervisely.com\u002Fcollections\u002Fsupervisely-ecosystem%2Fgl-metric-learning%2Fsupervisely%2Fretail-collection)，[标注应用](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fai-assisted-classification)）\n\n### 原则 🧭\n\nSupervisely 的开发基于以下五项原则：\n\n* 全部使用 **纯 Python**，并构建在您喜爱的库之上（如 OpenCV、Requests、FastAPI、PyTorch、imgaug 等）——这使得 Python 开发者和数据科学家能够轻松地构建应用，并与团队成员及机器学习社区共享。\n* 无需前端开发经验——通过使用全面且开箱即用的 UI 组件库，您可以构建 **强大** 且 **交互式** 的 Web GUI 应用程序。\n* **易于学习、快速编码**，并且 **可直接投入生产**。SDK 提供简单直观的 API，将复杂性封装在底层。只需几行代码即可完成任何操作。您只需专注于自己的任务，Supervisely 将负责处理其余的一切：界面、数据库、权限、安全、云端或自托管部署、网络、数据存储等。Supervisely 拥有完善的测试、文档和支持体系。\n* 一切皆可 **定制**——从标注界面到神经网络。平台需要根据您的具体任务和需求进行定制和扩展，而不是反过来。我们的 [应用生态系统](https:\u002F\u002Fecosystem.supervisely.com\u002F) 中提供了数百个示例，覆盖各种场景。\n* 应用可以是 **开源的或私有的**。Supervisely 团队开发的所有应用都是 [开源的](https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem)。您可以将其作为示例，直接 fork 并按需修改。同时，客户和社区用户也可以开发私有应用，以保护其知识产权。\n\n## 主要特性 💎\n\n- [**YouTube 完整视频课程**：什么是 Supervisely？](#complete-video-course-on-youtube-what-is-supervisely)\n- [目录](#table-of-contents)\n- [简介](#introduction)\n  - [Supervisely 平台 🔥](#supervisely-platform-)\n  - [Supervisely 生态系统 🎉](#supervisely-ecosystem-)\n- [开发 🧑‍💻](#development-)\n  - [开发者能做什么](#what-developers-can-do)\n    - [Level 1. HTTP REST API](#level-1-http-rest-api)\n    - [Level 2. 用于自动化和集成的 Python 脚本](#level-2-python-scripts-for-automation-and-integration)\n    - [Level 3. 无 UI 的 Headless 应用](#level-3-headless-apps-without-ui)\n    - [Level 4. 带交互式 UI 的应用](#level-4-apps-with-interactive-uis)\n    - [Level 5. 集成到标注工具中的带 UI 的应用](#level-5-apps-with-ui-integrated-into-labeling-tools)\n  - [原则 🧭](#principles-)\n- [主要特性 💎](#main-features-)\n  - [一分钟内开始](#start-in-a-minute)\n  - [神奇简单的 API](#magically-simple-api)\n  - [无处不在的定制化](#customization-is-everywhere)\n  - [交互式 GUI 是游戏规则改变者](#interactive-gui-is-a-game-changer)\n  - [使用现成的 UI 组件快速开发](#develop-fast-with-ready-ui-widgets)\n  - [便捷的调试](#convenient-debugging)\n  - [应用可以是私有的也可以是公有的](#apps-can-be-both-private-and-public)\n  - [一键部署](#single-click-deployment)\n  - [可靠的版本控制——发布与分支](#reliable-versioning---releases-and-branches)\n  - [同时支持 GitHub 和 GitLab](#supports-both-github-and-gitlab)\n  - [应用只是一个 Web 服务器，您可以使用任何喜欢的技术](#app-is-just-a-web-server-use-any-technology-you-love)\n  - [内置云开发环境（即将推出）](#built-in-cloud-development-environment-coming-soon)\n  - [受财富 500 强企业信赖，全球已有 65,000 名研究人员、开发者和公司使用](#trusted-by-fortune-500-used-by-65-000-researchers-developers-and-companies-worldwide)\n- [社区 🌎](#community-)\n  - [有想法或需要帮助吗？](#have-an-idea-or-ask-for-help)\n- [贡献 👏](#contribution-)\n- [合作 🤝](#partnership-)\n- [引用本项目](#cite-this-project)\n\n### 一分钟内开始\n\nSupervisely 的开源 SDK 和应用框架非常容易上手。您只需执行以下命令：\n\n```\npip install supervisely\n```\n\n### 神奇简单的 API\n\n[Supervisely Python SDK](https:\u002F\u002Fsupervisely.readthedocs.io\u002Fen\u002Flatest\u002Fsdk_packages.html) 简单直观，能够为您节省大量时间。减少样板代码，只需几行代码即可构建自定义集成。从 Python 中与平台通信从未如此简单。\n\n```python\n# 使用个人 API 令牌进行身份验证\napi = sly.Api.from_env()\n\n# 创建项目和数据集\nproject = api.project.create(workspace_id=123, name=\"demo project\")\ndataset = api.dataset.create(project.id, \"dataset-01\")\n\n# 上传数据\nimage_info = api.image.upload_path(dataset.id, \"img.png\", \"\u002FUsers\u002Fmax\u002Fimg.png\")\napi.annotation.upload_path(image_info.id, \"\u002FUsers\u002Fmax\u002Fann.json\")\n\n# 下载数据\nimg = api.image.download_np(image_info.id)\nann = api.annotation.download_json(image_info.id)\n```\n\n### 自定义无处不在\n\n在计算机视觉领域，只有通过自定义才能满足所有任务需求。Supervisely 允许您自定义从标注界面、上下文菜单到训练仪表盘和推理界面的一切。请查看我们的 [应用生态系统](https:\u002F\u002Fecosystem.supervisely.com\u002F)，从中获取灵感和示例，为您的下一个机器学习工具提供参考。\n\n### 交互式 GUI 是游戏规则改变者\n\n大多数 Python 程序都是基于命令行的。虽然经验丰富的程序员对此游刃有余，但其他技术人员和最终用户却常常感到困难。这就造成了数字鸿沟，即“GUI 缺失”。而带有图形用户界面（GUI）的应用则更容易被更广泛的受众接受和使用。此外，有些任务如果没有 GUI 根本无法完成。\n\n试想一下，如果所有的机器学习工具和代码仓库都配备一个带有“运行”按钮 ▶️ 的交互式 GUI，那该有多好！这样，您只需几分钟就能开始使用顶级深度学习框架，而不用再花费数周时间来针对自己的数据进行训练。\n\n🎯 我们的宏伟目标就是实现这一点。\n\n\n\u003Ca href=\"https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsemantic-segmentation-metrics-dashboard\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_8847958de0db.gif\" style=\"max-width:100%;\"\n  alt=\"语义分割指标仪表板应用\">\n\u003C\u002Fa>\n\n### 快速开发：即用型UI组件\n\n我们为您准备了数百个交互式UI组件和控件。只需将其添加到您的程序中，并填充数据即可。Python开发者无需具备前端开发经验；在我们的开发者门户中，您将找到所需的指南、示例和教程。我们支持以下UI组件：\n\n1. [Supervisely打造的组件](https:\u002F\u002Fdeveloper.supervisely.com\u002Fapp-development\u002Fwidgets)，专为计算机视觉任务设计，例如渲染带有标注的图像画廊、带标签的视频正反向播放、交互式混淆矩阵、表格、图表等。\n2. [Element UI组件](https:\u002F\u002Felement.eleme.io\u002F1.4\u002F#\u002Fen-US\u002Fcomponent\u002Fbutton)——基于Vue 2.0的组件库。\n3. [Plotly](https:\u002F\u002Fplotly.com\u002Fpython\u002F) Python绘图库。\n4. [开发您自己的自定义组件](https:\u002F\u002Fdeveloper.supervisely.com\u002Fapp-development\u002Fadvanced\u002Fhow-to-make-your-own-widget)。\n\nSupervisely团队将其大部分应用公开发布在[GitHub](https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem)上。您可以将其作为未来应用的示例：fork、修改并复制粘贴代码片段。\n\n### 方便的调试功能\n\nSupervisely由数据科学家为数据科学家打造，致力于降低开发门槛，提供友好的开发环境。我们尤其重视调试这一关键环节。\n\n即使在复杂场景下，例如开发集成于标注工具中的GUI应用，我们也保持简单易用——您可以在自己喜欢的IDE中设置断点来捕获回调函数，逐步执行程序，并实时查看更新，而无需刷新页面。就是这么简单！Supervisely会处理其余一切——WebSocket、身份验证、Redis、RabbitMQ、Postgres等。\n\n请观看下方视频，了解我们如何调试一款将神经网络直接应用于标注界面的应用程序：[链接](https:\u002F\u002Fecosystem.supervisely.com\u002Fapps\u002Fsupervisely-ecosystem%2Fnn-image-labeling%2Fannotation-tool)。\n\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FfOnyL8YHOBM\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_fdd32b1d6a01.png\" style=\"max-width:100%;\">\n\u003C\u002Fa>\n\n### 应用可私有也可公开\n\nSupervisely团队开发的所有应用均为[开源](https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem)。您可以将其作为示例：在[GitHub](https:\u002F\u002Fgithub.com\u002Fsupervisely-ecosystem)上查找、fork并按需修改。同时，客户和社区用户也可以开发私有应用，以保护其知识产权。\n\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FKyuc-lZu_tg\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_6bc9a1175b73.png\" style=\"max-width:100%;\">\n\u003C\u002Fa>\n\n### 一键部署\n\nSupervisely应用本质上就是一个Git仓库。您只需提供Git仓库的链接，Supervisely便会处理其余一切。现在，您只需点击应用前的“运行”按钮，即可在任何安装了[Supervisely Agent](https:\u002F\u002Fyoutu.be\u002FaDqQiYycqyk)的计算机上启动该应用。\n\n### 可靠的版本管理——发布与分支\n\n用户始终运行的是最新稳定版，而您可以并行开发和测试新功能——只需使用Git的发布和分支功能。Supervisely会自动从Git拉取更新，即便新版本的应用存在bug，也无需担心——用户可以一键选择并运行之前的版本。\n\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FngoHfM98R8k\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_b303dacbcbae.png\" style=\"max-width:100%;\">\n\u003C\u002Fa>\n\n### 同时支持GitHub和GitLab\n\n由于Supervisely应用只是一个Git仓库，因此我们支持来自全球最受欢迎的托管平台——GitHub和GitLab——的公共及私有仓库。\n\n### 应用即Web服务器，可使用任何您喜爱的技术\n\nSupervisely的Python SDK为Python开发者和数据科学家提供了构建任意复杂度交互式GUI应用的最简单方式。Python是开发Supervisely应用的推荐语言，但并非唯一选择。您可以使用任何您喜爱的语言或技术，任何Web服务器都可以部署在该平台上。\n\n例如，即使是[适用于Web的Visual Studio Code](https:\u002F\u002Fgithub.com\u002Fcoder\u002Fcode-server)，也可以作为应用运行（见下方视频）。\n\n### 内置云开发环境（即将推出）\n\n除了在您本地电脑或笔记本上的常用IDE中进行开发之外，云开发支持也将集成到Supervisely中，并将在**不久的将来发布**，以加速开发、标准化开发环境并降低初学者的门槛。具体操作方式如下：只需将您的电脑连接到Supervisely实例，运行IDE应用（如[JupyterLab](https:\u002F\u002Fjupyter.org\u002F)和[适用于Web的Visual Studio Code](https:\u002F\u002Fgithub.com\u002Fcoder\u002Fcode-server)），即可在一分钟内开始编码。我们将提供大量涵盖最常见用例的模板应用。\n\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FptHJsdolHHk\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_1c8b56db4b4d.png\" style=\"max-width:100%;\">\n\u003C\u002Fa>\n\n### 财富500强信赖之选，全球6.5万名研究人员、开发者和企业正在使用\n\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_readme_d581eb80030b.png)\n\nSupervisely帮助全球企业和研究人员在自动驾驶、农业、医疗等多个行业构建计算机视觉解决方案。加入我们的[Community Edition](https:\u002F\u002Fapp.supervisely.com\u002F)，或为贵组织申请[Enterprise Edition](https:\u002F\u002Fsupervisely.com\u002Fenterprise)。\n\n## 社区 🌎\n\n加入不断壮大的[Supervisely社区](https:\u002F\u002Fapp.supervisely.com\u002F)，目前已有超过6.5万名用户。\n\n#### 您有想法或需要帮助吗？\n\n如果您有任何问题、想法或反馈，请：\n\n1. [提交功能建议或创意](https:\u002F\u002Fideas.supervisely.com\u002F)，或[提供技术反馈](https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fissues)。\n2. [加入我们的Slack](https:\u002F\u002Fsupervisely.com\u002Fslack)。\n3. [联系我们](https:\u002F\u002Fsupervisely.com\u002Fcontact-us)。\n\n您的反馈对我们帮助巨大，我们深表感谢！\n\n## 贡献 👏\n\n希望助力计算机视觉研发更上一层楼吗？我们诚邀您参与并加速数千名研究人员的工作，方法包括：\n\n* 与我们一起构建并扩展Supervisely生态系统。\n* 将您的ML工具和研究成果集成到Supervisely中，与整个ML社区共享。\n\n## 合作伙伴 🤝\n\n我们非常乐意与更多技术合作伙伴、研究人员、开发者以及增值经销商携手合作，共同拓展并提升Supervisely生态系统的价值。\n\n如果您拥有以下资源，请随时联系我们：\n\n* ML服务或产品。\n* 独特的专业领域知识。\n* 行业垂直解决方案。\n* 解决特定任务的宝贵代码库和工具。\n* 自定义的NN模型和数据集。\n\n让我们探讨合作方式，尤其是在双方具有共同兴趣、技术和客户的情况下。\n\n## 引用本项目\n\n如果您在研究中使用本项目，请使用以下 BibTeX 格式进行引用：\n\n```\n@misc{ supervisely,\n    title = { 监控精灵计算机视觉平台 },\n    type = { 计算机视觉工具 },\n    author = { 监控精灵 },\n    howpublished = { \\url{ https:\u002F\u002Fsupervisely.com } },\n    url = { https:\u002F\u002Fsupervisely.com },\n    journal = { 监控精灵生态系统 },\n    publisher = { 监控精灵 },\n    year = { 2023 },\n    month = { jul },\n    note = { 访问日期：2023年7月20日 },\n}\n```","# Supervisely 快速上手指南\n\nSupervisely 是一个基于浏览器的计算机视觉操作系统平台，提供数据标注、模型训练、可视化及自动化生态应用。本指南帮助开发者快速搭建环境并运行第一个示例。\n\n## 环境准备\n\n在开始之前，请确保满足以下系统要求和前置依赖：\n\n*   **操作系统**：Linux (推荐 Ubuntu 20.04+), macOS, 或 Windows (建议使用 WSL2)。\n*   **Python 版本**：Python 3.8 - 3.11。\n*   **包管理器**：已安装 `pip`。\n*   **账号与令牌**：\n    *   拥有一个 [Supervisely 账号](https:\u002F\u002Fsupervisely.com\u002F)（可使用社区版或企业版）。\n    *   获取个人 **API Token**：登录平台后，点击右上角头像 -> \"My Profile\" -> \"Access Tokens\" 生成或复制现有 Token。\n*   **网络环境**：确保能访问 `app.supervisely.com` (社区版) 或您的私有部署地址。\n\n> **国内加速提示**：如果下载 `pip` 包速度较慢，建议使用国内镜像源（如清华源或阿里源）进行安装。\n\n## 安装步骤\n\n### 1. 安装 Python SDK\n\n使用 pip 安装 Supervisely 核心开发包。\n\n**标准安装命令：**\n```bash\npip install supervisely\n```\n\n**推荐使用国内镜像源加速安装：**\n```bash\npip install supervisely -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n```\n\n### 2. 配置 API Token\n\n为了在代码中自动认证，建议将 API Token 设置为环境变量。\n\n**Linux \u002F macOS:**\n```bash\nexport SUPERVISELY_API_TOKEN=\u003Cyour-token-here>\n```\n\n**Windows (PowerShell):**\n```powershell\n$env:SUPERVISELY_API_TOKEN=\"\u003Cyour-token-here>\"\n```\n\n*(请将 `\u003Cyour-token-here>` 替换为您实际复制的 Token 字符串)*\n\n## 基本使用\n\n以下是最简单的 Python 脚本示例，演示如何连接平台、创建项目、上传图像及标注数据。\n\n### 最小化代码示例\n\n创建一个名为 `quick_start.py` 的文件，填入以下内容：\n\n```python\nimport supervisely as sly\n\n# 1. 初始化 API 客户端\n# 自动读取环境变量 SUPERVISELY_API_TOKEN 进行认证\napi = sly.Api.from_env()\n\n# 2. 获取工作空间 ID (请在平台上查看您的 Workspace ID，或硬编码此处)\n# 注意：如果是首次使用，请替换为您实际的工作空间 ID\nworkspace_id = 123 \n\n# 3. 创建项目和数据集\nproject = api.project.create(workspace_id=workspace_id, name=\"demo_project_quickstart\")\ndataset = api.dataset.create(project.id, \"dataset_01\")\n\n# 4. 准备本地文件路径 (请替换为您本地的实际图片路径)\nimage_path = \"\u002Fpath\u002Fto\u002Fyour\u002Fimage.png\"\nannotation_path = \"\u002Fpath\u002Fto\u002Fyour\u002Fannotation.json\" \n\n# 5. 上传图像\n# 返回图像信息对象，包含 image_info.id\nimage_info = api.image.upload_path(dataset.id, \"img.png\", image_path)\n\n# 6. 上传标注 (可选)\n# 假设您有一个对应的 JSON 标注文件\nif os.path.exists(annotation_path):\n    api.annotation.upload_path(image_info.id, annotation_path)\n\n# 7. 下载验证 (可选)\n# 将图像下载为 numpy 数组\nimg_np = api.image.download_np(image_info.id)\nprint(f\"成功上传图像: {image_info.name}, 形状: {img_np.shape}\")\n```\n\n### 运行脚本\n\n在终端执行：\n\n```bash\npython quick_start.py\n```\n\n如果脚本运行无报错，请登录 Supervisely 网页端，即可在对应工作空间中看到新建的 `demo_project_quickstart` 项目及其中的数据。\n\n### 进阶：运行无界面应用 (Headless Apps)\n\n除了编写脚本，您还可以将 Python 脚本封装为“无界面应用”，通过平台右键菜单直接运行，无需编写前端代码。只需按照上述 SDK 逻辑编写脚本，并通过 Supervisely Agent 部署即可实现自动化任务（如格式转换、批量重命名、模型推理等）。","某自动驾驶团队需要每周处理数千张新采集的路况图像，进行数据清洗、格式转换并分发给标注团队。\n\n### 没有 supervisely 时\n- 工程师需编写大量分散的 Python 脚本手动调用 API 来上传下载数据，代码难以维护且容易出错。\n- 数据预处理（如调整分辨率、过滤模糊图片）依赖本地运行，无法利用平台算力，处理速度慢且占用开发机资源。\n- 缺乏统一的操作界面，非技术背景的标注员无法自主触发数据增强或格式转换任务，必须等待开发人员介入。\n- 每次更新处理逻辑都需要重新部署服务，版本管理混乱，难以追溯某次标注数据是由哪版代码生成的。\n- 自定义功能与标注工具割裂，标注过程中发现数据问题无法实时调用后台算法进行修正，工作流频繁中断。\n\n### 使用 supervisely 后\n- 利用 supervisely SDK 将繁琐的 API 调用封装为简洁的 Python 模块，几行代码即可实现复杂的数据自动化流转。\n- 将数据清洗逻辑打包为无头应用（Headless App）直接在平台云端运行，处理效率提升十倍且不占用本地资源。\n- 通过 supervisely 快速构建带交互界面的应用，标注员可在网页端一键执行数据增强或格式转换，实现自助服务。\n- 依托内置的版本控制和单键部署功能，每次逻辑更新自动生成新版本应用，确保数据处理流程可追溯、可回滚。\n- 开发带有 UI 的插件直接嵌入标注工具，标注员发现异常时可即时调用后台模型重测，工作流无缝衔接。\n\nsupervisely 通过极简的 SDK 和灵活的生态体系，将计算机视觉工程从“脚本堆砌”升级为“标准化应用开发”，极大提升了团队协作效率。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsupervisely_supervisely_0985ec21.png","Supervisely","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fsupervisely_ce5f8560.png","Web platform to build computer vision",null,"hello@supervisely.com","https:\u002F\u002Fsupervisely.com","https:\u002F\u002Fgithub.com\u002Fsupervisely",[80,84,88,92,96,99,103],{"name":81,"color":82,"percentage":83},"Python","#3572A5",95.9,{"name":85,"color":86,"percentage":87},"HTML","#e34c26",1.6,{"name":89,"color":90,"percentage":91},"JavaScript","#f1e05a",0.9,{"name":93,"color":94,"percentage":95},"CSS","#663399",0.6,{"name":97,"color":98,"percentage":95},"Jinja","#a52a22",{"name":100,"color":101,"percentage":102},"Dockerfile","#384d54",0.2,{"name":104,"color":105,"percentage":106},"Shell","#89e051",0.1,523,77,"2026-04-02T20:15:57","Apache-2.0",4,"未说明",{"notes":114,"python":115,"dependencies":116},"Supervisely 主要是一个基于 Web 浏览器的计算机视觉平台（操作系统），核心功能通过云端或自托管服务器运行，用户端只需浏览器即可使用。开发者可通过 Python SDK、HTTP REST API 或 Docker 容器进行集成和开发。README 中未列出本地运行具体硬件需求，因为计算密集型任务（如模型训练、推理）通常在服务器端或容器中执行。支持通过 Docker 部署应用，应用本质上是 Web 服务器，可使用任意技术栈开发。","3.8+",[117],"supervisely (SDK)",[14,15,13],[120,121,122,123],"ai","neural-networks","python","deep-learning","2026-03-27T02:49:30.150509","2026-04-07T21:20:44.599682",[127,132,137,142,147,152],{"id":128,"question_zh":129,"answer_zh":130,"source_url":131},16919,"如何导出为 COCO 格式或导入 COCO 数据集？","用户曾询问为何 COCO 插件被移除以及如何导入 COCO 格式数据。虽然早期版本支持导入，但后续版本中该功能有所变动或被标记为过时。建议检查最新的官方文档或插件目录以确认当前是否支持直接导入\u002F导出 COCO 格式。如果官方插件不可用，可能需要使用社区提供的转换脚本或等待官方更新恢复该功能。","https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fissues\u002F3",{"id":133,"question_zh":134,"answer_zh":135,"source_url":136},16920,"下载的掩码（mask）图片全是黑色的怎么办？","如果在下载 PNG 掩码时得到全黑图片，通常是因为标注对象的厚度（thickness）设置问题或渲染方式差异。有用户反馈在提取多边形掩码时，thickness 设置为 1 或 100 没有明显区别。这可能与具体的导出配置或可视化工具有关。建议检查导出设置中的颜色映射和厚度参数，或者尝试在 Supervisely 平台内部预览掩码以确认标注是否存在。如果问题依旧，可能是特定版本的文件生成 bug，建议联系支持团队或尝试更新 SDK。","https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fissues\u002F16",{"id":138,"question_zh":139,"answer_zh":140,"source_url":141},16921,"Supervisely Python SDK 是否支持 Python 3.12？遇到 'No module named pkg_resources' 错误如何解决？","目前官方正在积极进行迁移以支持 Python 3.12。遇到 `ModuleNotFoundError: No module named 'pkg_resources'` 错误通常是因为虚拟环境中缺少 `setuptools` 或其版本过旧。解决方案是显式安装或升级 setuptools：`pip install --upgrade setuptools`。此外，对于 Python 3.8-3.10 用户，如果遇到构建 wheel 失败的问题，也请确保 setuptools 已正确安装。官方建议在完全支持前暂时使用 Python 3.8 至 3.10 版本以保证稳定性。","https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fissues\u002F1515",{"id":143,"question_zh":144,"answer_zh":145,"source_url":146},16922,"为什么 Supervisely 限制了 pydantic 和 urllib3 的版本？如何更新到最新版本？","早期版本为了稳定性限制了 `pydantic` (\u003C2.5.0) 和 `urllib3` (\u003C2.2.0) 的版本。针对用户希望使用更新版本（如 pydantic==2.7.3, urllib3==2.2.2）的需求，维护者已经测试了新版本的兼容性并发布了新的 Supervisely Python SDK 版本来解决此限制。用户只需运行 `pip install --upgrade supervisely` 更新到最新版 SDK 即可解除版本锁定并使用最新的依赖库。","https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fissues\u002F1057",{"id":148,"question_zh":149,"answer_zh":150,"source_url":151},16923,"在 Docker 容器或 WSL2 中运行本地 Agent 时提示 'CUDA not detected' 或 'Unknown runtime specified nvidia' 如何解决？","这是因为 Docker 未正确配置以访问 GPU。在较新的 systemd 版本或 WSL2 环境下，需要额外配置：\n1. 确保在 Linux 发行版（包括 WSL2 中的 Ubuntu）中安装了 `nvidia-container-toolkit` (或 `nvidia-docker2`)。\n2. 对于 Docker Desktop 用户，需进入设置菜单 -> Docker Engine，在 JSON 配置中添加 runtime 配置：\n   ```json\n   \"runtimes\": {\n       \"nvidia\": {\n           \"path\": \"nvidia-container-runtime\",\n           \"runtimeArgs\": []\n       }\n   }\n   ```\n3. 重启 Docker 服务。完成上述步骤后，Agent 应能正确识别 NVIDIA 运行时并调用 GPU。","https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fissues\u002F54",{"id":153,"question_zh":154,"answer_zh":155,"source_url":156},16924,"Supervisely Agent 是否支持 Docker 19.03+ 版本？如何使用 --gpus all 参数？","Docker 19.03+ 已弃用 `nvidia-docker` 并将 `--runtime=nvidia` 替换为 `--gpus all` 标志。虽然早期 Agent 脚本可能仍硬编码了旧标志，但用户可以通过手动修改本地的 agent 启动脚本来适配新版本 Docker，将运行时的参数更改为 `--gpus all`。官方曾被问及是否会添加自动检测 Docker 版本的选项，但在获得官方更新前，手动修改脚本是使用新版 Docker 的有效变通方案。","https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fissues\u002F38",[158,163,168,173,178,183,188,193,198,202,207,212,217,221,225,229,234,239,244,249],{"id":159,"version":160,"summary_zh":161,"released_at":162},99162,"v6.73.555","## 变更内容\n* 实时训练：修复实验生成器中别名的解析问题，由 @max-unfinity 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1690 中完成。\n\n\n**完整变更日志**：https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.554...v6.73.555","2026-04-02T20:16:43",{"id":164,"version":165,"summary_zh":166,"released_at":167},99163,"v6.73.554","## 变更内容\n* 添加叠加图像：新增标注界面、自动导入转换器以及上传 API，支持带有叠加图像的图片，由 @kirasolda 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1687 中实现。\n\n\n**完整变更日志**：https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.553...v6.73.554","2026-04-01T16:19:26",{"id":169,"version":170,"summary_zh":171,"released_at":172},99164,"v6.73.553","## 变更内容\n* 实时训练：由 @max-unfinity 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1683 中改进了与 Web UI 的 API 通信。\n\n\n**完整变更日志**：https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.552...v6.73.553","2026-03-25T15:47:34",{"id":174,"version":175,"summary_zh":176,"released_at":177},99165,"v6.73.552","## 变更内容\n* 添加版本预览功能，由 @GoldenAnpu 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1679 中实现。\n\n\n**完整变更日志**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.551...v6.73.552","2026-03-24T12:17:45",{"id":179,"version":180,"summary_zh":181,"released_at":182},99166,"v6.73.551","## 变更内容\n* 由 @cxnt 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1680 中将 scikit-image 和 scipy 添加到安装依赖中\n* 由 @cxnt 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1681 中撤销“将 scikit-image 和 scipy 添加到安装依赖中”的更改\n* 由 @kirasolda 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1682 中修复 high_color_depth 中的错误\n\n\n**完整变更日志**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.550...v6.73.551","2026-03-23T14:49:03",{"id":184,"version":185,"summary_zh":186,"released_at":187},99167,"v6.73.550","## 变更内容\n* 修复 list_deployed_models，使其能够优雅地处理缺失的 'meta.app.moduleId' 字段，由 @almazgimaev 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1677 中完成。\n\n\n**完整变更日志**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.549...v6.73.550","2026-03-13T17:13:57",{"id":189,"version":190,"summary_zh":191,"released_at":192},99168,"v6.73.549","## 变更内容\n* 由 @vorozhkog 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1676 中修复了与 urllib 相关的依赖问题\n\n\n**完整变更日志**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.548...v6.73.549","2026-03-13T16:11:33",{"id":194,"version":195,"summary_zh":196,"released_at":197},99169,"v6.73.548","## 变更内容\n* 由 @almazgimaev 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1675 中修复了用于在 API 响应中标记界面 HTTP 错误处理的正则表达式模式。\n\n\n**完整变更日志**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.547...v6.73.548","2026-03-13T09:16:10",{"id":199,"version":200,"summary_zh":75,"released_at":201},99170,"v6.73.547","2026-03-12T20:14:26",{"id":203,"version":204,"summary_zh":205,"released_at":206},99171,"v6.73.546","## 变更内容\n* 由 @kirasolda 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1674 中实现：当标注接口不可用（非免费）时，新增对 HTTPError 的处理。\n\n\n**完整变更日志**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.545...v6.73.546","2026-03-12T19:27:41",{"id":208,"version":209,"summary_zh":210,"released_at":211},99172,"v6.73.545","## 变更内容\n* 由 @cxnt 在 https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1673 中修复了嵌套数据集的模型基准对比问题\n\n\n**完整变更日志**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.544...v6.73.545","2026-03-12T14:22:27",{"id":213,"version":214,"summary_zh":215,"released_at":216},99173,"v6.73.544","## What's Changed\r\n* Reorder frames for pointcloud episodes by @kirasolda in https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1671\r\n* Replace distutils with shutil and  PTable package with prettytable; Replace return statements from \"finally\" blocks by @vorozhkog in https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1670\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.543...v6.73.544","2026-03-11T16:36:23",{"id":218,"version":219,"summary_zh":75,"released_at":220},99174,"v6.73.543","2026-03-11T15:37:53",{"id":222,"version":223,"summary_zh":75,"released_at":224},99175,"v6.73.542","2026-03-10T02:59:49",{"id":226,"version":227,"summary_zh":75,"released_at":228},99176,"v6.73.541","2026-03-09T21:19:03",{"id":230,"version":231,"summary_zh":232,"released_at":233},99177,"v6.73.540","## What's Changed\r\n* Fix model benchmark comparison for collections by @cxnt in https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1666\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.539...v6.73.540","2026-03-06T10:25:26",{"id":235,"version":236,"summary_zh":237,"released_at":238},99178,"v6.73.539","## What's Changed\r\n* Fixed duplicated names of related images in pointcloud and pointcloud_episode projects by @kirasolda in https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1665\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.538...v6.73.539","2026-03-03T16:44:31",{"id":240,"version":241,"summary_zh":242,"released_at":243},99179,"v6.73.538","## What's Changed\r\n* Live Training: Rename EMA metric to model_quality by @max-unfinity in https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1664\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.537...v6.73.538","2026-03-02T15:48:23",{"id":245,"version":246,"summary_zh":247,"released_at":248},99180,"v6.73.537","## What's Changed\r\n* Add image description field to ImageInfo in ImageApi by @almazgimaev in https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1661\r\n* Replace image links in annotation, bitmap and imaging modules with GitHub-hosted assets. by @cxnt in https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fpull\u002F1662\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsupervisely\u002Fsupervisely\u002Fcompare\u002Fv6.73.536...v6.73.537","2026-02-25T13:35:14",{"id":250,"version":251,"summary_zh":75,"released_at":252},99181,"v6.73.536","2026-02-25T12:54:29"]