base on Refine high-quality datasets and visual AI models <div align="center"> <p align="center"> <!-- prettier-ignore --> <img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="55px"> &nbsp; <img src="https://user-images.githubusercontent.com/25985824/106288518-24bb7680-6216-11eb-8f10-60052c519586.png" height="50px"> **The open-source tool for building high-quality datasets and computer vision models** --- <!-- prettier-ignore --> <a href="https://voxel51.com/fiftyone">Website</a> • <a href="https://voxel51.com/docs/fiftyone">Docs</a> • <a href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/quickstart.ipynb">Try it Now</a> • <a href="https://voxel51.com/docs/fiftyone/getting_started_guides/index.html">Getting Started Guides</a> • <a href="https://voxel51.com/docs/fiftyone/tutorials/index.html">Tutorials</a> • <a href="https://voxel51.com/blog/">Blog</a> • <a href="https://discord.gg/fiftyone-community">Community</a> [](https://pypi.org/project/fiftyone) [](https://pypi.org/project/fiftyone) [](https://pepy.tech/project/fiftyone) [](https://hub.docker.com/r/voxel51/fiftyone/) [](LICENSE) [](https://discord.gg/fiftyone-community) [](https://medium.com/voxel51) [](https://share.hsforms.com/1zpJ60ggaQtOoVeBqIZdaaA2ykyk) [](https://twitter.com/voxel51) </p> </div> # 👋 hey there! We created **[FiftyOne](https://fiftyone.ai)** to supercharge your visual AI projects by enabling you to visualize datasets, analyze models, and improve data quality more efficiently than ever before 🤝 If you're looking to scale to production-grade, collaborative, cloud-native enterprise workloads, check out **[FiftyOne Enterprise](http://voxel51.com/enterprise)** 🚀 <div id='installation'/> ## <img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="20px"> &nbsp; installation &nbsp; 💻 As simple as: ```shell pip install fiftyone ``` <details> <summary>More details</summary> ### Installation options FiftyOne supports Python 3.9 - 3.12. For most users, we recommend installing the latest release version of FiftyOne via `pip` as shown above. If you want to contribute to FiftyOne or install the latest development version, then you can also perform a [source install](#source-install). See the [prerequisites section](#prerequisites) for system-specific setup information. We strongly recommend that you install FiftyOne in a [virtual environment](https://voxel51.com/docs/fiftyone/installation/virtualenv.html) to maintain a clean workspace. Consult the [installation guide](https://voxel51.com/docs/fiftyone/installation/index.html) for troubleshooting and other information about getting up-and-running with FiftyOne. </details> <div id='source-install'/> <details> <summary>Install from source</summary> ### Source installations Follow the instructions below to install FiftyOne from source and build the App. You'll need the following tools installed: - [Python](https://www.python.org) (3.9 - 3.12) - [Node.js](https://nodejs.org) - on Linux, we recommend using [nvm](https://github.com/nvm-sh/nvm) to install an up-to-date version. - [Yarn](https://yarnpkg.com) - once Node.js is installed, you can [enable Yarn](https://yarnpkg.com/getting-started/install) via `corepack enable` We strongly recommend that you install FiftyOne in a [virtual environment](https://voxel51.com/docs/fiftyone/installation/virtualenv.html) to maintain a clean workspace. If you are working in Google Colab, [skip to here](#source-installs-in-google-colab). First, clone the repository: ```shell git clone https://github.com/voxel51/fiftyone cd fiftyone ``` Then run the install script: ```shell # Mac or Linux bash install.sh # Windows .\install.bat ``` If you run into issues importing FiftyOne, you may need to add the path to the cloned repository to your `PYTHONPATH`: ```shell export PYTHONPATH=$PYTHONPATH:/path/to/fiftyone ``` Note that the install script adds to your `nvm` settings in your `~/.bashrc` or `~/.bash_profile`, which is needed for installing and building the App. ### Upgrading your source installation To upgrade an existing source installation to the bleeding edge, simply pull the latest `develop` branch and rerun the install script: ```shell git checkout develop git pull # Mac or Linux bash install.sh # Windows .\install.bat ``` ### Rebuilding the App When you pull in new changes to the App, you will need to rebuild it, which you can do either by rerunning the install script or just running `yarn build` in the `./app` directory. ### Developer installation If you would like to [contribute to FiftyOne](https://github.com/voxel51/fiftyone/blob/develop/CONTRIBUTING.md), you should perform a developer installation using the `-d` flag of the install script: ```shell # Mac or Linux bash install.sh -d # Windows .\install.bat -d ``` Although not required, developers typically prefer to configure their FiftyOne installation to connect to a self-installed and managed instance of MongoDB, which you can do by following [these simple steps](https://docs.voxel51.com/user_guide/config.html#configuring-a-mongodb-connection). ### Source installs in Google Colab You can install from source in [Google Colab](https://colab.research.google.com) by running the following in a cell and then **restarting the runtime**: ```shell %%shell git clone --depth 1 https://github.com/voxel51/fiftyone.git cd fiftyone # Mac or Linux bash install.sh # Windows .\install.bat ``` ### Generating documentation See the [docs guide](https://github.com/voxel51/fiftyone/blob/develop/docs/README.md) for information on building and contributing to the documentation. ### Uninstallation You can uninstall FiftyOne as follows: ```shell pip uninstall fiftyone fiftyone-brain fiftyone-db ``` </details> <div id='prerequisites'/> <details> <summary>Prerequisites for beginners</summary> ### System-specific setup Follow the instructions for your operating system or environment to perform basic system setup before [installing FiftyOne](#installation). If you're an experienced developer, you've likely already done this. <details> <summary>Linux</summary> <div id='prerequisites-linux'/> #### 1. Install Python and other dependencies These steps work on a clean install of Ubuntu Desktop 24.04, and should also work on Ubuntu 24.04 and 22.04, and on Ubuntu Server: ```shell sudo apt-get update sudo apt-get upgrade sudo apt-get install python3-venv python3-dev build-essential git-all libgl1-mesa-dev ``` - On Linux, you will need at least the `openssl` and `libcurl` packages - On Debian-based distributions, you will need to install `libcurl4` or `libcurl3` instead of `libcurl`, depending on the age of your distribution ```shell # Ubuntu sudo apt install libcurl4 openssl # Fedora sudo dnf install libcurl openssl ``` #### 2. Create and activate a virtual environment ```shell python3 -m venv fiftyone_env source fiftyone_env/bin/activate ``` #### 3. Install FFmpeg (optional) If you plan to work with video datasets, you'll need to install [FFmpeg](https://ffmpeg.org): ```shell sudo apt-get install ffmpeg ``` </details> <details> <summary>MacOS</summary> <div id='prerequisites-macos'/> #### 1. Install Xcode Command Line Tools ```shell xcode-select --install ``` #### 2. Install Homebrew ```shell /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" ``` After running the above command, follow the instructions in your terminal to complete the Homebrew installation. #### 3. Install Python ```shell brew install [email protected] brew install protobuf ``` #### 4. Create and activate a virtual environment ```shell python3 -m venv fiftyone_env source fiftyone_env/bin/activate ``` #### 5. Install FFmpeg (optional) If you plan to work with video datasets, you'll need to install [FFmpeg](https://ffmpeg.org): ```shell brew install ffmpeg ``` </details> <details> <summary>Windows</summary> <div id='prerequisites-windows'/> #### 1. Install Python ⚠️ The version of Python that is available in the Microsoft Store is **not recommended** ⚠️ Download a Python 3.9 - 3.12 installer from [python.org](https://www.python.org/downloads/). Make sure to pick a 64-bit version. For example, this [Python 3.10.11 installer](https://www.python.org/ftp/python/3.10.11/python-3.10.11-amd64.exe). Double-click on the installer to run it, and follow the steps in the installer. - Check the box to add Python to your `PATH` - At the end of the installer, there is an option to disable the `PATH` length limit. It is recommended to click this #### 2. Install Microsoft Visual C++ Download [Microsoft Visual C++ Redistributable](https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist). Double-click on the installer to run it, and follow the steps in the installer. #### 3. Install Git Download Git from [this link](https://git-scm.com/download/win). Double-click on the installer to run it, and follow the steps in the installer. #### 4. Create and activate a virtual environment - Press `Win + R`. type `cmd`, and press `Enter`. Alternatively, search **Command Prompt** in the Start Menu. - Navigate to your project. `cd C:\path\to\your\project` - Create the environment `python -m venv fiftyone_env` - Activate the environment typing this in the command line window `fiftyone_env\Scripts\activate` - After activation, your command prompt should change and show the name of the virtual environment `(fiftyone_env) C:\path\to\your\project` #### 5. Install FFmpeg (optional) If you plan to work with video datasets, you'll need to install [FFmpeg](https://ffmpeg.org). Download an FFmpeg binary from [here](https://ffmpeg.org/download.html). Add FFmpeg's path (e.g., `C:\ffmpeg\bin`) to your `PATH` environmental variable. </details> <details> <summary>Docker</summary> <div id='prerequisites-docker'/> <br> Refer to [these instructions](https://voxel51.com/docs/fiftyone/environments/index.html#docker) to see how to build and run Docker images containing release or source builds of FiftyOne. </details> </details> <div id='quickstart'> ## <img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="20px"> &nbsp; quickstart &nbsp; 🚀 Dive right into FiftyOne by opening a Python shell and running the snippet below, which downloads a [small dataset](https://voxel51.com/docs/fiftyone/user_guide/dataset_zoo/datasets.html#quickstart) and launches the [FiftyOne App](https://voxel51.com/docs/fiftyone/user_guide/app.html) so you can explore it: ```py import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") session = fo.launch_app(dataset) ``` Then check out [this Colab notebook](https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/quickstart.ipynb) to see some common workflows on the quickstart dataset. Note that if you are running the above code in a script, you must include `session.wait()` to block execution until you close the App. See [this page](https://voxel51.com/docs/fiftyone/user_guide/app.html#creating-a-session) for more information. <div id='key-features'> ## <img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="20px"> &nbsp; key features &nbsp; 🔑 - **[Visualize Complex Datasets:](https://docs.voxel51.com/user_guide/app.html)** Easily explore images, videos, and associated labels in a powerful visual interface. https://github.com/user-attachments/assets/9dc2db88-967d-43fa-bda0-85e4d5ab6a7a - **[Explore Embeddings:](https://docs.voxel51.com/user_guide/app.html#embeddings-panel)** Select points of interest and view the corresponding samples/labels. https://github.com/user-attachments/assets/246faeb7-dcab-4e01-9357-e50f6b106da7 - **[Analyze and Improve Models:](https://docs.voxel51.com/user_guide/evaluation.html)** Evaluate model performance, identify failure modes, and fine-tune your models. https://github.com/user-attachments/assets/8c32d6c4-51e7-4fea-9a3c-2ffd9690f5d6 - **[Advanced Data Curation:](https://docs.voxel51.com/brain.html)** Quickly find and fix data issues, annotation errors, and edge cases. https://github.com/user-attachments/assets/24fa1960-c2dd-46ae-ae5f-d58b3b84cfe4 - **[Rich Integrations:](https://docs.voxel51.com/integrations/index.html)** Works with popular deep learning libraries like PyTorch, Hugging Face, Ultralytics, and more. https://github.com/user-attachments/assets/de5f25e1-a967-4362-9e04-616449e745e5 - **[Open and Extensible:](https://docs.voxel51.com/plugins/index.html)** Customize and extend FiftyOne to fit your specific needs. https://github.com/user-attachments/assets/c7ed496d-0cf7-45d6-9853-e349f1abd6f8 <div id='getting-started'/> ## <img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="20px"> &nbsp; documentation &nbsp; 🪪 Check out these resources to get up and running with FiftyOne: | [Getting Started Guides](https://voxel51.com/docs/fiftyone/getting_started_guides/index.html) | [Tutorials](https://voxel51.com/docs/fiftyone/tutorials/index.html) | [Recipes](https://voxel51.com/docs/fiftyone/recipes/index.html) | [User Guide](https://voxel51.com/docs/fiftyone/user_guide/index.html) | [Examples](https://github.com/voxel51/fiftyone-examples) | [API Reference](https://voxel51.com/docs/fiftyone/api/fiftyone.html) | [CLI Reference](https://voxel51.com/docs/fiftyone/cli/index.html) | | --------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- | --------------------------------------------------------------- | --------------------------------------------------------------------- | -------------------------------------------------------- | -------------------------------------------------------------------- | ----------------------------------------------------------------- | Full documentation is available at [fiftyone.ai](https://fiftyone.ai). </div> <div id='additional-resources'> ## <img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="20px"> &nbsp; additional resources &nbsp; 🚁 | [FiftyOne Enterprise](https://voxel51.com/enterprise) | [Building Plugins](https://docs.voxel51.com/plugins/index.html) | [Vector Search](https://voxel51.com/blog/the-computer-vision-interface-for-vector-search) | [Dataset Zoo](https://docs.voxel51.com/dataset_zoo/index.html) | [Model Zoo](https://docs.voxel51.com/model_zoo/index.html) | [FiftyOne Brain](https://docs.voxel51.com/brain.html) | [VoxelGPT](https://github.com/voxel51/voxelgpt) | | ----------------------------------------------------- | --------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | -------------------------------------------------------------- | ---------------------------------------------------------- | ----------------------------------------------------- | ----------------------------------------------- | </div> <div id='fiftyone-enterprise'/> ## <img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="20px"> &nbsp; FiftyOne Enterprise &nbsp; 🏎️ Want to securely collaborate on billions of samples in the cloud and connect to your compute resources to automate your workflows? Check out [FiftyOne Enterprise](https://voxel51.com/enterprise). <div id='faq'/> ## <img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="20px"> &nbsp; faq & troubleshooting &nbsp; ⛓️‍💥 Refer to our [common issues](https://docs.voxel51.com/installation/troubleshooting.html) page to troubleshoot installation issues. If you're still stuck, check our [frequently asked questions](https://docs.voxel51.com/faq/index.html) page for more answers. If you encounter an issue that the above resources don't help you resolve, feel free to [open an issue on GitHub](https://github.com/voxel51/fiftyone/issues) or contact us on [Discord](https://discord.gg/fiftyone-community). </div> <div id='community'/> ## <img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="20px"> &nbsp; join our community &nbsp; 🤝 Connect with us through your preferred channels: [](https://discord.gg/fiftyone-community) [](https://medium.com/voxel51) [](https://twitter.com/voxel51) [](https://www.linkedin.com/company/voxel51) [](https://www.facebook.com/voxel51) 🎊 **Share how FiftyOne makes your visual AI projects a reality on social media and tag us with @Voxel51 and #FiftyOne** 🎊 </div> <div id='contributors'/> ## <img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="20px"> &nbsp; contributors &nbsp; 🧡 FiftyOne and [FiftyOne Brain](https://github.com/voxel51/fiftyone-brain) are open source and community contributions are welcome! Check out the [contribution guide](https://github.com/voxel51/fiftyone/blob/develop/CONTRIBUTING.md) to learn how to get involved. Special thanks to these amazing people for contributing to FiftyOne! <a href="https://github.com/voxel51/fiftyone/graphs/contributors"> <img src="https://contrib.rocks/image?repo=voxel51/fiftyone" /> </a> <div id='citation'/> ## <img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="20px"> &nbsp; citation &nbsp; 📖 If you use FiftyOne in your research, feel free to cite the project (but only if you love it 😊): ```bibtex @article{moore2020fiftyone, title={FiftyOne}, author={Moore, B. E. and Corso, J. J.}, journal={GitHub. Note: https://github.com/voxel51/fiftyone}, year={2020} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"3050","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"