You can either set up the tool with the provided docker image or build the tool by yourself. Once you have the tool set up, you can open http://localhost:8686 to create your annotation project. Below is a quick way to install dependencies and launch the Scalabel server. After launching the server, you can directly jump to Quick Start.

Note: You only need to do either Step 3 or 4, but not both.

  1. Check out the code

    git clone
    cd scalabel
  2. Prepare the local data directories

    bash scripts/

    If you have a local folder of images or point clouds to label, you can move them to local-data/items. After launching the server (finishing Step 3 or 4), the url for the images will start with http://localhost:8686/items, assuming the port in the scalabel config is 8686. The url of the example image local-data/items/examples/cat.webp is http://localhost:8686/items/examples/cat.webp. Any files in the items folder and subfolders will be served. Files at local-data/items/{subpath} are available at {hostname}/items/{subpath}.

  3. Using Docker

    Download from dockerhub

    docker pull scalabel/www

    Launch the server

    docker run -it -v "`pwd`/local-data:/opt/scalabel/local-data" -p \
        8686:8686 -p 6379:6379 scalabel/www \
        node app/dist/main.js \
        --config /opt/scalabel/local-data/scalabel/config.yml \

    Depending on your system, you may also have to increase docker’s memory limit (8 GB should be sufficient).

  4. Build the code yourself

    This is an alternative to using docker. We assume you have already installed Homebrew if you are using Mac OS X and you have apt-get if you are on Ubuntu. The code requires Python 3.7 or above. Please check Installation Tips if you don’t have the right version. We use 3.8 by default. Depending on your OS, run the script

    bash scripts/


    bash scripts/

    If you are on Ubuntu, you may need to run the script with sudo.

    Then you can launch the server using node:

    node --max-old-space-size=8192 \
        app/dist/main.js --config ./local-data/scalabel/config.yml

    Depending on your system, you may also have to increase the memory limit from 8192 (8 GB).

  5. Get labels

    The collected labels can be directly downloaded from the project dashboard. The labels follow Scalabel Format. You can visualize the labels by

    python3 -m scalabel.vis.labels -l <your_downloaded_label_path.json>