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Installing VisionSet

One wheel contains the API, CLI, MCP server, and compiled browser application. Nothing else must be downloaded afterward, and there is no separate frontend to serve.

Requirements
Python 3.11 or newer
Disk your images, plus a copy: assets are content-addressed into the workspace

Nothing else. No database server, no Node, no Docker, and no media binary — not even for video; see below. The metadata lives in one SQLite file inside the workspace, and the pixels live beside it.

Install

VisionSet is not on PyPI yet - that lands with the first beta. Until then, install from a built wheel or straight from the repository:

# from the repository, into an isolated environment (recommended)
uv tool install "git+https://github.com/Robomous/VisionSet"

# or into the current environment
pip install "git+https://github.com/Robomous/VisionSet"

To build the wheel yourself - which is what CI does, and what publishing will ship:

git clone https://github.com/Robomous/VisionSet && cd VisionSet
uv sync && pnpm install
bash scripts/build_dist.sh          # pnpm -r build → bundle:static → uv build
pip install dist/visionset-*.whl

That order matters and the script enforces it: the compiled UI is copied into the package immediately before the wheel is built, so a wheel built out of order installs cleanly and then serves nothing. See CONTRIBUTING.md.

Check it

visionset --version         # the version, and nothing else
visionset format list       # the formats this installation can write

format list is the more useful of the two: it reads installed entry-point metadata, so a non-empty answer proves the wheel is properly installed rather than merely importable. Run it for the current set rather than trusting a list written down somewhere - third-party distributions register into the same entry-point group, so what a given installation can write is a property of that installation.

Video, and where it is decoded

VisionSet needs no media binary at all, for images or for video. There is nothing to install and nothing this page can tell you to run.

Importing a video is a browser capability: the browser demuxes and decodes it with its own built-in WebCodecs support and turns it into the image assets a project is made of before the server ever hears about it. There is no server-side decoder behind that, and no fallback - a browser that cannot decode a given codec says so, in the browser, rather than quietly handing the file to a server that would have decoded it instead. See ingest.md.

Running a model on this machine

Only for local inference connections, and only when you have made one. VisionSet's auto-labeling feature is always present; what is optional is the runtime that executes a model here:

pip install "visionset[local-inference]"

That brings torch, torchvision, transformers, accelerate and huggingface_hub - roughly two gigabytes, most of it CUDA - which is exactly why it is not in the base install. It is the same command on every platform: the macOS wheels it installs carry Apple Silicon GPU support already, so a Mac needs no second index and no build flag to run a connection on mps. Without it you can still create a local connection, list it, and see what it is configured for; what you cannot do is fetch its weights or ask it to predict. Both refusals name the command above rather than simply saying "unavailable".

Installing it downloads no model. Weights arrive when you run visionset inference download, never at install time, never at startup, and never on the way to anything else. They land inside the workspace, under models/, so a workspace you copy to another machine takes its model with it. See inference.md.

Optional extras, for checking exports

Neither is needed to use VisionSet; both are what the project's own tests use to prove an export is loadable by the tool it is aimed at.

uv sync --group yolo    # ultralytics — brings torch, roughly two gigabytes
uv sync --group coco    # pycocotools

The yolo group has a wart worth knowing before you install it into a checkout: the ultralytics wheel ships a top-level tests package, which shadows this repository's own tests/ directory. Run the format smoke tests and then uv sync again; see CONTRIBUTING.md.

Where your data goes

Nowhere you did not name. A workspace is a directory you create:

visionset init ~/datasets/road-signs

Inside it: visionset.db (metadata) and blobs/ (content-addressed pixels), plus three directories that appear only once something puts them there — uploads/ for bytes sent to the REST API, exports/ for what an export wrote, and models/ for weights you fetched. Nothing is uploaded, nothing phones home, and no path outside that directory is written except where you point an export. workspaces.md has the whole layout.

init is the only command that creates a workspace, and it refuses a directory that already holds something. Every other command finds one - --workspace, then $VISIONSET_WORKSPACE, then the nearest workspace at or above the working directory. The full precedence, and why only the last of those searches upward, is in workspaces.md.

Next

  • tutorial.md - a first dataset, end to end, in about half an hour.
  • cli.md - the whole cycle from a terminal.
  • mcp.md - pointing an agent at a workspace.