Quick start

Get from zero to a running local ML Studio in a few minutes.

1. Install

pip install "oneopen-ml-studio[yolo]"

2. Initialize data directory

oneopen init

Creates ~/.oneopen (or ONEOPEN_DATA_DIR) with SQLite and folders for projects, exports, and models.

3. Start the server

oneopen start

Open http://127.0.0.1:8765.

(oneopen serve is the same command.)

Important

Local mode (default) binds 127.0.0.1 and does not require login. For Team/Enterprise, set ONEOPEN_DEPLOYMENT_MODE=team (or enterprise), open http://127.0.0.1:8765/setup, then use sessions or API tokens. Do not expose Local mode on untrusted networks.

Useful options

oneopen start --port 9000
oneopen start --host 127.0.0.1 --reload

4. Create a project

  1. New project (or /projects/new).

  2. Name + type — object detection is the recommended first path (classification and segmentation also work).

  3. Optional custom location; blank → ~/.oneopen/projects/<id>/.

5. Add classes and images

  1. Add at least one class.

  2. Upload images or import a YOLO folder/zip.

6. Annotate (visual)

  1. Open Annotate.

  2. Draw boxes or polygons (or assign a whole-image class for classification projects).

  3. Optionally run Label Assist (YOLO) or SAM point prompts, then correct.

7. Version, train, or export

  1. Assign splits.

  2. Create a dataset version.

  3. Export and/or Train locally (Ultralytics / LibreYOLO / Torchvision).

Typical first workflow (CV)

Create project → Add classes → Upload images
        ↓
   Annotate (manual + Label Assist / SAM)
        ↓
 Assign splits → Generate version
        ↓
   Export YOLO/COCO  and/or  Train locally

Next steps