Object Detection Studio (Pro)
The full end-to-end pipeline for training your own object-detection model. Six tabs at the top.
Object Detection Studio · Annotations
Build a YOLO dataset visually
Snap frames into the dataset, draw bounding boxes, split train / val, and export in YOLO format. Optional auto-segmentation speeds up labelling.
- Class list · bbox drawing
- Train / val split
- YOLO export
Tabs
- Capture — pick a target window and hotkey, collect screenshots into a dataset. Left-side list shows each capture; stats show total / labelled / unlabelled / train-val split / class count.
- Annotations — define classes on the right, draw boxes on the canvas and pick a class per box. Pan / zoom the image. Verify saved overlays the saved boxes to confirm. Click-assist: click an object once and a helper model draws the box (SAM 2). Auto-detect: scans all unlabelled captures and suggests boxes.
- Pre-Training — model size (small / medium / large), image size, epochs, batch size (or Auto). Export dataset packs images, boxes, class list, settings and a training notebook into a single ZIP. Fast transfer via Google Drive for Desktop is optional.
- Training — step-by-step for Colab (upload notebook, switch to GPU, Run all, download best.onnx, put in Models). "Open Colab in browser" opens Colab in your normal browser, reusing your Google session.
- Models — activate a trained model and live-test it on a chosen window. Controls: CPU/GPU inference, confidence slider, class filter, size filter (two independent lists: include and ignore), Start/Stop the live test.
- Settings — dataset location, capture-flow hotkeys, dataset folder rescan, and a Clear All Annotations action with a confirm.
The Training tab intentionally has no embedded browser: AIWeavio often needs to run as administrator (games with anti-cheat), and embedded Chromium can't run under administrator. Your normal browser has no such limits and already has your Google session signed in.