v1.7.1 — YOLO export fixes

Download & run (Windows)

1. Download YOLO-Annotator-1.7.1-windows.zip below and unpack it.
2. Install Python 3.12+ (3.13 recommended; 3.10 / 3.11 do not work — NumPy 2.5 needs 3.12) with "Add python.exe to PATH".
3. Double-click setup_venv.bat once (creates .venv, installs pinned dependencies, ~150 MB).
4. Start with run.bat.

Optional pre-labelling with a YOLO model: run setup_ml_env.bat once (or point ML → ML Settings… at an existing conda / venv with Ultralytics) — see the v1.7 notes.

User guide (RU): About.md inside the archive.

What's fixed in v1.7.1

• YOLO OBB export on non-square images. Rotated boxes were rotated in normalised coordinates, so on a non-square image the exported corners did not match the box drawn on the canvas. They are now rotated in pixels, exactly as drawn. Also applies to the auto-saved labels/.
• YOLO class ids after deleting a class. Labels used the project class id while data.yaml lists names in a row, so deleting a class in the middle of the schema shifted the names and produced ids ≥ nc. All YOLO exports, multi-task export and labels/ now use indices 0…nc−1 in the order of names.
• Docs: recipe for creating a new conda environment for pre-labelling.

If you trained on YOLO OBB exports of non-square images, or exported after deleting a class, please re-export your dataset.

Upgrading from 1.7: unpack into a new folder and run setup_venv.bat again — projects (.annproj) are compatible. For pre-labelling, either keep using your existing .venv-ml (in ML → ML Settings… choose the python.exe inside the old folder's .venv-ml\Scripts) or run setup_ml_env.bat in the new folder — don't copy .venv-ml between folders.