v1.9 — Review of pre-labelled annotations

Download & run (Windows)

1. Download YOLO-Annotator-1.9-windows.zip below and unpack it.
2. Install Python 3.12+ (3.13 recommended) 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.

For the AI features (YOLO pre-labelling and SAM): run setup_ml_env.bat once — a separate .venv-ml with PyTorch + Ultralytics (CUDA 12.8 build if an NVIDIA GPU is found, ~3 GB; --cpu for the CPU build) — or point ML → ML Settings… at an existing conda / venv with Ultralytics. The annotator itself never imports PyTorch.

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

What's new in v1.9

Review of pre-labelled annotations

A model makes mistakes, so every 🤖 annotation should be looked at — the app now remembers what has been reviewed and walks you through the rest.

• Unreviewed model annotations stand out: dashed outline, paler fill and 🤖 on the canvas, an orange row in the Annotations panel, 🤖N next to each image in the image list.
• New Review menu:
  • R accepts the selected annotation and moves to the next unreviewed one — press R, R, R…, delete what's wrong;
  • Shift+R accepts everything on the image and jumps to the next image to review;
  • U goes there without accepting;
  • "Mark selected as unreviewed" undoes a mistaken accept.
• Editing accepts too: move a box or fix a vertex and the annotation counts as reviewed (one Ctrl+Z restores both). Accepted ones show ✓🤖 and who accepted them (File → Your Name).
• Image filter 🤖 Unreviewed, QC rule Unreviewed (double-click jumps to it) and a 🤖 Review line in the statistics.
• Export → Skip unreviewed 🤖 annotations exports only what a person has checked (off by default).
• A new pre-labelling run (replace mode, Ctrl+L) and Remove Model Annotations now keep accepted annotations, like manual ones.
• Keys can be changed in Project Settings → Hotkeys. The status is stored in annotation meta — older projects open unchanged.

Quality

• Continuous integration: all test suites (~900 checks) now run on every push on Windows and Ubuntu with Python 3.12 and 3.13.
• Fix: setup_venv.bat accepted Python 3.10 / 3.11, but the pinned NumPy 2.5 needs Python 3.12+ — installation on 3.10/3.11 failed. The installer now looks for 3.12+ and says so.

Upgrading from 1.8: unpack into a new folder and run setup_venv.bat again — projects (.annproj) are compatible. For pre-labelling and SAM, keep using your existing ML environment (in ML → ML Settings… choose the python.exe of the old folder's .venv-ml\Scripts, or your conda env) or run setup_ml_env.bat in the new folder — don't copy .venv-ml between folders.