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v1.7 — Pre-labelling with your YOLO model

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@ILYAGRISH ILYAGRISH released this 08 Oct 16:50
· 13 commits to main since this release
v1.7
2cf56e6

Download & run (Windows)

  1. Download YOLO-Annotator-1.7-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. It creates a separate .venv-ml with PyTorch + Ultralytics (CUDA 12.8 build if an NVIDIA GPU is found, ~3 GB; setup_ml_env.bat --cpu for the CPU build). Or point ML → ML Settings… at any existing conda / venv with Ultralytics. The annotator itself never imports PyTorch.

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

What's new in v1.7

Pre-labelling with your YOLO model

  • ML → Pre-label Dataset… (Ctrl+Shift+L) runs any Ultralytics model — detect, segment, OBB, pose, classify — over all images or one split, with progress and Stop. Ctrl+L pre-labels the current image; one Ctrl+Z undoes it.
  • The project class decides the geometry: a segmentation model can fill polygon or brush-mask classes, a detector fills box / OBB / point classes, a pose model fills keypoint skeletons.
  • Class mapping by name, by hand in a searchable table, or create the selected / all missing classes with the right type (17-point pose models get the COCO skeleton; the empty default object class of a new project is replaced, so class ids start at 0).
  • Images that already have annotations: skip, replace earlier model annotations (manual ones kept), or add.
  • Model annotations are marked 🤖 0.87 (confidence) in the Annotations panel; ML → Remove Model Annotations… clears them on one image or everywhere.
  • Multi-user projects: only the images assigned to you are touched.
  • ~25 ms per image on an RTX 5060 Ti.

ML backend

  • Models run in a separate process with its own Python environment — PyTorch never enters the annotator, and a crash or out-of-memory in a model cannot take the app down.
  • ML Settings…: choose the interpreter, Check it (Python, PyTorch, Ultralytics, GPU), test-load a model. Status-bar indicator ML: off / ready / busy / error. Off by default — nothing starts until you use it.

Upgrading from 1.6: unpack into a new folder and run setup_venv.bat again — projects (.annproj) are compatible.

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