Replaces the bundled detector with Ultralytics' YOLOv8x trained on
Open Images V7 — 601 object classes including coffee cup, mug, wine
glass, mobile phone, computer monitor, sunglasses, laptop, plus body
parts (human face / hand / eye / arm / leg). Substantially better
"is that a coffee mug" detection than the 80-class COCO model.
Conversion
- Ultralytics' yolov8x-oiv7.pt → Core ML mlpackage via
ultralytics.YOLO(...).export(format="coreml", nms=True). Output is a
Vision-friendly pipeline (preprocessing + model + NMS), so
VNCoreMLRequest continues to return [VNRecognizedObjectObservation]
directly with no manual decoding.
- scripts/convert-yolo-oiv7.py is the conversion entry point.
- scripts/fetch-model.sh rewritten as a no-LFS fallback that
bootstraps a Python venv, installs coremltools + ultralytics, runs
the conversion, and drops the result into Sources/Resources/Models/.
Bundling
- yolov8x-oiv7.mlpackage is a directory (~131 MB weight.bin + small
model.mlmodel descriptor + Manifest.json).
- .gitattributes adds LFS tracking for **/weight.bin and
**/model.mlmodel inside Sources/Resources/Models so the contents go
to LFS without the surrounding directory metadata pretending to be
binary.
- Old YOLOv3.mlmodel removed from the repo (still in LFS history if
needed).
Theme
- Theme.Performance.detectorModelName updated to "yolov8x-oiv7" (the
detector falls back to mlpackage extension automatically via the
existing Bundle.main.url lookup).
README + scripts updated to reflect the new model and 601-class
vocabulary.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
james.maesandClaude Opus 4.7
0d46971563