james.maesandClaude Opus 4.7
0d46971563
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>
58 lines
1.8 KiB
Bash
Executable File
58 lines
1.8 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Fallback for environments without Git LFS. Re-runs the YOLO conversion
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# from Ultralytics' published Open Images V7 weights — produces a Core ML
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# mlpackage with 601 object classes in Sources/Resources/Models/.
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#
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# Run once after cloning, if `git lfs pull` is unavailable or didn't fetch
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# the model:
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# ./scripts/fetch-model.sh
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#
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# Requirements:
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# - python3.12 or python3.13 (3.14 isn't supported by coremltools yet)
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# - ~3 GB free disk for the venv (PyTorch + ultralytics + coremltools)
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# - ~250 MB network for the pretrained weights
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set -euo pipefail
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ROOT="$(cd "$(dirname "$0")/.." && pwd)"
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MODELS_DIR="$ROOT/Sources/Resources/Models"
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MODEL_NAME="yolov8x-oiv7"
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DEST="$MODELS_DIR/$MODEL_NAME.mlpackage"
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VENV="${VPOC_VENV:-/tmp/visionpoc-venv}"
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mkdir -p "$MODELS_DIR"
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if [[ -d "$DEST" ]] && [[ -f "$DEST/Data/com.apple.CoreML/weights/weight.bin" ]]; then
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size=$(stat -f%z "$DEST/Data/com.apple.CoreML/weights/weight.bin")
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if [[ "$size" -gt 10000000 ]]; then
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echo "$MODEL_NAME.mlpackage already present and looks valid. Skipping rebuild."
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exit 0
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fi
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fi
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PY_BIN=""
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for cand in python3.13 python3.12; do
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if command -v "$cand" >/dev/null 2>&1; then
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PY_BIN="$cand"
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break
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fi
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done
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if [[ -z "$PY_BIN" ]]; then
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echo "Need python3.12 or python3.13. Install via: brew install python@3.13" >&2
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exit 1
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fi
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if [[ ! -d "$VENV" ]]; then
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echo "Creating Python venv at $VENV…"
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"$PY_BIN" -m venv "$VENV"
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fi
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echo "Installing coremltools + ultralytics into $VENV (this takes a couple of minutes)…"
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"$VENV/bin/pip" install --quiet --upgrade pip
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"$VENV/bin/pip" install --quiet coremltools ultralytics
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echo "Running conversion…"
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"$VENV/bin/python" "$ROOT/scripts/convert-yolo-oiv7.py"
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echo "Done. Set Theme.Performance.detectorModelName to \"$MODEL_NAME\" (default)."
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