NorgesGruppen Grocery Shelf Object Detection
YOLOv8x model fine-tuned for grocery product detection and classification on store shelf images.
Trained for the NM i AI 2026 competition (NorgesGruppen Data task).
Results
Public leaderboard score: 0.8969
Scoring: 0.7 × detection mAP@0.5 + 0.3 × classification mAP@0.5
Model Details
- Architecture: YOLOv8x
- Training resolution: 1280px
- Classes: 356 grocery product categories
- Training data: 248 shelf images, ~22,700 annotations
- Augmentation: copy_paste=0.3, mixup=0.15, scale=0.5, mosaic=1.0
- Weights format: FP16 PyTorch (.pt), 132MB
- Framework: ultralytics 8.1.0
Usage
Inference Settings
The best public score was achieved with:
- (FP16)
- Simple single-pass inference (no SAHI, no TTA, no ensemble)
GitHub
