SanderHelgesen/grocery-detection-nmiai

Grocery Product Detection — NM i AI Competition

Ensemble model for detecting and classifying grocery products on store shelves.

Models

• yolo26x_1600.onnx — YOLO26x detector trained at 1600px resolution
• rfdetr_large_704.onnx — RF-DETR Large detector at 704px resolution
• efficientnet_b4_classifier.safetensors — Product classifier (356 classes, FP16)
• yolo26x_best.pt — YOLO26x PyTorch weights (accuracy-focused, 500 epochs)
• yolo26x_finetuned.pt — Fine-tuned on refined Roboflow dataset

Architecture

Two-stage ensemble:

1. Detection: YOLO26x + RF-DETR with Weighted Boxes Fusion
2. Classification verification: EfficientNet-B4 corrects low-confidence class predictions

Competition Score

• Best public leaderboard: 0.9021
• Scoring: 0.7 × detection_mAP@0.5 + 0.3 × classification_mAP@0.5

Training Data

• 248 shelf images from Norwegian grocery stores
• 22,731 COCO-format bounding box annotations
• 356 product categories
• Additional refined annotations via Roboflow