---
license: apache-2.0
tags:
  - object-detection
  - grocery
  - yolo
  - rf-detr
  - ensemble
datasets:
  - custom
---

# 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