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README.md

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+---
+license: mit
+tags:
+- object-detection
+- yolov8
+- grocery
+- retail
+- onnx
+datasets:
+- custom
+metrics:
+- map
+---
+
+# NorgesGruppen Data Challenge - Model Weights
+
+YOLO ensemble weights for grocery shelf product detection (356 categories).
+
+## Model Details
+
+| Model | Architecture | Resolution | Size | Format |
+|-------|-------------|------------|------|--------|
+| model1.onnx | YOLOv8m | 640px | 94 MB | ONNX |
+| model2.onnx | YOLOv8x | 1280px | 249 MB | ONNX |
+
+## Performance
+
+- **Score**: 0.9006 (mAP@0.5)
+- **Competition**: NM i AI 2026 - NorgesGruppen Data Challenge
+
+## Usage
+
+```python
+import onnxruntime as ort
+from PIL import Image
+import numpy as np
+
+# Load model
+session = ort.InferenceSession("model1.onnx", providers=["CUDAExecutionProvider"])
+
+# Preprocess image (letterbox to 640x640)
+img = Image.open("shelf.jpg").convert("RGB")
+# ... preprocessing code ...
+
+# Run inference
+output = session.run(None, {"images": img_array})[0]
+```
+
+## Source Code
+
+Full training and inference code: [GitHub - ngd-challenge](https://github.com/ahmetacer5/ngd-challenge)
+
+## License
+
+MIT License