ngd-challenge-weights – AI Model by ahmetacer5 | AlphaNeural AI

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ahmetacer5/
ngd-challenge-weights

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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

License

MIT License