ahmetacer5/ngd-challenge-weights
By ahmetacer5 · ↓ 0 · ♥ 0 · Updated 2026-03-22
What's in this repo
This page lists the files in ahmetacer5/ngd-challenge-weights, including model1.onnx, model2.onnx.
- 2 ONNX models (.onnx files)
- 4 files, including 2 .onnx, 1 .md
- Last updated 2026-03-22
ONNX files
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
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
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
Model info
onnx
object-detection
yolov8
grocery
retail
dataset:custom
license:mit
region:us
↓ 0
♥ 0
Created: 2026-03-22
Updated: 2026-03-22
View on Hugging FaceFiles (4)
.gitattributes
README.md
model1.onnx
ONNX
model2.onnx
ONNX
Related models
More from ahmetacer5
No other models by this author.