Downloads · 30 days
0
ngd-challenge-weights
ahmetacer5/ngd-challenge-weights
ngd-challenge-weights is a object detection model from ahmetacer5. Use it when you need objects located in an image. The card lists the license as mit.
YOLO ensemble weights for grocery shelf product detection (356 categories).
Access
Public
Updated Mar 22, 2026
Repo size
342 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.onnx342 MB · 100%
At a glance
- Task
- Object Detection
- License
- mit
- Access
- Public
- Created
- Mar 22, 2026
- Updated
- Mar 22, 2026
- SHA
- 89687f46
Datasets
Model card
From the Hugging Face model 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 ([email protected])
- 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
- Task
- Object Detection
- License
- mit
- Created
- Mar 22, 2026
- Updated
- Mar 22, 2026