license: agpl-3.0
datasets:
- openfoodfacts/front_image_classification
base_model:
- Ultralytics/YOLO11
metrics:
- accuracy
Front image classification model
This model classifies Open Food Facts images into two classes:
front(ID 0)other(ID 1)
Front images are the "default" image of a product, displayed on Open Food Facts product page. A front image is most of the time a photo of the front side of the product packaging. It's useful to be able to detect front images so that we can update the front image with a newer version (when the packaging changes for example).
Model Details
Model Description
- Developed by: Raphaël Bournhonesque
- Model type: Image Classification
- License: AGPL 3.0
- Finetuned from model [optional]: Yolo11n-cls
Uses
This model is intended to be used on Open Food Facts images only (images of food packaged products).
Training Details
Training Data
v1.0 of the front_image_classification dataset was used to train the model.
Training Procedure
- Epochs: 100
- Image size: 448
- Albumentation augmentation
This script was used for training the model.
The augmentation pipeline used for prediction:
A.Compose(
[
A.LongestMaxSize(max_size=max_size, p=1.0),
A.PadIfNeeded(min_height=max_size, min_width=max_size, p=1.0),
A.Normalize(mean=DEFAULT_MEAN, std=DEFAULT_STD, p=1.0),
ToTensorV2(p=1.0),
]
)
For optimal performance, it is advised to keep the same preprocessing pipeline during inference.
Evaluation
accuracy: 0.9525
Export
An ONNX export can be found in weights/model.onnx.