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---
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](https://huggingface.co/datasets/openfoodfacts/front_image_classification) dataset was used to train the model.
### Training Procedure
- Epochs: 100
- Image size: 448
- Albumentation augmentation
[This script](https://github.com/openfoodfacts/openfoodfacts-ai/blob/dbbec40a3d964124cd7c8d838023be4a10d6c0be/front-image-classification/train.py) was used for training the model.
The augmentation pipeline used for prediction:
```python
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`. |