Amenity Website Images
This image classification model can be used to classify images crawled from amenity websites into a fixed set of categories:
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 2,86–5,52 MB
- Modelo base
- yolov8n-cls.pt
- Versión
- vRevision 3e0d27e
- Actualizado
- 22 oct 2026
- Descargas
- 18
- Fuente
- Hugging Face
Descripción de la fuente
Amenity website image classifier
This image classification model can be used to classify images crawled from amenity websites into a fixed set of categories:
amenity_inside(ID:0): photos of the inside of the amenity, possibly with persons displayedamenity_outside(ID:1): photos of the outside of the amenity: can be possibly be a photo of the facade of the amenity.food_and_drink(ID:2): photos of the food or drinks that are served in the amenity (mostly for restaurants/cafe/hotels)other(ID:3): all other images, including icons, logos,...
The model was fine-tuned using the Ultralytics YOLO library.
Model Details
Model Description
- Developed by: Raphaël Bournhonesque
- Model type: image classification
- License: agpl-3.0
- Finetuned from model [optional]: yolov8n-cls.pt
Training Details
Training Data
The model was fine-tuned using the following dataset: raphael0202/amenity-website-images (revision: main).
As the dataset contains mostly images crawled from websites of restaurants and coffee shop, the model may exhibit lower accuracy on other types of amenities.
Training Procedure
Dependency versions:
- ultralytics: 8.4.14
- pytorch: 2.9.0+cu128
Training Hyperparameters
- Epochs: 100
- Batch size: 16
- Image size: 640
Evaluation
The following evaluation metrics were obtained after training the model:
-
metrics/accuracy_top1: 0.90183025598526
-
metrics/accuracy_top5: 1.0
-
fitness: 0.95091512799263
Evaluation on exported models
The model was also evaluated after exporting to ONNX format. The following metrics were obtained:
ONNX export
-
metrics/accuracy_top1: 0.90183025598526
-
metrics/accuracy_top5: 1.0
-
fitness: 0.95091512799263
Files
Most files stored on the repo are standard files created during training with the Ultralytics YOLO library.
What was added:
- an ONNX export of the trained model (best model), stored in
weights/model.onnx. - a Parquet file containing predictions on the full dataset, stored in
predictions.parquet. - metrics JSON files for each exported model format, stored in
metrics_*.json:metrics.json: metrics for the original PyTorch modelmetrics_onnx.json: metrics for the ONNX exported model
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 22 oct
Artefactos del modelo
3 artefactos
Artefactos del modelo
3 artefactosExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- base_model: yolov8n-cls.pt datasets: - raphael0202/amenity-website-images library_name: ultralytics license: agpl-3.0 pipeline_tag: image-classification --- # Amenity website image classifier [Wandb tracking run](https://wandb.ai/raphael-b-org/amenity-website-images/runs/yolov8n_20260710_095311) This image classification model can be used to classify images crawled from amenity websites into a fixed set of categories: - `amenity_inside` (ID: `0`): photos of the inside of the amenity, possibly with persons displayed - `amenity_outside` (ID: `1`): photos of the outside of the amenity: can be possibly be a photo of the facade of the amenity. - `food_and_drink` (ID: `2`): photos of the food or drinks that are served in the amenity (mostly for restaurants/cafe/hotels) - `other` (ID: `3`): all other images, including icons, logos,... The model was fine-tuned using the Ultralytics YOLO library. ## Model Details ### Model Description - **Developed by:** Raphaël Bournhonesque - **Model type:** image classification - **License:** agpl-3.0 - **Finetuned from model [optional]:** yolov8n-cls.pt ## Training Details ### Training Data The model was fine-tuned using the following dataset: [raphael0202/amenity-website-images](https://huggingface.co/datasets/raphael0202/amenity-website-images) (revision: `main`). As the dataset contains mostly images crawled from websites of restaurants and coffee shop, the model may exhibit lower accuracy on other types of amenities. ### Training Procedure Dependency versions: - ultralytics: 8.4.14 - pytorch: 2.9.0+cu128 #### Training Hyperparameters - **Epochs:** 100 - **Batch size:** 16 - **Image size:** 640 ## Evaluation The following evaluation metrics were obtained after training the model: - **metrics/accuracy_top1:** 0.90183025598526 - **metrics/accuracy_top5:** 1.0 - **fitness:** 0.95091512799263 ### Evaluation on exported models The model was also evaluated after exporting to ONNX format. The following metrics were obtained: #### ONNX export - **metrics/accuracy_top1:** 0.90183025598526 - **metrics/accuracy_top5:** 1.0 - **fitness:** 0.95091512799263 ## Files Most files stored on the repo are standard files created during training with the Ultralytics YOLO library. What was added: - an ONNX export of the trained model (best model), stored in `weights/model.onnx`. - a Parquet file containing predi...
Source context: 18 downloads · 0 likes · Pipeline image-classification · Library ultralytics · Repo raphael0202/amenity-website-images
Source context: 214 downloads · 0 likes · Pipeline image-classification · Library ultralytics · Repo raphael0202/amenity-website-images
