
# Food Classification

This is a Food Image Classifier model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize 7 different types of popular foods, including **apple pie**, **falafel**, **french toast**, **ice cream**, **ramen**, **sushi**, and **tiramisu**. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience.

### Gradio

Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model:
[![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/Food-Classification_App)


## Validation Metrics

- Loss: 0.094
- Accuracy: 0.977
- Macro F1: 0.977
- Micro F1: 0.977
- Weighted F1: 0.977
- Macro Precision: 0.978
- Micro Precision: 0.977
- Weighted Precision: 0.978
- Macro Recall: 0.977
- Micro Recall: 0.977
- Weighted Recall: 0.977
{"tags": ["vision", "image-classification"], "datasets": ["Kaludi/data-food-classification"], "widget": [{"src": "https://kristineskitchenblog.com/wp-content/uploads/2021/04/apple-pie-1200-square-592-2.jpg", "example_title": "Apple Pie"}, {"src": "https://upload.wikimedia.org/wikipedia/commons/d/da/Strawberry_ice_cream_cone_%285076899310%29.jpg", "example_title": "Ice Cream"}, {"src": "https://cdn.britannica.com/52/128652-050-14AD19CA/Maki-zushi.jpg", "example_title": "Sushi"}], "co2_eq_emissions": {"emissions": 2.7745203231331614}}
transformers pytorch swin image-classification vision dataset:Kaludi/data-food-classification co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #swin #image-classification #vision #dataset-Kaludi/data-food-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Food Classification

This is a Food Image Classifier model that has been trained by Kaludi to recognize 7 different types of popular foods, including apple pie, falafel, french toast, ice cream, ramen, sushi, and tiramisu. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience.

### Gradio

Tis model supports a Gradio Web UI to run the data-food-classification model:
![Open In HF Spaces](URL


## Validation Metrics

- Loss: 0.094
- Accuracy: 0.977
- Macro F1: 0.977
- Micro F1: 0.977
- Weighted F1: 0.977
- Macro Precision: 0.978
- Micro Precision: 0.977
- Weighted Precision: 0.978
- Macro Recall: 0.977
- Micro Recall: 0.977
- Weighted Recall: 0.977
# Food Classification

This is a Food Image Classifier model that has been trained by Kaludi to recognize 7 different types of popular foods, including apple pie, falafel, french toast, ice cream, ramen, sushi, and tiramisu. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience. ### Gradio

Tis model supports a Gradio Web UI to run the data-food-classification model:
![Open In HF Spaces](URL ## Validation Metrics

- Loss: 0.094
- Accuracy: 0.977
- Macro F1: 0.977
- Micro F1: 0.977
- Weighted F1: 0.977
- Macro Precision: 0.978
- Micro Precision: 0.977
- Weighted Precision: 0.978
- Macro Recall: 0.977
- Micro Recall: 0.977
- Weighted Recall: 0.977
TAGS
#transformers #pytorch #swin #image-classification #vision #dataset-Kaludi/data-food-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Food Classification

This is a Food Image Classifier model that has been trained by Kaludi to recognize 7 different types of popular foods, including apple pie, falafel, french toast, ice cream, ramen, sushi, and tiramisu. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience. ### Gradio

Tis model supports a Gradio Web UI to run the data-food-classification model:
![Open In HF Spaces](URL ## Validation Metrics

- Loss: 0.094
- Accuracy: 0.977
- Macro F1: 0.977
- Micro F1: 0.977
- Weighted F1: 0.977
- Macro Precision: 0.978
- Micro Precision: 0.977
- Weighted Precision: 0.978
- Macro Recall: 0.977
- Micro Recall: 0.977
- Weighted Recall: 0.977
TAGS
#transformers #pytorch #swin #image-classification #vision #dataset-Kaludi/data-food-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Food Classification

This is a Food Image Classifier model that has been trained by Kaludi to recognize 7 different types of popular foods, including apple pie, falafel, french toast, ice cream, ramen, sushi, and tiramisu. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience.### Gradio

Tis model supports a Gradio Web UI to run the data-food-classification model:
![Open In HF Spaces](URL## Validation Metrics

- Loss: 0.094
- Accuracy: 0.977
- Macro F1: 0.977
- Micro F1: 0.977
- Weighted F1: 0.977
- Macro Precision: 0.978
- Micro Precision: 0.977
- Weighted Precision: 0.978
- Macro Recall: 0.977
- Micro Recall: 0.977
- Weighted Recall: 0.977