---
license: mit
datasets:
- ethz/food101
language:
- en
metrics:
- accuracy
base_model:
- google/efficientnet-b4
pipeline_tag: image-classification
library_name: keras
tags:
- computer-vision
- classification
- deep-learning
- efficientnet
---

# EfficientNetB4 Fine-Tuned on Food101

This repository contains a fine-tuned EfficientNetB4 model trained on the [Food101 dataset](https://huggingface.co/datasets/mhamza-007/multi-class-food-dataset). The Food101 dataset comprises 101 different classes of food, making it an excellent benchmark for image classification tasks in the food domain.

## Model Details

- **Base Architecture**: EfficientNetB4 (pre-trained on ImageNet)
- **Fine-Tuning Layers**: Last 10 layers unfrozen
- **Number of Classes**: 101 (Food101)
- **Input Shape**: (224, 224, 3)

## Training Configuration

- **Epochs**: 10  
- **Batch Size**: 32  
- **Optimizer**: Adam  
- **Learning Rate**: 0.0001  
- **Loss Function**: `sparse_categorical_crossentropy`  
- **Metrics**: `accuracy`  
- **Validation Split**: 0.15  
- **Fine-Tuning**: Unfreezing last 10 layers of the base model

## Performance

| Phase        | Loss    | Accuracy |
|--------------|---------|----------|
| **Train** | 0.4790  | 87.40%   |
| **Test**     | 0.9583  | 74.28%   |