This repository contains a fine-tuned EfficientNetB4 model trained on the Food101 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
