metadata
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. 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% |