| | --- |
| | 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** | 0.6283 | 79.28% | |
