| --- |
| license: mit |
| datasets: |
| - Voxel51/Food101 |
| language: |
| - en |
| metrics: |
| - accuracy |
| base_model: |
| - timm/tf_efficientnetv2_s.in21k_ft_in1k |
| new_version: timm/tf_efficientnetv2_s.in21k |
| pipeline_tag: image-classification |
| tags: |
| - code |
| --- |
| # Food Classifier (Food-101) |
|
|
| A deep learning–based food image classification project trained on the **Food-101** dataset using **PyTorch**. |
| The model predicts food categories from images and is designed for real-world usage and future mobile deployment. |
|
|
| --- |
|
|
| ## Project Overview |
|
|
| This project focuses on building a high-accuracy food image classifier by fine-tuning a pretrained convolutional neural network (CNN). |
| It serves as both a learning project and a foundation for future applications such as mobile food recognition apps. |
|
|
| --- |
|
|
| ## 🧠 Model Architecture |
|
|
| - **Base model:** EfficientNetV2-S (pretrained on ImageNet) |
| - **Framework:** PyTorch |
| - **Training strategy:** Transfer learning with fine-tuning |
| - **Input size:** 224 × 224 RGB images |
| - **Output:** Food category probabilities (Softmax) |
|
|
| EfficientNetV2 was chosen for its strong balance between accuracy and computational efficiency. |
|
|
| --- |
|
|
| ## Dataset |
|
|
| - **Dataset:** Food-101 |
| - **Number of classes:** 101 food categories |
| - **Images per class:** ~1,000 |
| - **Total images:** 101,000 |
|
|
| The dataset contains diverse real-world food images with varying lighting, angles, and backgrounds. |
|
|
| 🔗 Dataset source: |
| https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/ |
| |
| --- |
| |
| ## Training Details |
| |
| - **Optimizer:** Adam |
| - **Loss function:** Cross-Entropy Loss |
| - **Data augmentation:** |
| - Random resize & crop |
| - Horizontal flip |
| - Normalization |
| - **Validation split:** Used for model selection and checkpointing |
| |
| --- |
| |
| ## Model Performance |
| |
| | Metric | Result | |
| |------|------| |
| | **Top-1 Accuracy** | **96%** (validation) | |
| | **Loss** | Low and stable | |
| |
| The final model achieved strong generalization performance on unseen validation images. |
| |
| --- |
| |
| ## Pretrained Weights |
| |
| Due to GitHub file size limits, the trained `.pth` model file is hosted externally. |
| |
| 👉 **Download pretrained model:** |
| https://huggingface.co/htetooyan/FoodClassifier/tree/main |
| |
| After downloading, place the file in: |
| |
| ```bash |
| checkpoints/best_model.pth |
