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---
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 |