htetooyan/FoodClassifier

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.

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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.

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🧠 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.

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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/

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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

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Model Performance

Metric	Result
Top-1 Accuracy	96% (validation)
Loss	Low and stable

The final model achieved strong generalization performance on unseen validation images.

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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:

checkpoints/best_model.pth