FoodSnap AI

[Adeelp1]

🍱 Food Classification & Nutrition Prediction

A deep learning model that classifies food from images using EfficientNetB0 and returns nutritional facts using a SQLite3 database.

🚀 Features

• Image classification with EfficientNetB0 pretrained on ImageNet

• Transfer learning with fine-tuning for improved accuracy

• Nutrition info retrieval from a SQLite3 database

• Exported model in both .keras and .tflite formats

🖼️ Dataset

• 12 custom food categories

• 1200+ images

🧠 Model Architecture

• Base model: EfficientNetB0 (ImageNet weights)

• Layers: Base model, GlobalAveragePooling, Dense, Softmax

• Loss: sparse_categorical_crossentropy

• Optimizer: Adam

🧪 Results

• Training Accuracy: ~97%

• Validation Accuracy: ~98%

💾 Model Export

• .tflite

🛠️ Tools & Frameworks

• Python | TensorFlow | Keras | EfficientNet | SQLite3 | Google Colab | NumPy

📦 Nutrition Database (Example)

Food	Calories	Protein	Fat
Apple	52	0.3g	0.2g
Samosa	262	4g	17g
Banana	96	1.3g	0.3g

Set Up and Run Locally

Clone repository

git clone https://github.com/Adeelp1/foodsnap-ai.git
cd foodsnap-ai

Checkout branch

git checkout feature/adeelp1

Install dependencies:

pip install -r requirements.txt

Make sure you have all dependencies installed, then run the program using:

python main.py path/to/your/image.jpg