Food Item Recognition and Calorie Estimation Model

• Github URL: Project Link



• This machine learning project develops an advanced system for recognizing food items from images and estimating their calorie content. By leveraging cutting-edge image classification techniques and nutrition APIs, users can easily track their dietary intake and make informed food choices.



• Features
  • Intelligent Image Classification
    • Uses Convolutional Neural Network (CNN) to classify food items.
    • Trained on the comprehensive Food-101 dataset.
    • Supports recognition across 101 distinct food categories.
  • Accurate Calorie Estimation
    • Integrates with nutrition APIs like Nutritionix or Edamam.
    • Provides real-time calorie content for recognized food items.
    • Enables precise dietary tracking.
  • End-to-End Solution
    • Seamless workflow from image input to food classification and calorie estimation.
    • User-friendly interface through Jupyter Notebook.
• Dataset
  • Food-101 Dataset
  • Source: Kaggle Food-101 Dataset
  • Composition:
    • 101 food categories.
    • 1,000 images per category.
    • Total of 101,000 images.
  • Purpose: Training and validating the image classification model.
• Prerequisites
  • System Requirements:
    • Python 3.7+
    • pip package manager
    • Jupyter Notebook
    • (Optional) GPU for faster model training
  • Dependencies:
    • TensorFlow
    • Matplotlib
    • NumPy
    • OpenCV
    • Collections