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a COMPUTER VISION problem solved using convolutional neural networks, and transfer learning to classify and analyze food across 11 diverse categories.

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TechWithRamaa/FoodSenseAI

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FoodSenseAI

  • The project is built upon the principles of deep learning and incorporates advanced techniques to classify a dataset containing 16,643 food images grouped into 11 categories

  • Dataset from kaggle

  • Built while learning from coursera

  • Skills -> Computer Vision | Image Classification | Python | Tensorflow | Keras | CNN | Transfer Learning | PreTrained Models

  • Implementation -> food_sense_ai.ipynb

Data Handling and Preparation:

  • Utilizes Pandas and NumPy for efficient data manipulation
  • Real-time data augmentation is achieved using ImageDataGenerator to improve model generalization

Model Architecture

  • Employs Convolutional Neural Networks (CNNs) for feature extraction from food images
  • Integrates pre-trained model InceptionResNetV2 through transfer learning to leverage learned features, enhancing classification accuracy

Optimization and Training:

  • Uses Stochastic Gradient Descent (SGD) and other advanced optimizers for effective learning
  • Implements callbacks to manage learning rates, prevent overfitting, and save the best model versions

Visualization and Analysis:

  • Employs Matplotlib and Seaborn for visualizing data distributions, model performance, and training metrics

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a COMPUTER VISION problem solved using convolutional neural networks, and transfer learning to classify and analyze food across 11 diverse categories.

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