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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
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Dataset from kaggle
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Built while learning from coursera
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Skills -> Computer Vision | Image Classification | Python | Tensorflow | Keras | CNN | Transfer Learning | PreTrained Models
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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
- 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