Food-Vision-Big deep learning food 101 image recognition and classification

Welcome to Food Vision Big™, a cutting-edge project dedicated to advancing the field of food image recognition. Building upon the success of Food Vision mini, this project harnesses the full potential of the colossal Food101 dataset to create a state-of-the-art model. Key Objectives: Data Domination: Master the entire Food101 dataset, with 75,750 training images and 25,250 test images. Model Mastery: Leveraging EfficientNet and ResNet, we meticulously trained our models to push the limits of food image recognition, achieving a remarkable 74% accuracy. Speed Supremacy: With mixed precision training and data prefetching, we've streamlined the training process, making it faster and more efficient. Our Journey: Data Exploration and Preprocessing: Utilizing TensorFlow Datasets (TFDS), we explored, analyzed, and meticulously preprocessed the Food101 dataset. Model Training: EfficientNet and ResNet were at the core of our model, fine-tuned and optimized to tackle the challenges of food image recognition. Performance Evaluation: We conducted extensive evaluations using various metrics, including Precision, Recall, F1-Score, and confusion matrices, to validate our model's performance.