Description
This assignment involves implementing and training four deep-learning models on the Food101 dataset for image classification. The models include a basic CNN, an all-convolutional net, a regularized model, and a transfer learning model. The goal is to compare the performance of these models, analyze their architectures and parameters, and evaluate their accuracy on the test set. The assignment also emphasizes the use of PyTorch Lightning and early stopping techniques and the importance of regularization through data augmentation or dropout. A more detailed instruction will be provided once the offer is accepted.
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