Deep Learning Model Training and Evaluation: A Comprehensive Guide

This guide details the process of training and evaluating deep learning models. The preprocessed data is randomly divided into three sets: a training set (60%), a validation set (20%), and a test set (20%). We utilize the PyTorch deep learning framework for our model development. The Adam optimizer is employed with a weight decay of 0.0001. Model training is performed on 8 GPUs with a batch size of 16. The training process consists of 100 epochs, starting with an initial learning rate of 0.0001, which is adjusted over a total of 500 epochs. The input image size for the model is set to 512x512 pixels.