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This is project 3 for CSCI 8360 course at UGA. In this project we were challenged to predict the class labels of the test set from CIFAR-10 image dataset with 10 different labeled categories. The dataset consist of 60,000 PNG images with 50,000 in the training set and 10,000 in the test set. We conducted a number of manipulations on the data and applied the Deep Residual Networks with 119 layers by Kaiming He to train the classification model. In the end, the model achieves 92.82% prediction accuracy in the test set.