Traffic Sign Classification using Convolutional Neural Networks

Applied Convolutional Networks (ConvNets) to traffic sign classification on the GTSRB dataset consisting of 43 different classes of traffic signs. Converting given color images to grayscale does not negatively affect the accuracy and removes the redundant computational burden from the model. The Best accuracy yielded after rounds of experiments was 96.28% on the test dataset.