Application of the German Traffic Sign Recognition Benchmark on the VGG16 network using transfer learning and bottleneck features in Keras

Convolutional Neural Networks (CNNs) are successful tools in image classification. CNNs are inspired by the animal visual cortex using a similar connectivity pattern as between neurons. The purpose of this thesis is to create a classifier, using transfer learning, that manages to classify images of traffic signs from the German Traffic Sign Recognition Benchmark (GTSRB) with good accuracy and to improve the performance further by tuning the hyperparameters. The pre-trained CNN used is the VGG16 network from the paper "Very deep convolutional networks for large-scale image recognition". The result showed that the VGG16 network got an accuracy of 74.5\% for the hyperparameter set where the learning rate was 1e-6, the batch size was 15 and the dropout rate 0.3. The conclusion was that transfer learning using the bottleneck features is a good tool for building a classifier with only a small amount of training data available and that the results probably could be further improved using more real data or data augmentation both for training and testing and by tuning more of the hyperparameters in the network