A comparison of training algorithms when training a Convolutional Neural Network for classifying road signs
Rasmus Bergendal, Andreas Rohlén · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019
This thesis is a comparison between three dierent training algorithms when training a Convolutional Neural Network for classifying road signs. The algorithms that were compared were Gradient Descent, Adadelta, and Adam. For this study the German Trac Sign Recognition Benchmark (GTSRB) was used, which is a scientically relevant dataset containing around 50000 annotated images. A combination of supervised and offline learning was used and the top accuracy of each algorithm was registered. Adam achieved the highest accuracy, followed by Adadelta and then GradientDescent. Improvements to the neural network were implemented in form of more convolutional layers and more feature recognizing filters. This improved the accuracy of the CNN trained with Adam by 0.76 percentagepoints