Train Keras models on German Traffic Sign Recognition Benchmark (GTSRB)

This repository contains a simple and light script to train several CNNs on the GTRSB dataset. Currently, you can select between an AlexNet, LeNet-5, VGG19 and the ResNet50, but note that the last two are pretrained on ImageNet.

Requirements

• numpy
• tensorflow
• matplotlib

Usage

1. First, download GTSRB training and test dataset from http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset#Downloads
2. Train a network using the following command:

python train_model.py 
-a="<model architecture>" 
-t="<path to train images>" 
-v="<path to validation images>" 
-l="path to validation labels" 
-i=<image size> 
-g="<train on grayscale images>"

Argparse parameters

Option	Required	Choices	Default	Option Summary
['-a', '--architecture']	True	True		Model architecture for training. ['alex', 'vgg19', 'resnet50', 'lenet-5']
['-t', '--train_path']	False	False	res/GTSRB/train/Final_Training/Images/"	Input directory for train set
['-v', '--validation_path']	False	False	res/GTSRB/test/Final_Test/Images/	Input directory for validation set
['-l', '--validation_labels']	False	False	res/GTSRB/test/Final_Test/GT-final_test.csv	Path to 'GT-final_test.csv' file
['-i', '--image_dim']	False	False	64	Image width and height in pixel
['-g', '--grayscale']	False	False	False	Train only on grayscale images
['-r', '--result_folder']	False	False	results/	Result folder for trained model and generated images and logs.

Usage example

For instance, it could look like this:

python train_model.py 
-a="alex" 
-t="res/GTSRB/train/Final_Training/Images/" 
-v="res/GTSRB/test/Final_Test/Images/" 
-l="res/GTSRB/test/Final_Test/GT-final_test.csv" 
-i=64 
-g="false"

This command will train the network by 10 epochs and produces a keras model.h5 file keeping the best. In addition, training process will be logged and the accuracy and loss is visualized.