This project will execute the training and prediction of traffic signs, based on the Kaggle competition Here.

It uses both a convolutional neural network (Lasagne) and Logistic Regression (scikit-learn) in combination with feature extraction through scikit-image. The models were trained on an NVidia GTX 960 with 2GB of memory.

The executable files are listed below with their respective functionality:

• haar_importances.py:
  • This file will calculate the importances of the different haar configurations.
  • All importances will be written to the file "haarImportance.txt" in the current directory.
  • The importances are sorted according their importance in descending order.
  • This means that the most important configuration will be on the first line of the file.
  • This file can later be used by the haar_feature.
• main.py:
  • This file will train the model on all the train given and predict the results of the test images given.
  • These results are all written to a file named 'result.csv' in the current directory.
  • The given train and test images are specified in the python file. Also the used model and features are given in the code file.
• meta_parameter_estimators.py
  • This file will test metaparameters of features based on the error_rate.
  • Which parameters, features are trained with which trainer is specified in the file itself.