In this project, we use convolutional neural networks to classify traffic signs. We will train a model so it can decode traffic signs from natural images by using the German Traffic Sign Dataset.
After the model is trained, we will then test model program on new images of traffic signs we find on the web.
RGB images are converted to grayscale for processing
Anaconda is used for managing my dependencies.
(Note: the hyperlinks only works if you are on the homepage of this GitHub reop, and if you are viewing it in “github.io” you can be redirected by clicking the View the Project on GitHub on the top)
Traffic_Sign_Classifier.ipynb is the main code for demos.
images folder contains the test images.
data folder user should download training and testing pickled data and put them into this folder.
(2) If you using Anaconda or miniconda, activate your environment which includes the dependencies by:
source activate your-conda-environment
(2) Load jupyter notebook and then select the
jupyter notebook Traffic_Sign_Classifier.ipynb