fangchun007 / TrafficSignRecognitionClassifier

Self-Driving Car Engineer Nanodegree/Deep Learning Project/Build a Traffic Sign Recognition Classifier

Project: Build a Traffic Sign Recognition Program

Overview

In this project, you will use what you've learned about deep neural networks and convolutional neural networks to classify traffic signs. You 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, you will then test your model program on new images of traffic signs you find on the web, or, if you're feeling adventurous pictures of traffic signs you find locally!

Dependencies

This lab requires:

• CarND Term1 Starter Kit

The lab enviroment can be created with CarND Term1 Starter Kit. Click here for the details.

• As I checked, there is no opencv installed in the anaconda environment in CarND Term1 Starter Kit. Therefore

source activate carnd-term1
conda install opencv

Dataset

1. Download the dataset. This is a pickled dataset in which we've already resized the images to 32x32.
2. Clone the project and start the notebook.

git clone https://github.com/fangchun007/TrafficSignRecognitionClassifier
cd TrafficSignRecognitionClassifier
jupyter notebook Traffic_Sign_Classifier.ipynb

3. Follow the instructions in the Traffic_Sign_Classifier.ipynb notebook.