Traffic Sign Recognition
About this repository
This repository contains a Ipython notebook project that shows how a traffic sign recognition works. The algorithm works based on the Computer Vision algorithms. It runs offline and not in real-time. I use the deep neural networks and convolutional neural networks to classify traffic signs.
The training and validation datasets to classify traffic sign images comes from the German Traffic Sign Dataset
The goals / steps of this project are the following:
- Load the data set
- Explore, summarize and visualize the data set
- Design, train and test a model architecture
- Use the model to make predictions on new images
- Analyze the softmax probabilities of the new images
- Summarize the results with a written report
Download the data set. The classroom has a link to the data set in the "Project Instructions" content. This is a pickled dataset in which we've already resized the images to 32x32. It contains a training, validation and test set.
Install the Udacity starting kit from CarND Term1 Starter Kit.
Clone this repository:
git clone https://github.com/ywiyogo/CarND1-P5-VehicleDetection.git
Go to the repository folder
and activate the virtualenvironment:
source activate carnd-term1
Start the program
jupyter notebook Traffic_Sign_Classifier.ipynb