• 1. Overview
• 2. Data Set Summary & Exploration
  • Provide a basic summary of the data set
  • Include an exploratory visualization of the dataset
• 3. Design and Test a Model Architecture
• 4. Test a Model on New Images

1. Overview

In this project, I have implemented a classifier using deep neural networks, convolutional neural networks and transfer learning to classify traffic signs. I have trained a model so that it can decode traffic signs from natural images by using the German Traffic Sign Dataset. After the model is trained, I have tested my model program on new images of traffic signs collected from the web.

The steps of this project are the following:

• Load the data set (see below for links to the project 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

2. Data Set Summary & Exploration

This is a pickled dataset in which we’ve already resized the images to 32x32.

Provide a basic summary of the data set

I used the numpy library to calculate summary statistics of the traffic signs data set:

• The size of training set is 34799
• The size of validation set is 4410
• The size of test set is 12630
• The shape of a traffic sign image is (32, 32, 3)
• The number of unique classes/labels in the data set is 43

Include an exploratory visualization of the dataset

Here is an exploratory visualization of the data set. It pulls in a random set of 5 images.

I also detail the dataset structure by plotting the occurrence of each image class to get an idea of how the data is distributed.

3. Design and Test a Model Architecture

My final model consisted of the following layers:

To train the model, I used an LeNet for the most part that was given, but I added a dropout function for deleteing the fully connected layer’s output. I used the AdamOptimizer with a learning rate of 0.001. The epochs used was 30 while the batch size was 64. Other important parameters I learned were important was the number and distribution of additional data generated.

My final model results were:

• Train Accuracy = 0.999
• Valid Accuracy = 0.968
• Test Accuracy = 0.946

4. Test a Model on New Images

Here are five German traffic signs that I found on the web:

The model was able to correctly guess 8 of the 8 traffic signs, which gives an accuracy of 100%.

For the first image, the model is relatively sure that this is a Bumpy road (probability of 0.996), and the image does contain a bumpy road. The top 3 soft max probabilities were: