Traffic-Sign-Classification
Objective
Design, train and test a neural network model for German Traffic Signs classification
Data set Summary
Size of Training Set: 34799 samples Size of Validation Set: 4410 samples Size of Test Set: 12630 samples Shape of a sample: (32,32,3) Number of unique classes: 43
Pre-processing the data
Step 1: Added additional training samples by adding noise to the existing images.
Step 2: Converted all the training samples to grayscale because the result is not dependent on the color channels.
Step 3: Normalized the data with the formula (image_data-128)/128. This is done so as to avoid impact of images with big weights.
Network architecture
The Layers in the network are as below:
Layer 1:
Type: Convolutional.
Input = 32x32x1. Output = 28x28x6.
Pooling: Input = 28x28x6. Output = 14x14x6.
Layer 2:
Type: Convolutional
Input =14x14x6 Output = 10x10x16
Pooling : Input = 10x10x16. Output = 5x5x16
Layer 3:
Type: Convolutional
Input = 5x5x16. Output = 1x1x32
Layer 4:
Type:Fully connected
Input = 32. Output = 120
Layer 5:
Type:Fully connected
Input = 120. Output = 84
Layer 6:
Type:Fully connected
Input = 84. Output = 32
Layer 7:
Type:Fully connected
Input = 32. Output = 43