Project: Build a Traffic Sign Recognition Program
Download the dataset to run the network from https://d17h27t6h515a5.cloudfront.net/topher/2017/February/5898cd6f_traffic-signs-data/traffic-signs-data.zip
and rename the extracted zip file to dataset in the cloned repo
Basic LeNet, traffic sign classifier is build: Gives validation accuracy around 95.4% and test accuracy 94% with learning rate = 0.001 for 100 epochs
My model consisted of the following layers based on LeNet architecture.
| Input | grayscale image | 32x32x1 | Convolution1 5x5x1x6 | 1x1x1 stride, VALID padding |outputs 28x28x6 | RELU1 | | Average pooling 2x2x1 | 2x2x1 stride, VALID padding |outputs 14x14x6 | Convolution2 5x5x6x16 | 1x1x1 stride, VALID padding |outputs 10x10x16 | RELU2 | | Average pooling 2x2x1 | 2x2x1 stride, VALID padding |outputs 5x5x16 | flatten 400 | | Fully connected 400x120 | |outputs 120 | RELU3 | | Dropout1 | 0.7 | Fully connected 120x84 | |outputs 84 | RELU4 | | Dropout2 | 0.7 | Fully connected 84x43 | |outputs 43 | Softmax |