sanketgujar / carnd-traffic-sign-classifier-project

German Traffic Sign Classifier

Traffic Sign Recognition Traffic Sign Recognition Project

Here is a link to my project code

Data Set Summary & Exploration I used numpy, matplotlib and pandas to explore the dataset.

• The size of training set is 34799
• The size of the 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

2. Here is an exploratory visualization of the data set. The training images in the dataset can be seen as below

The label distribution in training images can be visualized by the bargraph below

Preprocessing As a first step, I decided to convert the images to grayscale to reuduc the amount of data, the grayscale image will train the netwrok faster than color images. Grayscale images will not affect the performance as most of the traffic signs have red color. Here is an example of a traffic sign image before and after grayscaling.

![alt text][image2]

As a last step, I normalized the image data to reduce skewness of images.

![alt text][image2]

2.Model Description

My final model consisted of the following layers:

Layer	Description	Input	Output
Input preprocessing	32x32x3 RGB image	32x32x3	32x32x1
Convolution 5x5	1x1 stride, valid padding, RELU	32x32x1	28x28x48
Max pooling	2x2 stride, 2x2 kernel	28x28x48	14x14x48
Convolution 5x5	1x1 stride, valid padding, RELU	14x14x48	10x10x96
Max pooling	2x2 stride, 2x2 kernel	10x10x96	5x5x96
Convolution 3x3	1x1 stride, valid padding, RELU	5x5x96	3x3x172
Max pooling	1x1 stride, 2x2 kernel	3x3x172	2x2x172
Flatten	flatten the matrix to 1D	2x2x172	688
FC	Fully connected layer	688	84
FC	output = traffic sign labels in data	84	43

Parameters To train the model, I used an my local machine CPU. Training Parameters: Batch size : 128 Epoch : 15 Optimizer : Adamoptimizer Learning_rate: 0.001 Sigma_layers: 0.1

Results My final model results were:

• training set accuracy of 95.5
• validation set accuracy of 93.1
• test set accuracy of 92.8

Testing Model on New Images Here are five German traffic signs that I found on the web:

Here are the results of the prediction:

Image	Prediction
Keep Rigth	Keep Right
Stop	Stop
30 km/h	30 km/h
Priority road	Priority road
cycle road	General caution

The model was able to correctly guess 4 of the 5 traffic signs, which gives an accuracy of 80%. This compares favorably to the accuracy on the test set of 92.6.

For the first image, the model is relatively sure that this is a keep rigth (probability of 1.0), and the image does contain a stop sign. The top five soft max probabilities were

Probability	Prediction
1.00	Keep Right
1.00	Stop
0.58	30Km/h
0.98	Priority Road
0.83	General Caution