/German_traffic_sign_classifier

Primary LanguageHTML

Project: Build a Traffic Sign Recognition Program

Overview

In this project, I have used what I've learned about deep neural networks and convolutional neural networks to classify traffic signs. I've trained and validated a model so it can classify traffic sign images using the German Traffic Sign Dataset. After the model is trained, I tried out my model on images of German traffic signs that I found on the web.

To meet specifications, the project consists of below three files:

  • the Ipython notebook with the code
  • the code exported as an html file
  • a writeup report either as a markdown or pdf file

All files are written to meet the rubric points

The Project

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
  • Visualize the convolutional neural network layer
  • Summarize the results with a written report

Python Packages Dependencies

  • Pickle : This is for loading the existing dataset pickled
  • Numpy : For converting RGB images to gray
  • Matplotlib : For visualization purpose
  • Sklearn : For shuffling the input data
  • Tensorflow : Neural network creation, training

The Implementation

Loading and Visualization of the data set

The German Traffic Sign Dataset is already pickled for us. It comes predivided into three sections.

  • train.p : Training dataset consisting of 34799 samples
  • valid.p : Validation dataset consisting of 4410 samples
  • test.p : Testing dataset consisting of 12630 samples
  1. I loaded these datasets in respective variables.
  2. I chose randomly which image and its respective label to visualize. This made me know how our image of traffic sign actually looks like
  3. All the images present in the train, test, validation set are 32x32x3 size.

Preprocessing of training set

  1. The images/samples under train.p are in RGB format. Thus, I changed that to grayscale with a very good method found on web. This method is explained here.
  2. After converting it to grayscale, I normalized it with below implementation.
a = .1
b = .9
min_data = 0 # Min value in grayscale is 0
max_data = 255 # Max value in grayscale is 255
norm = a + ((rgb - min_data)*(b - a)/(max_data - min_data))
  1. This preprocessing helped reduce the total size of dataset. This is because, if we use RGB color space, to save one image, we need to save data of 3 channels. But to save image in grayscale, we need to save data for only one channel. Thus size of our images data set reduces.
  2. Once all the training set images are converted into grayscale, we are ready to design a neural network for image classification.

Network definition

The network is defined in the model() function. The model consists of

  • 3 Conv2D layers
  • Every Conv2D layer has a maxpooling layer
  • 4 Fully Connected layers

The below graph will describe how the network looks.

alt text

This graph is generated using Tensorboard

Optimizers and other supporting functionalities

  • For optimization, we're using AdamOptimizer already built in tensorflow with the default hardcoded learning_rate = 0.001.
  • I have written a function evaluate() for calculation of our accuracy after every epoch.
  • This function returns the accuracy over the passed X_data and y_data to the evaluate() function.

Training

  • Once we are done with the preprocessing and setting up other functionalities, we're ready to run a session for training purpose
  • For session, we've set below two hyperparameters for
    • batch_size = 128
    • num_epochs = 50
  • For every epoch, we're finding the validation accuracy and storing it under validation_accuracy_list which will later help us plot the validation accuracy graph to see how well we are doing. See below print of epoch #31 for more clarification
Training: 31/50
EPOCH 31 ...
Validation Accuracy = 0.957
  • Once all the epochs are complete we are ready to plot the validation accuracy graph.
  • The validation accuracy increases with the number of iterations.
  • It increases rapidly in the beginning, but later stays almost constant.

Validation Accuracy vs Number of Epochs

Saving the trained model

  • Our model has run for predefined (50) number of epochs. It has lerned from the training set and it's time to save that learning in some checkpoint so that we can load that later for inferencing part.
  • Here while training, if the validation accuracy goes above 0.95, we're saving the model to trafficSignClassifier
  • For this, we've used tensorflow's Saver() method.

Testing model on test set

  • We had already loaded the test set from German Traffic Sign Dataset
  • Now, we will load the saved model and run the inferencing on it
  • For this purpose, we'll be using the evaluate() function to evaluate the accuracy on test set
  • For our model, it comes out to be, 0.939
INFO:tensorflow:Restoring parameters from ./trafficSignClassifier
Test accuracy = 0.939

Testing model on new test set

This is to check the accuracy on the images downloaded from internet. I've downloaded some images from internet. But these images are of different shapes. So, initially to feed these to our network, I'll have to modify them to a 32x32 format with grayscale color. These images are

Keep Right No Entry Priority Road Speed Limit 50km/h Stop

  • We predicted these images and the accuracy came out to be 100% since all 5/5 images were predicted correctly.

Predictions

Analyzing the model

  • In the further steps, we're analyzing the model.
  • Here, we have used the tensorflow's top_k() function to find the top 'k' number of probabilities for inference
  • As we're asked to find the top 5 probabilities, it's found and printed in the next cells along with the index prediction
Label: keep_right 
 Output: [[1. 0. 0. 0. 0.]] 
 Predicted Label Index: [[38.  0.  1.  2.  3.]]
Label: no_entry 
 Output: [[1. 0. 0. 0. 0.]] 
 Predicted Label Index: [[17.  0.  1.  2.  3.]]
Label: speed_limit_50 
 Output: [[1. 0. 0. 0. 0.]] 
 Predicted Label Index: [[2. 0. 1. 3. 4.]]
Label: priority_road 
 Output: [[1. 0. 0. 0. 0.]] 
 Predicted Label Index: [[12.  0.  1.  2.  3.]]
Label: stop 
 Output: [[1. 0. 0. 0. 0.]] 
 Predicted Label Index: [[14.  0.  1.  2.  3.]]

Visualizing the layers (Optional)

This is the optional topic to cover in this project.

  • For this visualizations, we're already given a function named outputFeatureMap() which takes several arguments including the activation stage and the input image.
  • We had to load the saved model in order to start inferencing for one particular layer.
  • From the session variable, we used the get_tensor_by_name() function which takes in the tensor name which we gave to particular layer while writing the network model and returns its object.
  • We sent the image and the tensor we got from previous step to the outputFeatureMap() function and it plotted the visualization of layer.
  • Here, we've visualized second conv2d layer from our model

Conv2D Visualization