/CarND-Traffic-Sign-Classifier-Project

Classify Traffic Signs.

Primary LanguageHTMLMIT LicenseMIT

Traffic Sign Recognition

Project Writeup

Build a Traffic Sign Recognition Project

The goals / 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

Rubric Points

Here I will consider the rubric points individually and describe how I addressed each point in my implementation.

Writeup / README

1. Provide a Writeup / README that includes all the rubric points and how you addressed each one. You can submit your writeup as markdown or pdf. You can use this template as a guide for writing the report. The submission includes the project code.

You're reading it! and here is a link to my project code

Data Set Summary & Exploration

1. Provide a basic summary of the data set. In the code, the analysis should be done using python, numpy and/or pandas methods rather than hardcoding results manually.

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

  • 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. Include an exploratory visualization of the dataset.

Here is an exploratory visualization of the data set. It is a bar chart showing how the training dataset is distributed acrosses the different classes of traffic signs

Visualization

Design and Test a Model Architecture

1. Data Preproceessing techniques

As a last step, I normalized the image data because ...

I chose to generate additional augmented data because tests showed that the model performed significantly better with more dataset

To add more data to the the data set, I used basically generated additional 2 rotated versions of the original images, by rotating the images st +5 and -5 degrees.

2. Model Architecture

My final model consisted of the following layers:

Layer Description
Input 32x32x3 RGB image
Convolution 5x5 1x1 stride, same padding, outputs 28x28x6
Activation RELU
Max pooling Reduces the size to 14x14x6
Convolution 5x5 Input 14x14x6, outputs 10x10x6
Activation RELU
Max pooling Reduces the size to 5x5x16
Flatten Output is 400
Fully Connected None-Linear - output - 120
Activation RELU
Dropout
Fully Connected None-Linear - output - 84
Activation RELU
Dropout
Fully Connected + RELU None-Linear - output - 43 (Class Size)

3. Training Approach

To train the model, I used a model based of the LeNet architecture. I used the AdamOptimizer, using a Batch size of 256 and ran 20 Epochs. I found that using a learning rate of 0,001 provided best results.

Training Discussion

My final model results:

  • Validation set accuracy of 93.4%
  • Training set accuracy of 98.9%
  • Test set accuracy of 92.3%

I iteratively adjusted the parameters and approaches to achieve the results described here. I figured that the model performed much better with more data set, I augmented the input images by generating slightly rotated version of the images (Rotating by about +5 and -5 degrees of each image)

I struggled with over-fitting, where the model performed well in test data, but did very badly with the validation dataset. Adding a dropout of 0.7 improved the accuracy of both the validation and training set. I tried running with 5, 10, 15, 20, 25 epochs, and settled for 20 epochs.

I found the learning rate of 0.001 to be just fine.

Test a Model on New Images From the web

1. Choice of new Images

Here are five German traffic signs that I found on the web: This images were randomly selected across a couple of sources

alt text alt text alt text alt text alt text

I find that the model should not find it difficult to classify these images as they are sufficiently bright and properly aligned; I cropped the images to remove most of the irrelevant background.

2. Predictions

Here are the results of the prediction:

Image Prediction
30 km/h Zone End of all speed and passing limits
60 km/h Zone 60 km/h Zone
No Entry No Entry
Turn right ahead Turn right ahead
Keep Right Keep right

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% from the training

3. Softmax Probabilities

The model is provides a very high degree of probabilities when predicting new images.

1:
 1: 100.00%
 32: 0.00%
 2: 0.00%
 6: 0.00%
 13: 0.00%
3:
 3: 99.75%
 9: 0.14%
 32: 0.11%
 1: 0.00%
 0: 0.00%
17:
 17: 100.00%
 0: 0.00%
 22: 0.00%
 14: 0.00%
 26: 0.00%
33:
 33: 98.79%
 35: 1.21%
 40: 0.00%
 39: 0.00%
 9: 0.00%
38:
 38: 100.00%
 40: 0.00%
 13: 0.00%
 36: 0.00%
 39: 0.00%