Machine Learning
Course Project
Traffic Sign Classification
In this project, you are given a dataset as following:
Images of Traffic Signs.
Number of Instances (records in your data set): 15540 Number of Classes (types of Traffic Signs): 15
Relevant Information:
This dataset contains 15540 images for 15 traffic signs, the image for each data is around 32X32 pixels, but not fixed.One image is illustrated below for your reference.
Basically, the images are saved in ppm files in different folders. Each folder represents one class of traffic signs. In each folder, there is a csv file that includes the filenames in the folder, the width and height of each image, the Region of interest ( x1,y1,x2,y2 illustrated in the image below) of each image, and the class ID or the output label.
For the access of ppm file and csv file, the short demo code in attached in UMMoodle provides some simple solutions.
Project requirement:
In this project, the student should design 3 different classifiers (KNN is not permitted) to classify the data into 15 classes:
Implement a python program (trainer) which can be used to import the historical data from ppm files, preprocess data and train 3 classifiers. Then save the three classifiers.
Provide some preprocessing on the loaded data to generate some good inputs for the classifier
Implement another python program (tester) which can be used to import the testing data from a folder, and test the trained classifiers over the ppm images in the folder
Please use cross validation to choose the best classifier.
Input:Your programs should be able to accept (read) historical data files (ppm files and csv files) for trainer and testing data files (ppm files) for tester
Output:Your trainer program should output some performance indexes and the trained classifiers. You tester program should output classification result for testing data.
What to Submit at UMMoodle before the deadline:
1.Well-commented python source code for training and testing.
2.Saved three classifiers, and trainer program and the tester program that can used them to test data in a folder.
Reviews
There are no reviews yet.