This project was used to be a proof of concept and also to answer the research question that i had selected for my honours project. This also allowedme to further my knowledge in the area of machine learning also.
“What deep neural network model design is most effective for colorizing a greyscale image back into color automatically ”
Due to the nature of the project being focused around imagery it was decided that a convolutional neural network also known as CNN was to be used as it offered more features and support in this type of project.
When it came to the accuracy of the model that i had generated it suprised me with the accuracy that it had after just a short training period and with the limited dataset that i had access to.
Training Time: 26 Hours - Due to CPU limitation
Dataset Size: 12,000 Images - 6,000 for training and 6,000 for testing
For the first test i used an image of a White male with this test i aimed to test if the network could correctly colorize the correct skin tone. In this test i received a 74.15% Accuracy this was lower than expected but this was also due to the background not being colorized as the network was only trained on faces.
For the second test i used an image of an african american male with this test i aimed to test if the network could correctly colorize the correct skin tone. In this test i received a 90.19% Accuracy this was higher than expected but this was due to the person taking up more of the image than the background resulting in a higher accuracy.
For the third test i used an image of a White male and a african american male with this test i aimed to test if the network could correctly colorize the correct skin tone of both people . In this test i received a 83.83% Accuracy this was around what i was expecting as what i learned form the previous two tests that the background would lower the accuracy of the overall image.
This project was able to correctly identify and colorize image of people and apply the correct skin tone and color to the image