Convolutional Neural Network (CNN) architectures can be pretty general purpose for vision tasks. In this article, I’ll relay my experience in using the same network architecture for 18 different classification tasks.

The classification tasks include facial features such as length of the chin (3 gradations), type of hair (111 types), and hair color (10 hair-colors) etc.

I will be using the CartoonSet 100k Image dataset from Google available here. My code for these experiments is available here.

For these experiments, I used the 10K version of the dataset. On initial exploration, the dataset consists of 10 folders. First order of business is to download the dataset from the website and extract it. You will see these 10 folders:

['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

Inside each sub-folder there are ‘.png’ image files of the cartoon and a .csv descriptor file.

['cs11502169095236683120.csv',
 'cs11502169095236683120.png',
 'cs11502298889929094331.csv',
 'cs11502298889929094331.png',
 'cs11502404786906647764.csv',
 'cs11502404786906647764.png',
 'cs11502407216397343631.csv',
 'cs11502407216397343631.png',
 'cs11502919926067511421.csv',
 'cs11502919926067511421.png']

Let’s do a quick visualization: (checkout my code in github)

#data-science #deep-learning #computer-vision #machine-learning #convolutional-network