Lego Minifigure Gender Classification Using Deep Learning. With CNN’s and transfer learning
Through my journey of working on the convolutional neural network (CNN) section of Udacity’s deep learning nanodegree, I decided to work on my own project to see if CNN’s would be able to classify between the genders of Lego minifigures.
The reason I decided to do this is because I’m a Lego fan and have been collecting minifigures for many years now. I think I now have over 200 of the little guys, mostly obtained from blind bags.
Oh and I also take photos of them which I share on Instagram!
Transfer learning is when you use a pre-trained neural network, and use it for a different dataset.
Since I have a small dataset, I wanted to utilize ImageNet’s pre-trained images as it has many pictures of people and clothing, so it should be easier to determine the features of the minifigures. With the similarities in human features and clothing of the minifigures, I would categorize my dataset to be similar to what is present in ImageNet.
According to Udacity, if the new dataset is small and similar to the original training data, you have to change the neural network as follows:
Google Reveals "What is being Transferred” in Transfer Learning. Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community.
Project walk-through on Convolution neural networks using transfer learning. From 2 years of my master’s degree, I found that the best way to learn concepts is by doing the projects.
Experimental evaluation of how the size of the training dataset affects the performance of a classifier trained through Transfer Learning.
A step-by-step guide to classifying dog images amongst 115 breeds! Stuck behind the paywall? Click here to read the full story with my friend link!
Deep learning on graphs: successes, challenges, and next steps. TL;DR This is the first in a series of posts where I will discuss the evolution and future trends in the field of deep learning on graphs.