Through using supervised learning, I was able to train a convolutional neural network to classify 18 different classes of military vehicles. I also used GradCAM, an explainable AI technique used to visualize where the CNN is looking at in an image. Below, I ran the GradCAM code on an image of myself! Pretty cool!


A bit more about this project

In the summer of 2019, I undertook a supervised learning research project for the Air Force to improve image classification of military vehicles in drones using t he machine learning library PyTorch. Some of the work is classified, including the dataset so the Github repo does not contain the full code base I worked with nor the datset. However, the code for GradCAM that I helped improve is all there. GradCAM is very useful explainable A.I. technique used to highlight the features of a iamge a neural network is looking at when performing image classification. In short, GradCAM essentially utilizes the gradients flowing into the last convolutional layer of a CNN for a certain class and performs a class activated mapping for that class to dispaly a “heatmap” of where the model is looking to make it’s classification decision. I ran the code on a picture of myself to show a little demonstration (keep in mind there hasn’t been any training done on classifying my face) and as you can it see it seems to be looking at the edges of my collar.

I used this code to load in trained models to see where the neural networks were looking at and it proves to be very helpful when trying to improve image classification. The results I obtained from the summer, I presented to a group of senior Air Force staff and fellow researchers. I’m quite proud of the code and findings I produced and this project cemented my love for machine learning.