History: For my masters research project at University of Toronto, I was given airport Xray baggage scan images containing gun and knife to develop a model that performs an automatic detection of gun and knife in the baggage. Given only a small amount of Xray images, I am using Domain Adaptation by using a large number of normal non-Xray images of gun and knife from the web to train a model and adapting the model to perform well on the Xray images. In this post, I will address a strange behaviour of ResNet50 and show how the t-SNE visualization technique helped clear up the fog. In Chapter 3.2, I addressed a concerning and unintuitive behaviour of ResNet50 that was fine-tuned for gun vs. knife binary classification task using only web images (no Xray images). It classified images unrelated to gun or knife (e.g.

I was given Xray baggage scan images by an airport to develop a model that performs automatic detection of dangerous objects (gun and knife). Given only a small amount of Xray images, I am using Domain Adaptation by first collecting a large number of normal (non-Xray) images of dangerous objects from the internet, training a model using only those normal images, then adapting the model to perform well on Xray images. In my previous post, I talked about iterative data collection process for web images of gun and knife to be used for domain adaptation. In this post, I will discuss transfer learning with ResNet50 using the scraped web images. For now, we won't worry about the Xray images and only focus on training the model with the web images. To read this post, it's recommended to have some knowledge about how to apply transfer learning using a model pre-trained on ImageNet in PyTorch. I won't explain every step in detail, but will share some useful tips that can answer questions like: