These past 10 hours of coding have been absolute hell. I have tried to make images with the same label closer in the embedding space, but then everything else broke. After about a week, I just gave up on it entirely and accepted that it was only going to aim for differentiating images, not caring about the image labels. After making this change(a very saddening one, pouring all that time down the drain), I was able to spike model performance greatly.
I have practically come full circle, with my model hardly different from what it was at my last devlog, but I have learnt some valuable stuff. I have improved the loss function, giving a much cleaner way to compare embedding vectors and calculate their (dis)similarity. Additionally, I have created a few pieces of code that allow me to visualise various aspects of my model. All in all, I wouldn’t consider this time to be a total waste, as I have learned some valuable stuff. If you want to take a look at the various parts I have added, check out the images attached below and read my explanation of what they do:

1. The first image shows all the kernels in the first convolutional layer of my network.
2. The second image shows a tree with all the operations applied to the images, converting them from an image to an embedding vector.
3. The third image shows the process of my model being trained