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+Model that acheived 5th place overall on FathomNet 2025 kaggle competition (competition link: https://www.kaggle.com/competitions/fathomnet-2025/overview)
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+Associated code: https://github.com/robertahunt/FathomNet2025
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+Things I think worked well:
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+1. Choosing the class which minimized the expected loss based on the distance matrix (and not the class with the highest probability) and implementing this as a matrix multiplication with the distance matrix.
+Implementing a small Graph Neural Network layer - the idea here was to help in cases where there are many specimens of the same species in a single overall image, and one is easy to classify, but the other instances may be blurry. Then adding a graph layer could help guide the model to the correct classification.
+2. Using EfficientNet as a simple and fast base network, made experimenting fairly fast and simple.
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+Things I wish I had done differently:
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+1. Setting the seed earlier: I initially used a random seed each time, which made measuring progress and reproducing results difficult. It wasn't until near the end I changed this. This is why the results using this seed also sadly do not match perfectly with the public results.
+2. Making the saving and logging process cleaner overall so it would be easier to compare results