Update README.md

README.md

@@ -9,8 +9,8 @@ Associated code: https://github.com/robertahunt/FathomNet2025
 Things I think worked well:
 
 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.
+2. 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.
+3. Using EfficientNet as a simple and fast base network, made experimenting fairly fast and simple.
 
 Things I wish I had done differently: