I am relatively new to the deep learning landscape, so please don’t be as mean as Reddit! It seems like a general question so I won’t be giving my code here as it doesn’t seem necessary (if it is, here’s the link to colab)
A bit about the data: You can find the original data here. It is a downsized version of the original dataset of 82 GB.
Once I trained my CNN on this, it predicts ‘No Diabetic Retinopathy’ (No DR) every single time, leading to an accuracy of 73%. Is the reason for this is just the vast amount of No DR images or something else? I have no idea! The 5 classes I have for prediction are ["Mild", "Moderate", "No DR", "Proliferative DR", "Severe"].
It’s probably just bad code, was hoping you guys could help
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Answer
I was about to comment:
A more rigorous approach would be to start measuring your dataset balance: how many images of each class do you have? This will likely give an answer to your question.
But couldn’t help myself look at the link you gave. Kaggle already gives you an overview of the dataset:
Quick calculation: 25,812 / 35,126 * 100 = 73%. That’s interesting, you said you had an accuracy of 74%. Your model is learning on an inbalanced dataset, with the first class being over represented, 25k/35k is enormous. My hypothesis is that your model keeps predicting the first class which means that on average you’ll end up with an accuracy of 74%.
What you should do is balance your dataset. For example by only allowing 35,126 - 25,810 = 9,316 examples from the first class to appear during an epoch. Even better, balance your dataset over all classes such that each class will only appear n times each, per epoch.