Hi, I’m trying to create a multiclassification model to predict a particular bird species using a real world dataset of the following images that were submitted by the public. It consists of roughly 33,000 images very similar to the following.
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The problem I’m having is that my model is incapable of getting an accuracy beyond ~58% on the training dataset, beyond which is starts to flatten out as shown here:

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I also applied a cyclical learning rate but to no avail as shown above. So, I used transfer learning particularly InceptionV3 and MobileNet and the validation accuracy still didn’t improve beyond my own model. So, I went back to look at the data. From your experience from looking at the above images, which range from a max resolution of 4347x3260 to 512x512, do you think I need to clean the dataset by removing some of the images and cropping out parts of the images?
Thanks