I'm dealing with an image classification problem, with a multiclass imbalanced dataset (the bigger class has 4000 samples and the smaller has 110 samples) with 50 classes and 24000 samples.

I'm using a neural network for doing the task. I'm adopting a set o metrics for evaluating the model. Below I provide the metrics with the values achieved by my model:

* accuracy: 0.93
* macro-f1: 0.86
* weighted f1: 0.93
* macro precision: 0.87
* weight precision: 0.93
* macro recall: 0.85
* weighted recall: 0.93
* macro AUROC: 0.99
* weighted AUROC: 0.99

These values are averages obtained in a 5-fold cross-validation process. That is, the metrics are obtained by classifying each test fold. After that, the values of each metric are averaged.

However, I'm not confident about the values for AUROC (macro and weighted). I think that these values are "too good to be correct".

What do you think? Can you provide some guidelines for ensuring that this is correct for finding my error? Is AUROC suiable for evaluating the model in this context?