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The answer to such wide questions always have the same answer - it depends. Since I don't know the exact ratio of the four classes, I'll mention a few important points that can help you decide how to move forward.
Different people set different thresholds for when their dataset is imbalanced, I'm refraining from giving you an exact percentage but the general ...
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In PyTorch a typical gotcha that leads to this behavior is forgetting to set the model in evaluation mode when doing inference. You can do this by invoking .eval() on the model.
Evaluation mode changes the behavior of some stochastic elements that can lead to not deterministic results, like batch normalization and dropout.
Apart from that, unless you have ...
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