I am doing a binary image classification task using Transfer learning(Inception v3). But there is caveat in the dataset, the classes are imbalanced in the favour of negative samples in both train and test dataset. So there are 3 questions:
- If I use the image augmentation during training(rotation, horizontal flip), will it not increase the number of samples of both classes during different epochs in training. If it does, the ratio between the 2 classes still remains same?
2).Does image augmentation alone can solve the problem for imbalanced dataset?
3). when using colab’s gpu, and training heavy models like this one, how one can get benefit of hyperparameter tuning using mlflow to see the effect of different batch sizes , epochs etc…