Extremely stochastic validation loss/accuracy
There can be a few reasons for this behavior as already pointed out in &quot;Why is the validation accuracy fluctuating?&quot;:

Size of train / validation stes: Fluctuations may become stronger the smaller your validation set is, especially during the early stage of training where predictions are closer to random predictions.

Overfitting: Train loss seems to be low and accuracy over 90%. If you're overfitting, the predictions on the validation set become almost random and you observe such fluctuations.

Random validation set: If you're validation set is not constant across all epochs, the validation loss will be different and losses from different epochs cannot be compared anymore. Especially if you have imbalanced classes.

Weighted loss function: Using a weighted loss function for imbalanced data sets makes your loss appear more stochastically, especially in the early stage of training and if the distributions of the training and validation sets mismatch.


Maybe you can share additional information to your data, preprocessing, training, model etc. so it is easier to spot the cause of this more precisely.