Multiclass classification on imbalanced dataset : Accuracy or micro F1 or macro F1
There are two not so widely known in the data science community metrics that work well for imbalanced data and can be used for multi-class data: Cohen's kappa and Matthews Correlation Coefficient (MCC).

Cohen's kappa is a statistic that was designed to measure inter-annotator agreement, but it can be used to measure agreement between the ground truth and a prediction. There are number of explanations online e.g. on Wikipedia or here and it is implemented in scikit-learn.

MMC was initially designed for a binary classification but then generalized for multi-class data. There are also multiple online sources for MCC, e.g.  Wikipedia and here, and it is implemented in scikit-learn. 

Hope this helps.