Dont we need to have predictions from the model output in order to calculate an accuracy ?? what i was trying to say  earlier (and couldnt make it clear) was that for pytorch’s Mask RCNN implementation … we need to have model in eval model in order to generate predictions whcih can be then subsequently used for accuracy calculations … the same cannot be done in model train mode …  as said in its documentation here 9
   The model returns a Dict[Tensor] during training, containing the classification and regression
   losses for both the RPN and the R-CNN, and the mask loss.

and this is realized via  eager_outputs  methods in generalizedRCNN class as seen here 2.
To my understanding , in order to get predictions (and subsequent acc) in train mode , the only options seems to be is modification of this eager_outputs method. i have tried it but in vain (as of now).
To answer your  question , i am looking for mIoU … which i am already calculting via predictions in eval mode. but since now i want to analyse model overfitting , i need to have a train_mIoU generated from the training dataset. ofcourse i can just change the dataset to training dataset in eval mode to generate train_mIoU but that adds another overhead in computations … i was looking if there is any option to get the same during model training and NOT during evaluations …
P.S thinking out of the box , any other option to analyse model overfitting ? i feel reluctant to go to train/test loss monitoring for this purpose … but open to suggestions .