I am developing fully convolutional model for semantic segmentation task and I tried to use spatial dropout layers to prevent overfitting of my model.

My model has interesting learning curves and I am not sure if it is overfitting or not. According to loss function my model is overfitting because it has smaller train loss than validation loss. But according to graph of mean intersection over union (mIoU), which is metric I use to evaluate model, my model is not overfitting because train mIoU curve is uniformly under the validation mIoU curve thus train mIoU is smaller than validation mIoU. How can I now tell if my model is overfitting or not?

Here is graph. In left image is loss function and in right one are metrics (jaccard is mIoU):

I am developing fully convolutional model for semantic segmentation task and I tried to use spatial dropout layers to prevent overfitting of my model.

My model has interesting learning curves and I am not sure if it is overfitting or not. According to loss function my model is overfitting because it has smaller train loss than validation loss. But according to graph of mean intersection over union (mIoU) or other metrics my model is not overfitting because train metric curves (dashed) are uniformly under the validation metric curves (solid). How can I now tell if my model is overfitting or not?

Here is graph. In left image is loss function and in right one are metrics (jaccard is mIoU, dice is F1 score):

I am developing fully convolutional model for semantic segmentation task and I tried to use spatial dropout layers to prevent overfitting of my model.

My model has interesting learning curves and I am not sure if it is overfitting or not. According to loss function my model is overfitting because it has smaller train loss than validation loss. But according to graph of mean intersection over union (mIoU), which is metric I use to evaluate model, my model is not overfitting because train mIoU curve is uniformly under the validation mIoU curve thus train mIoU is smaller than validation mIoU. How can I now tell if my model is overfitting or not?

I am developing fully convolutional model for semantic segmentation task and I tried to use spatial dropout layers to prevent overfitting of my model.

My model has interesting learning curves and I am not sure if it is overfitting or not. According to loss function my model is overfitting because it has smaller train loss than validation loss. But according to graph of mean intersection over union (mIoU), which is metric I use to evaluate model, my model is not overfitting because train mIoU curve is uniformly under the validation mIoU curve thus train mIoU is smaller than validation mIoU. How can I now tell if my model is overfitting or not?

Here is graph. In left image is loss function and in right one are metrics (jaccard is mIoU):