rcshubhadeep:

It looks okay to me, At least at the highest smoothing, it seems that the loss is decreasing well.


The loss value itself is quite “high” and depending which criterion you are using your model might not be learning enough. Based on the loss curve it also seems that there is initially some decrease in the loss, which hits a plateau at iteration or epoch 40.



 rcshubhadeep:

However, when I evaluate the model, with the eval dataset, each evaluation run on the same set of images spit out different prediction. I almost feel like the model is close to random prediction. I am not sure what I am getting wrong here.


Are you using model.eval() during the validation? This would make sure to disable dropout layers and to use the running stats in batchnorm layers instead the batch statistics.