I want to train a semantic segmentation model using UNet layers. I trained the model one time without any convergence criterion and max epoch of 500 and the other time with a convergence criterion (validation patience of 10 and validation frequency of 25). The secnd training process converged at a lower epoch number compared to the first one. The results of the application of the second model is significantly less accurate than the first one on the test slices. I noticed that the training loss in the second training process is different from the first one. My question is that why defining a convergence criterion changes the training loss curve? I would be thankful if any one can help me understand why this is happening. The learning rate is constant and equal to 1e-05.
Training curves without convergence criterion:
Training curves with convergence criterion:
