Hi, I’m comparing two semantic segmentation models FCN and DeepLabV3 with the same dataset, same seed, same loss function Cross Entropy. But i’m confused about how to pick the right model weights.

For each model, should I pick the mode weights with smallest test loss, or the highest evaluation metrics (mean iou, pixel acc)? Because I remember in order to prevent overfitting, we should choose the smallest test loss. However, in this tutorial, the author picks the greatest eval acc instead of smallest test loss.

# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
    best_acc = epoch_acc
    best_model_wts = copy.deepcopy(model.state_dict())


The FCN model has better training loss, training acc, training iou, test acc, test iou but larger test loss than DeepLabV3 model along all epochs. In such case, can I say FCN model beats the DeepLabV3 model?