I think I don’t have a good understanding of train accuracy. This is the snippet for train the model and calculates the loss and train accuracy for segmentation task.
 for epoch in range(2):  # loop over the dataset multiple times
        
        running_loss = 0
        total_train = 0
        correct_train = 0
        for i, data in enumerate(train_loader, 0):
            # get the inputs
            t_image, mask = data
            t_image, mask = Variable(t_image.to(device)), Variable(mask.to(device))
        

            # zeroes the gradient buffers of all parameters
            optimizer.zero_grad()
            # forward + backward + optimize
            outputs = model(t_image) # forward
            loss = criterion(outputs, mask) # calculate the loss
            loss.backward() # back propagation
            optimizer.step() # update gradients
            running_loss += loss.item()
            
            # accuracy
            _, predicted = torch.max(outputs.data, 1)
            total_train += mask.size(0)
            correct_train += predicted.eq(mask.data).sum().item()
            train_accuracy = 100 * correct_train / total_train
           #avg_accuracy = train_accuracy / len(train_loader)                                     
            
            print('Epoch {}, train Loss: {:.3f}'.format(epoch, loss.item()), "Training Accuracy: %d %%" % (train_accuracy))

I am getting a number like below for the training accuracy which I assume is the amount of pixel which corresponds with the ground truth (mask). is that correct?
Epoch 0, train Loss: 0.721 Training Accuracy: 500300 %
Epoch 0, train Loss: 0.707 Training Accuracy: 676000 %

How can I calculate the accuracy of the network that shows the performance of the model? (ex. The accuracy of the network on the 10000 test images: 54 % in pytorch tutorial but this is for the classification task 60 )