I found a resnet example 1 where the best eval accuracy is given. I want to know if that is the final test/eval accuracy?
It is in the function below
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    start_time = time.time()
    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
                model.train() # Set model to training mode
else:
                model.eval() # Set model to evaluate mode
            running_loss = 0.0
            running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
                        optimizer.zero_grad()
                        loss.backward()
                        optimizer.step()
# statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)
if phase == 'train':
                scheduler.step()
            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

    time_elapsed = time.time() - start_time
print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
print('Validation Accuracy: {:4f}'.format(best_acc))
# load best model weights
    model.load_state_dict(best_model_wts)
return model