Hi, I am working on a CNN model for MRI brain images classification (Alzheimer disease), I use transfer learning method for image classification - vgg16 model trained on ImageNet (1000 classes). I’ve modified last layer so the size of each output sample is set to 2.
I am using SGD optimizer, weighted CrossEntropyLoss as loss function because dataset is not balanced and learning rate scheduler - StepLR.
I encountered a problem with validation accuracy that stays (almost) the same or is decreasing (approximately 70% whereas state of the art in this topic is around 95-99%), I thought that the problem is overfitting but then I’ve tried to use L2 regularization, data augmentation and dropout but nothing helped. MRI images are normalized.
Here is a code of my training function
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = 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)
for phase in ['train', 'val']:
if phase == 'train':
model.train()
else:
model.eval()
running_loss = 0.0
running_corrects = 0
for inputs, masks, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs.float())
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
if phase == 'train':
loss.backward()
optimizer.step()
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))
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
model.load_state_dict(best_model_wts)
return model
I am training the model for 100 epochs.
Learning rate is 0.001
I would appreciate any help. Thanks in advance