Deep Learning, Pytorch, Fine Tuning, Computer Vision, Transfer Learning.
targets) # Perform backward pass and gradient scaling scaler.scale(loss).backward() # Update model parameters scaler.step(optimizer) scaler.update() # Print training progress if batch_idx % log_interval == 0: print(f"Epoch {epoch+1}/{num_epochs} | Batch {batch_idx}/{len(train_loader)} | Loss: