[Verse 1] From LeNet's simple start to AlexNet's fame VGG went deeper, ResNet broke the chain Of vanishing gradients with skip connections EfficientNet scaled with smart reflections Convolutional layers see the patterns flow Pooling layers downsample what we need to know [Chorus] CNN architectures learning to see Filters and features dancing free Transfer learning saves the day Fine-tune models in your own way From classification to detection Computer vision's new direction [Verse 2] YOLO detects objects in real-time speed Faster R-CNN when precision's what you need U-Net segments with encoder-decoder style Mask R-CNN goes that extra mile Each architecture built for different tasks Custom solutions for what the problem asks [Chorus] CNN architectures learning to see Filters and features dancing free Transfer learning saves the day Fine-tune models in your own way From classification to detection Computer vision's new direction [Bridge] Vision Transformers changed the game Attention mechanisms stake their claim Self-attention patches process images new DINOv2 learns without labels too But CNNs still reign for many tasks Inductive bias gives what vision asks [Verse 3] Take ResNet fifty pretrained weights Freeze the backbone, fine-tune what creates Your custom classes at the final layer Data augmentation makes you a player Rotation, flipping, color shifts applied Robust training keeps overfitting denied [Chorus] CNN architectures learning to see Filters and features dancing free Transfer learning saves the day Fine-tune models in your own way From classification to detection Computer vision's new direction [Outro] PyTorch loads the model state Custom datasets sealed our fate Convolutional neural networks reign In the visual learning domain
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