[Verse 1] From LeNet's simple seven layers bright Through AlexNet's dropout revolution VGG stacked deeper, pixel sight ResNet's shortcuts solved degradation Skip connections jumping past the blocks EfficientNet scaled with compound locks [Chorus] Convolve and pool, extract the features Kernels sliding, learning creatures Transfer what you've learned before Fine-tune layers, unlock the door CNN, CNN, vision's blueprint Attention shows you what's important [Verse 2] YOLO sees it all in single pass Bounding boxes, confidence in hand Faster R-CNN, two-stage class Proposals refined across the land U-Net bridges encoder to decode Mask R-CNN paints each pixel's code [Chorus] Convolve and pool, extract the features Kernels sliding, learning creatures Transfer what you've learned before Fine-tune layers, unlock the door CNN, CNN, vision's blueprint Attention shows you what's important [Bridge] PyTorch loads the pretrained ResNet weights Freeze the backbone, train the head Data augmentation duplicates Rotation, cropping, colors spread Vision Transformers patch and embed Attention matrices, convolution's thread [Verse 3] Transfer learning saves the day ImageNet knowledge, weights intact Fine-tune gently, learning rate decay Shallow layers frozen, deep ones cracked DINOv2 learns without labels clean Self-supervised vision, unseen machine [Chorus] Convolve and pool, extract the features Kernels sliding, learning creatures Transfer what you've learned before Fine-tune layers, unlock the door CNN, CNN, vision's blueprint Attention shows you what's important [Outro] From classical nets to transformers new Computer vision's evolution grew Each architecture builds upon the last Neural networks see the future fast
← Unit 3.1 — Neural Network Fundamentals | Unit 3.3 — Recurrent Networks & Sequence Models →