👁️ Vision Transformer (ViT)
Transformers meet computer vision: Understanding images through self-attention
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🎯 What is Vision Transformer?
Vision Transformer (ViT) applies the Transformer architecture directly to images. Instead of using convolutional layers, ViT splits images into patches and processes them as sequences, just like words in text. This approach achieved state-of-the-art results on ImageNet with less computational resources when pre-trained on large datasets.
"An image is worth 16×16 words" - By treating image patches as tokens, ViT proves that pure attention mechanisms can excel at vision tasks without convolutions.
❌ Traditional CNNs
- •Local receptive fields
- •Inductive bias from convolutions
- •Limited global context
- •Fixed architecture design
✅ Vision Transformers
- •Global self-attention
- •Minimal inductive bias
- •Full image context from layer 1
- •Flexible, scalable architecture
Achieves 88.55% top-1 accuracy on ImageNet with ViT-H/14
Powers DETR and other detection-free object detection models
Used in Segformer, SegViT for semantic segmentation
📈 Performance & Scale
ViT's performance improves dramatically with scale. When pre-trained on large datasets (JFT-300M with 300 million images), ViT outperforms ResNet-based models while requiring significantly fewer computational resources during training.