⚡ Transformers
Transformer Models for Vision
The attention-based architecture transforming Computer Vision — from ViT to DETR and beyond
SOTA
Results
1B+
Parameters
2020+
Vision Era
Key Features
What Makes This Technology Special
Vision Transformer (ViT)
Uses patch embedding instead of convolutions for image classification
DETR
Detection Transformer — anchor-free end-to-end object detection
Swin Transformer
Hierarchical vision transformer for dense prediction tasks
Self-Attention
Attention mechanism that understands relationships across image regions
Foundation Models
Large-scale models trained on massive data — CLIP, SAM
Multi-Modal
Bridge vision and natural language — CLIP, GPT-4V
Benefits
Why You Need This Technology
Outperforms CNN
Achieves state-of-the-art scores on many vision benchmarks
Global Context Understanding
Self-attention sees relationships across the entire image
Language Integration
Control vision tasks with natural language prompts
Foundation Model Era
The basis for modern multi-capable AI systems
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