DINO
Attention maps that learned to see objects — with zero labels
Latest: DINO (2021); superseded by DINOv2 (2023) and DINOv3 (2025)
Self-distillation with no labels: DINO showed that Vision Transformers trained purely self-supervised develop attention maps that segment objects without ever being told what an object is. This 2021 FAIR/Inria result seeded Meta's entire label-free backbone program and popularized emergent properties in ViTs.
Why it matters
DINO showed that a Vision Transformer trained with self-distillation and no labels develops attention maps that segment objects on their own — an emergent property that became one of the most recognizable images in vision research. It seeded Meta's entire label-free backbone program (DINOv2, DINOv3) and mainstreamed emergent behavior in ViTs.
Facts
- The famous attention-map visualizations — a ViT 'seeing' object outlines with zero labels — became one of the most recognizable images in modern vision research.
Try it yourself
dino-vitb16 on Hugging Face ↗ Code on GitHub ↗ Read the paper ↗