DINOv2 learned to see by looking at 142 million images — with no labels at all. Below, its raw patch features are projected to color (the paper's famous PCA "rainbows"), computed with the real model on a Raspberry Pi. Same-colored regions = parts the model considers similar… including across different objects.
Drag the divider: photo on the left, what DINOv2 sees on the right. Notice how heads match heads and legs match legs — nobody ever told it what those are.
Move your cursor over the left image — patches with the most similar DINOv2 features light up on the right, live (cosine similarity in your browser). Watch a retriever's ear find a dalmatian's ear.
Features this good, learned without labels, are why DINOv2/v3 power everything from forest mapping with the World Resources Institute to NASA JPL's Mars robots — one frozen backbone, endless tasks.
Each image becomes a ~37×37 grid of patches; each patch a 384-dim vector. We PCA those vectors to 3 components → RGB. Foreground/background separates on the first component, parts on the next three.
DINOv2 paper · code & weights (Apache 2.0) · the DINO constellation on the map.