DINOv3
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DINOv3 is a self‑supervised vision foundation model that scales data and model size, introduces a Gram anchoring loss to preserve dense patch consistency during long training, and adds post‑hoc tweaks for resolution, size, and text alignment. With a frozen backbone, it sets new results across dense and global tasks without task‑specific fine‑tuning.
Key idea: Gram anchoring. Regularize patch features by matching the student’s patch‑feature Gram matrix to that of an earlier “Gram teacher,” improving local consistency while leaving global features flexible. Implemented late in training, it can repair degraded dense features.
Immediate dense gains. Applying Gram anchoring quickly boosts VOC and ADE20k segmentation and strengthens robustness on ObjectNet; using higher‑resolution teacher features adds further improvements.
High‑res teacher trick. Compute teacher features at 2× input resolution, then downsample to smooth patch similarities; this yields better Gram targets and extra mIoU on ADE20k.
Frozen‑backbone SOTA. A lightweight Plain‑DETR decoder on top of a frozen DINOv3 backbone reaches 66.1 mAP on COCO, rivaling or beating specialized detectors with hundreds of millions of trainable parameters.
General, scalable suite. The release covers multiple model sizes and training recipes designed to serve diverse resource and deployment needs while outperforming prior self‑ and weakly‑supervised foundations.
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