Meta AI has just released DINOv3, a breakthrough self-supervised computer vision model that sets new standards for versatility and accuracy across dense prediction tasks, all without the need for labeled data. DINOv3 employs self-supervised learning (SSL) at an unprecedented scale, training on 1.7 billion images with a 7 billion parameter architecture. For the first time, a single frozen vision backbone outperforms domain-specialized solutions across multiple visual tasks, such as object detection, semantic segmentation, and video tracking—requiring no fine-tuning for adaptation.