In recent years, self-supervised learning has emerged as a powerful technique for training computer vision models without requiring large amounts of labeled data. Models trained using self-supervision can learn rich representations directly from images, circumventing the need for manual image labeling.<\/p>\n\n\n\n
DINOv2 now on Huggingface: <\/strong>https:\/\/huggingface.co\/docs\/transformers\/main\/model_doc\/dinov2<\/a><\/p>\n\n\n\n Github:<\/strong> https:\/\/github.com\/facebookresearch\/dinov2<\/a><\/p>\n\n\n\n Demos<\/strong>: https:\/\/dinov2.metademolab.com\/<\/a><\/p>\n\n\n\n Meta AI has developed a breakthrough self-supervised learning model called DINOv2 that achieves state-of-the-art results matching or exceeding traditional supervised computer vision models. In this article, we’ll provide an introduction to DINOv2, explain how it works, discuss its applications, and provide pointers for getting started.<\/p>\n\n\n\n DINOv2 is a self-supervised model based on Vision Transformers (ViT) architecture<\/a>. It was trained on a large dataset of 142 million unlabeled images scraped from the web. DINOv2 shows remarkable performance on image classification, segmentation, retrieval and even specialized tasks like depth estimation without needing any fine-tuning.<\/p>\n\n\n\nOverview of DINOv2<\/h2>\n\n\n\n