{"id":2511,"date":"2023-09-01T14:30:53","date_gmt":"2023-09-01T14:30:53","guid":{"rendered":"https:\/\/mlnews.dev\/?p=2511"},"modified":"2023-10-03T04:11:58","modified_gmt":"2023-10-03T04:11:58","slug":"dinov2-state-of-the-art-computer-vision-models-with-self-supervised-learning","status":"publish","type":"post","link":"https:\/\/mlnews.dev\/dinov2-state-of-the-art-computer-vision-models-with-self-supervised-learning\/","title":{"rendered":"DINOv2: State-of-the-art computer vision models with self-supervised learning"},"content":{"rendered":"\n
DINOv2’s latest progress in natural language processing opens the way for innovative advancements in computer vision models. A team of researchers from Meta AI Research and Inria has achieved major advances in producing all-purpose visual features.<\/p>\n\n\n\n
DINOv2 can learn from any collection of photos since it utilizes self-supervision. Learning features via using discriminative signals between photos or groupings of images. This technique has its beginning in early deep learning work, but it became popular with the emergence of instances of classification perspective. Several enhancements were implemented depending on instance-level goals or clustering. These perspectives perform better on common benchmarks like ImageNet, but they are difficult on bigger-size models. Reexamine these methodologies’ training in the setting of big pretraining datasets and models.<\/p>\n\n\n\n
DINOv2 is an automated process for creating dedicated, diversified, and curated picture datasets rather than uncurated data, as is commonly done in the self-supervised literature.<\/p>\n\n\n\n
DINOv2 can learn from any collection of photos since it utilizes self-supervision. DINOv2 can also adopt properties that the current standard technique cannot, For example, depth estimates.<\/p>\n\n\n\n