Meta AI announced the release of Dinov2 two years after debuting DINO, a self-supervised vision transformer model. In contrast to other models of a similar type, like CLIP, this one performs exceptionally well and doesn’t need modifications.
Meta accomplished this by pretraining on a vast volume of unprocessed text utilizing pretext objectives like non-supervised word vectors or language modeling. The model is free source and has been pre-trained on 142 million photos in an unlabeled, self-supervised manner.
High-performance characteristics offered by DINOv2 can be used as direct inputs for basic linear classifiers. Due to its adaptability, DINOv2 may be used to build multifunctional backbones for a variety of computer vision jobs, according to a blog post from the company.
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DinoV2 assists with tasks like depth estimation, image classification, semantic segmentation, and image retrieval without the requirement for expensive labelled data, which will save developers a great deal of time and resources.
According to Meta, the model uses self-supervised learning and produces results that are comparable to or better than those produced by the traditional approaches used in the relevant fields.
Self-supervised learning is the key appeal since it enables DINOv2 to create adaptable, general frameworks for a range of computer vision applications and tasks. Before using the model in various domains, no further fine-tuning is necessary.
