Introducing DINOv3: Self-supervised learning for vision at unprecedented scale
DINOv3 is a state-of-the-art computer vision model trained with self-supervised learning (SSL) that produces powerful, high-resolution image features. For the first time, a single frozen vision backbone outperforms specialized solutions on multiple long-standing dense prediction tasks.
Learn more here: https://ai.meta.com/blog/dinov3-self-supervised-vision-model/?utm_source=youtube&utm_medium=organic_social&utm_content=video&utm_campaign=dinov3
A few highlights of DINOv3:
1️⃣SSL enables 1.7B-image, 7B-param training without labels, supporting annotation-scarce scenarios including satellite imagery
2️⃣Produces excellent high-resolution features and state-of-the art performance on dense prediction tasks
3️⃣Versatile application across vision tasks and domains, all with a frozen backbone (no fine-tuning required)
4️⃣ Includes distilled smaller models (ViT-B, ViT-L) and ConvNeXt variants for deployment flexibility
To help foster innovation and collaboration in the computer vision community, we’re releasing DINOv3 under a commercial license with a full suite of pre-trained models, adapters, training and evaluation code, and (much!) more. Find them here: https://github.com/facebookresearch/dinov3
--
Subscribe: https://www.youtube.com/aiatmeta?sub_confirmation=1
Learn more about our work: https://ai.meta.com
Follow us on Twitter: https://twitter.com/aiatmeta
Follow us on Facebook: https://www.facebook.com/aiatmeta
Connect with us on LinkedIn: https://www.linkedin.com/showcase/aiatmeta/
Meta focuses on bringing the world together by advancing AI, powering meaningful and safe experiences, and conducting open research.
DINOv3 is a state-of-the-art computer vision model trained with self-supervised learning (SSL) that produces powerful, high-resolution image features. For the first time, a single frozen vision backbone outperforms specialized solutions on multiple long-standing dense prediction tasks.
Learn more here: https://ai.meta.com/blog/dinov3-self-supervised-vision-model/?utm_source=youtube&utm_medium=organic_social&utm_content=video&utm_campaign=dinov3
A few highlights of DINOv3:
1️⃣SSL enables 1.7B-image, 7B-param training without labels, supporting annotation-scarce scenarios including satellite imagery
2️⃣Produces excellent high-resolution features and state-of-the art performance on dense prediction tasks
3️⃣Versatile application across vision tasks and domains, all with a frozen backbone (no fine-tuning required)
4️⃣ Includes distilled smaller models (ViT-B, ViT-L) and ConvNeXt variants for deployment flexibility
To help foster innovation and collaboration in the computer vision community, we’re releasing DINOv3 under a commercial license with a full suite of pre-trained models, adapters, training and evaluation code, and (much!) more. Find them here: https://github.com/facebookresearch/dinov3
—
Subscribe: https://www.youtube.com/aiatmeta?sub_confirmation=1
Learn more about our work: https://ai.meta.com
Follow us on Twitter: https://twitter.com/aiatmeta
Follow us on Facebook: https://www.facebook.com/aiatmeta
Connect with us on LinkedIn: https://www.linkedin.com/showcase/aiatmeta/
Meta focuses on bringing the world together by advancing AI, powering meaningful and safe experiences, and conducting open research.
