vc1-large
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vc1-large-permissive
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View details ↗vfusion3d
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View details ↗VGGSfM
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View details ↗VGGT-1B
facebook**Meta AI Research**; **University of Oxford, VGG** Visual Geometry Grounded Transformer (VGGT, CVPR 2025) is a feed-forward neural network that directly infers all key 3D attributes of a scene, including extrinsic and intrinsic camera parameters, point maps, depth maps, and 3D point tracks, **from one, a few, or hundreds of its views, within seconds**. If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work:
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View details ↗VGGT-1B-Commercial
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View details ↗vggt_alpha
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View details ↗VGGT-Omega
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View details ↗VGGT_tracker_fixed
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View details ↗video_seal
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View details ↗vit-mae-base
facebookVision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick and first released in this repository. Disclaimer: The team releasing MAE did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like). Images are presented to the model as a sequence of fixed-size patches.
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View details ↗vit-mae-huge
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View details ↗vit-mae-large
facebookVision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick and first released in this repository. Disclaimer: The team releasing MAE did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like). Images are presented to the model as a sequence of fixed-size patches.
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View details ↗vit-msn-base
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View details ↗vit-msn-base-4
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View details ↗vit-msn-large
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View details ↗vit-msn-large-7
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View details ↗vit-msn-small
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