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ViT-Huge model (ViT-H/16) from original paper (https://arxiv.org/abs/2010.11929).

ViT-Huge model (ViT-H/16) from original paper (https://arxiv.org/abs/2010.11929).

ViT model matching the architecture of the So400M model from "Scaling Vision Transformers to 400 Million Parameters" (https://arxiv.org/abs/2302.

Using hand picked window size for various resolutions. E-RADIO employs windowed attention, which may be sensitive to the choice of t

A method to perform model self-compression merges BN into conv layers converts MLP relative positional bias into precomputed

ViT-Base (ViT-B/14) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://g

ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://g

Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64

ViT-giant model (ViT-g/16) from original paper (https://arxiv.org/abs/2010.11929).

ViT-Huge model (ViT-H/16) from original paper (https://arxiv.org/abs/2010.11929).

ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 224x224, source ht

ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 224x224, source ht

ViT-Small (ViT-S/16)

ViT model matching the architecture of the So400M model from "Scaling Vision Transformers to 400 Million Parameters" (https://arxiv.org/abs/2302.

ViT model matching the architecture of the So400M model from "Scaling Vision Transformers to 400 Million Parameters" (https://arxiv.org/abs/2302.

ViT-Tiny (Vit-Ti/16)