vit

Vision Transformer

Introduction

@article{dosoViTskiy2020,
  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
  author={DosoViTskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
  journal={arXiv preprint arXiv:2010.11929},
  year={2020}
}

Results and models

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
UPerNet	ViT-B + MLN	512x512	80000	9.20	6.94	47.71	49.51	config	model | log
UPerNet	ViT-B + MLN	512x512	160000	9.20	7.58	46.75	48.46	config	model | log
UPerNet	ViT-B + LN + MLN	512x512	160000	9.21	6.82	47.73	49.95	config	model | log
UPerNet	DeiT-S	512x512	80000	4.68	29.85	42.96	43.79	config	model | log
UPerNet	DeiT-S	512x512	160000	4.68	29.19	42.87	43.79	config	model | log
UPerNet	DeiT-S + MLN	512x512	160000	5.69	11.18	43.82	45.07	config	model | log
UPerNet	DeiT-S + LN + MLN	512x512	160000	5.69	12.39	43.52	45.01	config	model | log
UPerNet	DeiT-B	512x512	80000	7.75	9.69	45.24	46.73	config	model | log
UPerNet	DeiT-B	512x512	160000	7.75	10.39	45.36	47.16	config	model | log
UPerNet	DeiT-B + MLN	512x512	160000	9.21	7.78	45.46	47.16	config	model | log
UPerNet	DeiT-B + LN + MLN	512x512	160000	9.21	7.75	45.37	47.23	config	model | log