Image Classification On Imagenet V2

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Top 1 Accuracy

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이 벤치마크에서 각 모델의 성능 결과

		Paper Title	Repository
Model soups (BASIC-L)	84.63	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
ViT-e	84.3	PaLI: A Jointly-Scaled Multilingual Language-Image Model
Model soups (ViT-G/14)	84.22	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
MAWS (ViT-6.5B)	84.0	The effectiveness of MAE pre-pretraining for billion-scale pretraining
SwinV2-G	84.00%	Swin Transformer V2: Scaling Up Capacity and Resolution
ViT-G/14	83.33	Scaling Vision Transformers
MAWS (ViT-2B)	83.0	The effectiveness of MAE pre-pretraining for billion-scale pretraining
MOAT-4 (IN-22K pretraining)	81.5	MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
SWAG (ViT H/14)	81.1	Revisiting Weakly Supervised Pre-Training of Visual Perception Models
MOAT-3 (IN-22K pretraining)	80.6	MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
MOAT-2 (IN-22K pretraining)	79.3	MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
MOAT-1 (IN-22K pretraining)	78.4	MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
SwinV2-B	78.08	Swin Transformer V2: Scaling Up Capacity and Resolution
VOLO-D5	78	VOLO: Vision Outlooker for Visual Recognition
VOLO-D4	77.8	VOLO: Vision Outlooker for Visual Recognition
CAIT-M36-448	76.7	-	-
SEER (RegNet10B)	76.2	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
ResMLP-B24/8 22k	74.2	ResMLP: Feedforward networks for image classification with data-efficient training
ViT-B-36x1	73.9	Three things everyone should know about Vision Transformers
ResMLP-B24/8	73.4	ResMLP: Feedforward networks for image classification with data-efficient training

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Image Classification On Imagenet V2

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평가 결과