Image Classification on ImageNet

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Top 1 Accuracy

Rank	Model	Top 1 Accuracy	Number of params	GFLOPs	Hardware Burden	Top 5 Accuracy	Operations per network pass	Paper	Code	Year
1	Vision Transformers for Kidney Stone Ima 자동 추출	95.2	–	–	–	–	–	Vision Transformers for Kidney Stone Image Classification: A Comparative Study with CNNs		2025
2	AEGIS 자동 추출	92.1	–	–	–	–	–	AEGIS: A Semantic GAN and Evidential Learning Frameworkfor Robust Adversarial Detection in Vision Sensors		2026
3	CoCa (finetuned)	91.0%	2100M	–	–	–	–	CoCa: Contrastive Captioners are Image-Text Foundation Models	mlfoundations/open_clip · facebookresearch/multimodal · lucidrains/CoCa-pytorch · +3	2022
4	Model soups (BASIC-L)	90.98%	2440M	–	–	–	–	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time	mlfoundations/model-soups · Burf/ModelSoups · facebookresearch/ModelRatatouille · +3	2022
5	Model soups (ViT-G/14)	90.94%	1843M	–	–	–	–	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time	mlfoundations/model-soups · Burf/ModelSoups · facebookresearch/ModelRatatouille · +3	2022
6	DaViT-G	90.4%	1437M	1038	–	–	–	DaViT: Dual Attention Vision Transformers	rwightman/pytorch-image-models · leondgarse/keras_cv_attention_models · dingmyu/davit · +1	2022
7	DaViT-H	90.2%	362M	334	–	–	–	DaViT: Dual Attention Vision Transformers	rwightman/pytorch-image-models · leondgarse/keras_cv_attention_models · dingmyu/davit · +1	2022
7	Meta Pseudo Labels (EfficientNet-L2)	90.2%	480M	–	95040G	98.8	–	Meta Pseudo Labels	google-research/google-research · kekmodel/MPL-pytorch · sayakpaul/PAWS-TF · +6	2020
9	SwinV2-G	90.17%	3000M	–	–	–	–	Swin Transformer V2: Scaling Up Capacity and Resolution	rwightman/pytorch-image-models · microsoft/Swin-Transformer · PaddlePaddle/PaddleDetection · +20	2021
10	MAWS (ViT-6.5B)	90.1%	6500M	–	–	–	–	The effectiveness of MAE pre-pretraining for billion-scale pretraining	facebookresearch/maws	2023