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SOTA
自监督图像分类
Self Supervised Image Classification On
Self Supervised Image Classification On
评估指标
Number of Params
Top 1 Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Number of Params
Top 1 Accuracy
Paper Title
Repository
DINOv2+reg (ViT-g/14)
1100M
87.1
Vision Transformers Need Registers
DINOv2 (ViT-g/14 @448)
1100M
86.7%
DINOv2: Learning Robust Visual Features without Supervision
DINOv2 (ViT-g/14)
1100M
86.5%
DINOv2: Learning Robust Visual Features without Supervision
DINOv2 distilled (ViT-L/14)
307M
86.3%
DINOv2: Learning Robust Visual Features without Supervision
MIM-Refiner (D2V2-ViT-H/14)
632M
84.7%
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
DINOv2 distilled (ViT-B/14)
85M
84.5%
DINOv2: Learning Robust Visual Features without Supervision
MIM-Refiner (MAE-ViT-2B/14)
1890M
84.5%
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
MIM-Refiner (MAE-ViT-H/14
632M
83.7%
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
MIM-Refiner (D2V2-ViT-L/16)
307M
83.5%
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
MIM-Refiner (MAE-ViT-L/16)
307M
82.8%
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
iBOT (ViT-L/16) (IN22k)
307M
82.3%
iBOT: Image BERT Pre-Training with Online Tokenizer
MAE-CT (ViT-H/16)
632M
82.2%
Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget
Mugs (VIT-L/16)
307M
82.1%
Mugs: A Multi-Granular Self-Supervised Learning Framework
MAE-CT (ViT-L/16
307M
81.5%
Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget
EsViT (Swin-B)
87M
81.3
Efficient Self-supervised Vision Transformers for Representation Learning
iBOT (ViT-L/16)
307M
81.3%
iBOT: Image BERT Pre-Training with Online Tokenizer
DINOv2 distilled (ViT-S/14)
21M
81.1%
DINOv2: Learning Robust Visual Features without Supervision
MoCo v3 (ViT-BN-L/7)
304M
81.0%
An Empirical Study of Training Self-Supervised Vision Transformers
EsViT(Swin-S)
49M
80.8
Efficient Self-supervised Vision Transformers for Representation Learning
MSN (ViT-L/7)
306M
80.7%
Masked Siamese Networks for Label-Efficient Learning
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