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SOTA
이미지 분류
Image Classification On Cifar 10
Image Classification On Cifar 10
평가 지표
Percentage correct
평가 결과
이 벤치마크에서 각 모델의 성능 결과
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모델 이름
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Paper Title
Repository
DINOv2 (ViT-g/14, frozen model, linear eval)
99.5
DINOv2: Learning Robust Visual Features without Supervision
ViT-H/14
99.5
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
µ2Net (ViT-L/16)
99.49
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
ViT-L/16
99.42
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
CaiT-M-36 U 224
99.4
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CvT-W24
99.39
CvT: Introducing Convolutions to Vision Transformers
BiT-L (ResNet)
99.37
Big Transfer (BiT): General Visual Representation Learning
RDNet-L (224 res, IN-1K pretrained)
99.31
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs
RDNet-B (224 res, IN-1K pretrained)
99.31
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs
ViT-B (attn fine-tune)
99.3
Three things everyone should know about Vision Transformers
Heinsen Routing + BEiT-large 16 224
99.2
An Algorithm for Routing Vectors in Sequences
ViT-B/16 (PUGD)
99.13
Perturbated Gradients Updating within Unit Space for Deep Learning
Astroformer
99.12
Astroformer: More Data Might not be all you need for Classification
CeiT-S (384 finetune resolution)
99.1
Incorporating Convolution Designs into Visual Transformers
TNT-B
99.1
Transformer in Transformer
DeiT-B
99.1
Training data-efficient image transformers & distillation through attention
EfficientNetV2-L
99.1
EfficientNetV2: Smaller Models and Faster Training
AutoFormer-S | 384
99.1
AutoFormer: Searching Transformers for Visual Recognition
VIT-L/16 (Spinal FC, Background)
99.05
Reduction of Class Activation Uncertainty with Background Information
LaNet
99.03
Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search
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