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SOTA
图像分类
Image Classification On Imagenet V2
Image Classification On Imagenet V2
评估指标
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
Top 1 Accuracy
Paper Title
Repository
ResMLP-S24/16
69.8
ResMLP: Feedforward networks for image classification with data-efficient training
-
ResMLP-S12/16
66.0
ResMLP: Feedforward networks for image classification with data-efficient training
-
Mixer-B/8-SAM
65.5
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
-
MAWS (ViT-6.5B)
84.0
The effectiveness of MAE pre-pretraining for billion-scale pretraining
-
LeViT-256
69.9
LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
-
CAIT-M36-448
76.7
-
-
ViT-B-36x1
73.9
Three things everyone should know about Vision Transformers
-
MOAT-1 (IN-22K pretraining)
78.4
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
-
ResNet50 (A1)
68.7
ResNet strikes back: An improved training procedure in timm
-
Discrete Adversarial Distillation (ViT-B, 224)
71.7
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
-
SwinV2-B
78.08
Swin Transformer V2: Scaling Up Capacity and Resolution
-
Model soups (ViT-G/14)
84.22
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
-
ViT-B/16-SAM
67.5
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
-
LeViT-192
68.7
LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
-
SEER (RegNet10B)
76.2
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
-
MOAT-2 (IN-22K pretraining)
79.3
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
-
VOLO-D4
77.8
VOLO: Vision Outlooker for Visual Recognition
-
MOAT-3 (IN-22K pretraining)
80.6
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
-
SwinV2-G
84.00%
Swin Transformer V2: Scaling Up Capacity and Resolution
-
ResMLP-B24/8
73.4
ResMLP: Feedforward networks for image classification with data-efficient training
-
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