Domain Generalization On Imagenet R

Métriques

Top-1 Error Rate

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Top-1 Error Rate	Paper Title	Repository
Model soups (ViT-G/14)	4.54	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
GPaCo (ViT-L)	39.7	Generalized Parametric Contrastive Learning
SEER (RegNet10B)	43.9	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Pyramid Adversarial Training Improves ViT (Im21k)	42.16	Pyramid Adversarial Training Improves ViT Performance
VOLO-D5+HAT	40.3	Improving Vision Transformers by Revisiting High-frequency Components
Stylized ImageNet (ResNet-50)	58.5	ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Mixer-B/8-SAM	76.5	When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
LLE (ViT-B/16, SWAG, Edge Aug)	31.3	A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
Model soups (BASIC-L)	3.90	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
MAE (ViT-H, 448)	33.5	Masked Autoencoders Are Scalable Vision Learners
PRIME with JSD (ResNet-50)	53.7	PRIME: A few primitives can boost robustness to common corruptions
ConvFormer-B36	48.9	MetaFormer Baselines for Vision
CAR-FT (CLIP, ViT-L/14@336px)	10.3	Context-Aware Robust Fine-Tuning	-
ConvFormer-B36 (384)	47.8	MetaFormer Baselines for Vision
ConvNeXt-XL (Im21k, 384)	31.8	A ConvNet for the 2020s
ResNet-152x2-SAM	71.9	When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
FAN-Hybrid-L(IN-21K, 384))	28.9	Understanding The Robustness in Vision Transformers
RVT-Ti*	56.1	Towards Robust Vision Transformer
RVT-S*	52.3	Towards Robust Vision Transformer
ConvFormer-B36 (IN21K, 384)	33.5	MetaFormer Baselines for Vision

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