Domain Generalization On Imagenet A

Metrics

Top-1 accuracy %

Results

Performance results of various models on this benchmark

		Paper Title	Repository
Model soups (BASIC-L)	94.17	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Model soups (ViT-G/14)	92.67	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
µ2Net+ (ViT-L/16)	84.53	A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems
CAR-FT (CLIP, ViT-L/14@336px)	81.5	Context-Aware Robust Fine-Tuning	-
CAFormer-B36 (IN-21K, 384)	79.5	MetaFormer Baselines for Vision
MAE (ViT-H, 448)	76.7	Masked Autoencoders Are Scalable Vision Learners
FAN-Hybrid-L(IN-21K, 384)	74.5	Understanding The Robustness in Vision Transformers
ConvFormer-B36 (IN-21K, 384)	73.5	MetaFormer Baselines for Vision
CAFormer-B36 (IN-21K)	69.4	MetaFormer Baselines for Vision
ConvNeXt-XL (Im21k, 384)	69.3	A ConvNet for the 2020s
MAE+DAT (ViT-H)	68.92	Enhance the Visual Representation via Discrete Adversarial Training
ConvFormer-B36 (IN-21K)	63.3	MetaFormer Baselines for Vision
Pyramid Adversarial Training Improves ViT (Im21k)	62.44	Pyramid Adversarial Training Improves ViT Performance
CAFormer-B36 (384)	61.9	MetaFormer Baselines for Vision
TransNeXt-Base (IN-1K supervised, 384)	61.6	TransNeXt: Robust Foveal Visual Perception for Vision Transformers
TransNeXt-Small (IN-1K supervised, 384)	58.3	TransNeXt: Robust Foveal Visual Perception for Vision Transformers
ConvFormer-B36 (384)	55.3	MetaFormer Baselines for Vision
SEER (RegNet10B)	52.7	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
TransNeXt-Base (IN-1K supervised, 224)	50.6	TransNeXt: Robust Foveal Visual Perception for Vision Transformers
CAFormer-B36	48.5	MetaFormer Baselines for Vision

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Domain Generalization On Imagenet A

Metrics

Results