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Object Recognition On Shape Bias

Métriques

shape bias

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	shape bias	Paper Title	Repository
ViT-22B-384	86.4	Scaling Vision Transformers to 22 Billion Parameters
ResNet-50	22.1	ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
ViT-22B-560	83.8	Scaling Vision Transformers to 22 Billion Parameters
GoogLeNet	31.2	ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
ViT-22B-224	78.0	Scaling Vision Transformers to 22 Billion Parameters
CLIP (ViT-B)	79.9	Learning Transferable Visual Models From Natural Language Supervision
SWSL (ResNet-50)	28.6	Billion-scale semi-supervised learning for image classification
SimCLR (ResNet-50x4)	40.7	A Simple Framework for Contrastive Learning of Visual Representations
Parti	91.7	Intriguing properties of generative classifiers
VGG-16	17.2	ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Stable Diffusion	92.7	Intriguing properties of generative classifiers
SimCLR (ResNet-50x1)	38.9	A Simple Framework for Contrastive Learning of Visual Representations
SWSL (ResNeXt-101)	49.8	Billion-scale semi-supervised learning for image classification
ResNet-50 (with strong augmentations)	62.2	The Origins and Prevalence of Texture Bias in Convolutional Neural Networks	-
ResNet-50 (L2 eps 5.0 adv trained)	69.5	Do Adversarially Robust ImageNet Models Transfer Better?
SimCLR (ResNet-50x2)	41.7	A Simple Framework for Contrastive Learning of Visual Representations
Imagen	98.7	Intriguing properties of generative classifiers
AlexNet	42.9	ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

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