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Object Recognition On Shape Bias

Metrics

shape bias

Results

Performance results of various models on this benchmark

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

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