الرئيسية الأخبار أحدث الأوراق البحثية الدروس مجموعات البيانات الموسوعة SOTA نماذج LLM لوحة الأداء GPU الفعاليات

العربية

Object Recognition On Shape Bias

المقاييس

shape bias

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	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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