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Image Classification On Dtd

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title	Repository
Linear FT(ViT-L/14)	90.0	Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
RADAM (ConvNeXt-L)	84.0	RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps
µ2Net+ (ViT-L/16)	82.23	A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems
Bamboo (ViT-B/16)	81.9	Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy
µ2Net (ViT-L/16)	81.0	An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
SEER (RegNet10B - linear eval)	80.5	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Inceptionv4	79.79	Non-binary deep transfer learning for image classification
TWIST (ResNet-50)	76.6	Self-Supervised Learning by Estimating Twin Class Distributions
TransBoost-ResNet50	76.49	TransBoost: Improving the Best ImageNet Performance using Deep Transduction
NNCLR	75.5	With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
Inceptionv4 (random initialization)	66.8	Non-binary deep transfer learning for image classification

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