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Image Classification On Dtd

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
SEER (RegNet10B - linear eval)80.5Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision-
TWIST (ResNet-50)76.6Self-Supervised Learning by Estimating Twin Class Distributions-
µ2Net (ViT-L/16)81.0An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems-
TransBoost-ResNet5076.49TransBoost: Improving the Best ImageNet Performance using Deep Transduction-
NNCLR75.5With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations-
RADAM (ConvNeXt-L)84.0RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps-
µ2Net+ (ViT-L/16)82.23A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems-
Bamboo (ViT-B/16)81.9Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy-
Linear FT(ViT-L/14)90.0Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models-
Inceptionv479.79Non-binary deep transfer learning for image classification-
Inceptionv4 (random initialization)66.8Non-binary deep transfer learning for image classification-
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Image Classification On Dtd | SOTA | HyperAI