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Image Classification
Image Classification On Dtd
Image Classification On Dtd
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Paper Title
Repository
SEER (RegNet10B - linear eval)
80.5
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
TWIST (ResNet-50)
76.6
Self-Supervised Learning by Estimating Twin Class Distributions
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µ2Net (ViT-L/16)
81.0
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
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
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
Linear FT(ViT-L/14)
90.0
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
Inceptionv4
79.79
Non-binary deep transfer learning for image classification
Inceptionv4 (random initialization)
66.8
Non-binary deep transfer learning for image classification
0 of 11 row(s) selected.
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