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الرئيسية
SOTA
Data Augmentation
Data Augmentation On Imagenet
Data Augmentation On Imagenet
المقاييس
Accuracy (%)
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
Accuracy (%)
Paper Title
Repository
DeiT-S (+MixPro)
81.3
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
ResNet-50 (LoRot-I)
77.71
Tailoring Self-Supervision for Supervised Learning
DeiT-T (+MixPro)
73.8
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
ResNet-50 (UA)
77.63
UniformAugment: A Search-free Probabilistic Data Augmentation Approach
ResNet-200 (DeepAA)
81.32
Deep AutoAugment
ResNet-200 (UA)
80.4
UniformAugment: A Search-free Probabilistic Data Augmentation Approach
DeiT-B (+MixPro)
82.9
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
ResNet-50 (RA)
77.6
RandAugment: Practical automated data augmentation with a reduced search space
ResNet-200 (AA)
80.0
AutoAugment: Learning Augmentation Policies from Data
ResNet-50 (DeepAA)
78.30
Deep AutoAugment
ResNet-50 (AA)
77.6
AutoAugment: Learning Augmentation Policies from Data
ResNet-50 (Faster AA)
76.5
Faster AutoAugment: Learning Augmentation Strategies using Backpropagation
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ResNet-50 (Fast AA)
77.6
Fast AutoAugment
ResNet-200 (Fast AA)
80.6
Fast AutoAugment
ResNet-50 (TA wide)
78.07
TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation
ResNet-50 (LoRot-E)
77.72
Tailoring Self-Supervision for Supervised Learning
ResNet-50 (DADA)
77.5
DADA: Differentiable Automatic Data Augmentation
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