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SOTA
半监督图像分类
Semi Supervised Image Classification On 2
Semi Supervised Image Classification On 2
评估指标
Top 1 Accuracy
Top 5 Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Top 1 Accuracy
Top 5 Accuracy
Paper Title
Repository
Semi-ViT (ViT-Huge)
84.3%
96.6%
Semi-supervised Vision Transformers at Scale
SimCLRv2 self-distilled (ResNet-152 x3, SK)
80.9%
95.5%
Big Self-Supervised Models are Strong Semi-Supervised Learners
SimCLRv2 (ResNet-152 x3, SK)
80.1%
95.0%
Big Self-Supervised Models are Strong Semi-Supervised Learners
SimCLRv2 distilled (ResNet-50 x2, SK)
80.2%
95.0%
Big Self-Supervised Models are Strong Semi-Supervised Learners
SimCLRv2 distilled (ResNet-50)
77.5%
93.4%
Big Self-Supervised Models are Strong Semi-Supervised Learners
TWIST (ResNet-50 x2)
75.3%
92.8%
Self-Supervised Learning by Estimating Twin Class Distributions
SimMatch + EPASS (ResNet-50)
75.3%
92.6
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
SimCLR (ResNet-50 4×)
-
92.6%
A Simple Framework for Contrastive Learning of Visual Representations
SequenceMatch (ResNet-50)
75.2%
91.9
SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning
SimCLRv2 (ResNet-50 x2)
73.9%
91.9%
Big Self-Supervised Models are Strong Semi-Supervised Learners
CoMatch + EPASS (ResNet-50)
74.1%
91.5
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
CoMatch (w. MoCo v2)
73.7%
91.4%
CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
Meta Pseudo Labels (ResNet-50)
73.89%
91.38%
Meta Pseudo Labels
CowMix (ResNet-152)
73.94%
91.24%
Milking CowMask for Semi-Supervised Image Classification
S4L-MOAM (ResNet-50 4×)
73.21%
91.23%
S4L: Self-Supervised Semi-Supervised Learning
Rotation + VAT + Ent. Min.
-
91.23%
S4L: Self-Supervised Semi-Supervised Learning
WCL (ResNet-50)
72.0%
91.2%
Weakly Supervised Contrastive Learning
SimCLR (ResNet-50 2×)
-
91.2%
A Simple Framework for Contrastive Learning of Visual Representations
CPC v2 (ResNet-161)
73.1%
91.2%
Data-Efficient Image Recognition with Contrastive Predictive Coding
RELICv2 (ResNet-50)
72.4%
91.2%
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
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