Image Classification on ImageNet 1k (10% labels)
84.9Top-1 AccSemi-SST
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| Semi-SSTArchitecture Type=Transformer, Architecture=ViT-Huge, Param=632M2025.05 | 84.9 | — | — | |
| Semi-SSTArchitecture=ViT-Huge, Param=632M2025.05 | 84.9 | — | — | |
| Super-SSTArchitecture Type=Transformer, Architecture=ViT-Huge, Param=632M2025.05 | 84.8 | — | — | |
| Super-SSTArchitecture=ViT-Huge, Param=632M2025.05 | 84.8 | — | — | |
| Semi-ViTArchitecture Type=Transformer, Architecture=ViT-Huge, Param=632M2025.05 | 84.3 | — | — | |
| Semi-ViTArchitecture Type=Transformer, Architecture=ViT-Large, Param=307M2025.05 | 83.3 | — | — | |
| Super-SSTArchitecture Type=Transformer, Architecture=ViT-Small, Param=22M, distilled=true2025.05 | 80.3 | — | — | |
| Super-SST (distilled)Architecture=ViT-Small, Param=22M2025.05 | 80.3 | — | — | |
| SimCLR v2Arch.=RN152w3+SK, Param.=795M2022.02 | 80.1 | — | — | |
| Semi-ViTArchitecture Type=Transformer, Architecture=ViT-Base, Param=86M2025.05 | 79.7 | — | — | |
| SEERArch.=RG10B, Param.=10B2022.02 | 78.8 | — | — | |
| Semi-SSTArchitecture Type=CNN, Architecture=ConvNeXt-T, Param=28M2025.05 | 78.6 | — | — | |
| Semi-SSTArchitecture Type=Transformer, Architecture=ViT-Small, Param=22M2025.05 | 78.6 | — | — | |
| Semi-SSTArchitecture=ViT-Small, Param=22M2025.05 | 78.6 | — | — | |
| Super-SSTArchitecture Type=Transformer, Architecture=ViT-Small, Param=22M2025.05 | 78.3 | — | — | |
| Super-SSTArchitecture=ViT-Small, Param=22M2025.05 | 78.3 | — | — | |
| SEERArch.=RG256, Param.=1.5B2022.02 | 77.9 | — | — | |
| Super-SSTArchitecture Type=CNN, Architecture=ConvNeXt-T, Param=28M2025.05 | 77.9 | — | — | |
| SimMatchV2Architecture Type=CNN, Architecture=ConvNeXt-T, Param=28M2025.05 | 77.8 | — | — | |
| BYOLArch.=RN200, Param.=250M2022.02 | 77.7 | — | — | |
| Semi-ViTArchitecture Type=Transformer, Architecture=ViT-Small, Param=22M2025.05 | 77.1 | — | — | |
| SEERArch.=RG128, Param.=693M2022.02 | 76.7 | — | — | |
| SimMatchV2Architecture Type=CNN, Architecture=ResNet-50, Param=26M2025.05 | 76.2 | — | — | |
| SimMatchV2Architecture Type=Transformer, Architecture=ViT-Small, Param=22M2025.05 | 75.9 | — | — | |
| SemiformerArchitecture=ViT-S + Conv, Params=40M, Learning Paradigm=Semi-supervised2021.11 | 75.5 | — | — | |
| PAWSArchitecture Type=CNN, Architecture=ResNet-50, Param=26M2025.05 | 75.5 | — | — | |
| SemiFormerArchitecture Type=Transformer, Architecture=ViT-S+Conv, Param=42M2025.05 | 75.5 | — | — | |
| iBOTArch.=ViT-S/162021.11 | 75.1 | — | — | |
| ShrinkMatchPre-training=None, Method=ShrinkMatch, Epochs=400, Params (train / test)=31.8M / 25.6M2023.08 | 74.5 | 91.9 | — | |
| SimMatchArchitecture Type=CNN, Architecture=ResNet-50, Param=26M2025.05 | 74.4 | — | — | |
| DINOArch.=ViT-S/162021.11 | 74.3 | — | — | |
| SimMatch+Pre-training=None, Method=SimMatch+ [45], Epochs=400, Params (train / test)=30.0M / 25.6M2023.08 | 74.1 | 91.5 | — | |
| Semi-ViTArchitecture Type=CNN, Architecture=ConvNeXt-T, Param=28M2025.05 | 74.1 | — | — | |
| FixMatch-EMANPre-training=MoCo-EMAN [5], Method=FixMatch-EMAN [5], Epochs=1100, Params (train / test)=30.0M / 25.6M2023.08 | 74 | 90.9 | — | |
| EMANArchitecture Type=CNN, Architecture=ResNet-50, Param=26M2025.05 | 74 | — | — | |
| CowMixArch.=RN152, Param.=265M2022.02 | 73.9 | — | — | |
| MPLArchitecture=ResNet-50, Params=24M, Learning Paradigm=Semi-supervised2021.11 | 73.9 | — | — | |
| CowMixArchitecture=ResNet-152, Params=60M, Learning Paradigm=Semi-supervised2021.11 | 73.9 | — | — | |
| MPLArchitecture Type=CNN, Architecture=ResNet-50, Param=26M2025.05 | 73.9 | — | — | |
| CoMatchPre-training=MoCo v2 [9], Method=CoMatch [20], Epochs=1200, Params (train / test)=30.0M / 25.6M2023.08 | 73.7 | 91.4 | — | |
| CoMatchPre-training=None, Method=CoMatch [20], Epochs=400, Params (train / test)=30.0M / 25.6M2023.08 | 73.6 | 91.6 | — | |
| CoMatchArchitecture Type=CNN, Architecture=ResNet-50, Param=26M2025.05 | 73.6 | — | — | |
| BYOLArchitecture=ResNet-50 (2x), Params=94M, Learning Paradigm=Self-supervised pretraining2021.11 | 73.5 | — | — | |
| S4LArchitecture=ResNet-50 (4x), Params=375M, Learning Paradigm=Semi-supervised2021.11 | 73.2 | — | — | |
| BYOL multi-taskBackbone=ResNet-50, Evaluation Protocol=Semi-supervised2026.02 | 72.5 | 91.3 | — | |
| ReLIC v2Backbone=ResNet-50, Evaluation Protocol=Semi-supervised2026.02 | 72.4 | 91.2 | — | |
| DINOArchitecture=ViT-S, Params=21M, Learning Paradigm=Self-supervised pretraining2021.11 | 72.2 | — | — | |
| WCLPre-training=WCL [44], Method=Fine-tune, Epochs=800, Params (train / test)=34.2M / 25.6M2023.08 | 72 | 91.2 | — | |
| SimCLRArchitecture=ResNet-50 (2×), Params=94M, Learning Paradigm=Self-supervised pretraining2021.11 | 71.7 | — | — | |
| FixMatchBackbone=ResNet-50, Evaluation protocol=Fine-tuning, Method category=Methods using label-propagation, Data augmentation=RandAugment2020.06 | 71.5 | 89.1 | — | |
| FixMatchArch.=RN50, Param.=24M2022.02 | 71.5 | — | — | |
| CPCArchitecture=ResNet-161, Params=305M, Learning Paradigm=Self-supervised pretraining2021.11 | 71.5 | — | — | |
| FixMatchArchitecture=ResNet-50, Params=24M, Learning Paradigm=Semi-supervised2021.11 | 71.5 | — | — | |
| FixMatchArchitecture Type=CNN, Architecture=ResNet-50, Param=26M2025.05 | 71.5 | — | — | |
| SimCLRv2Arch.=RN50, self-distillation=true2021.11 | 70.5 | — | — | |
| SimCLRv2Architecture Type=CNN, Architecture=ResNet-50, Param=26M2025.05 | 70.5 | — | — | |
| C-BYOLBackbone=ResNet-50, Evaluation Protocol=Semi-supervised2026.02 | 70.5 | 90 | — | |
| SwAVBackbone=ResNet-50, Evaluation protocol=Fine-tuning, Method category=Methods using self-supervision only2020.06 | 70.2 | 89.9 | — | |
| SwAVArch.=RN502021.11 | 70.2 | — | — | |
| SwAVArch.=RN50, Param.=24M2022.02 | 70.2 | — | — | |
| SwAVBackbone=ResNet-50, Pre-training=ImageNet-1K, Evaluation Protocol=Fine-tuned2023.04 | 70.2 | 89.9 | — | |
| SwAVPre-training=SwAV [6], Method=Fine-tune, Epochs=800, Params (train / test)=30.4M / 25.6M2023.08 | 70.2 | 89.9 | — | |
| SwAVBackbone=ResNet-50, Evaluation Protocol=Semi-supervised2026.02 | 70.2 | 89.9 | — | |
| NNCLRBackbone=ResNet-50, Evaluation Protocol=Semi-supervised2026.02 | 69.8 | 89.3 | — | |
| Barlow TwinsBackbone=ResNet-50, Pre-training=ImageNet-1K, Evaluation Protocol=Fine-tuned2023.04 | 69.7 | 89.3 | — | |
| Barlow TwinsBackbone=ResNet-50, Evaluation Protocol=Semi-supervised2026.02 | 69.7 | 89.3 | — | |
| VICRegBackbone=ResNet-50, Pre-training=ImageNet-1K, Evaluation Protocol=Fine-tuned2023.04 | 69.5 | 89.5 | — | |
| I-VNE+Backbone=ResNet-50, Pre-training=ImageNet-1K, Evaluation Protocol=Fine-tuned2023.04 | 69.1 | 89.9 | — | |
| Fine-tuningBackbone=MAE ViT-B, Evaluation Protocol=Fine-tuning (FT)2025.06 | 68.9 | — | — | |
| UDABackbone=ResNet-50, Evaluation protocol=Fine-tuning, Method category=Methods using label-propagation, Data augmentation=RandAugment2020.06 | 68.8 | 88.5 | — | |
| BYOLArch.=RN502021.11 | 68.8 | — | — | |
| BYOLArchitecture=ResNet-50, Params=24M, Learning Paradigm=Self-supervised pretraining2021.11 | 68.8 | — | — | |
| UDAArchitecture=ResNet-50, Params=24M, Learning Paradigm=Semi-supervised2021.11 | 68.8 | — | — | |
| BYOLBackbone=ResNet-50, Pre-training=ImageNet-1K, Evaluation Protocol=Fine-tuned2023.04 | 68.8 | 89 | — | |
| UDAArchitecture Type=CNN, Architecture=ResNet-50, Param=26M2025.05 | 68.8 | — | — | |
| BYOLBackbone=ResNet-50, Evaluation Protocol=Semi-supervised2026.02 | 68.8 | 89 | — | |
| SimCLR v2Pre-training=SimCLR v2 [8], Method=Fine-tune, Epochs=800, Params (train / test)=34.2M / 25.6M2023.08 | 68.4 | 89.2 | — | |
| SimCLRv2Arch.=RN50, self-distillation=false2021.11 | 68.1 | — | — | |
| MoCo v2 (Fine-tune)Pre-training=MoCo v2 [9], Method=Fine-tune, Epochs=800, Params (train / test)=30.0M / 25.6M2023.08 | 66.1 | 87.9 | — | |
| SimCLRBackbone=ResNet-50, Evaluation protocol=Fine-tuning, Method category=Methods using self-supervision only2020.06 | 65.6 | 87.8 | — | |
| SimCLRArch.=RN50, Param.=24M2022.02 | 65.6 | — | — | |
| SimCLRArchitecture=ResNet-50, Params=24M, Learning Paradigm=Self-supervised pretraining2021.11 | 65.6 | — | — | |
| SimCLRBackbone=ResNet-50, Pre-training=ImageNet-1K, Evaluation Protocol=Fine-tuned2023.04 | 65.6 | 87.8 | — | |
| SimCLRBackbone=ResNet-50, Evaluation Protocol=Semi-supervised2026.02 | 65.6 | 87.8 | — | |
| Efficient ProbingBackbone=MAE ViT-B, Evaluation Protocol=Efficient Probing (EP)2025.06 | 65.2 | — | 71.5 | |
| PIRLBackbone=ResNet-50, Evaluation protocol=Fine-tuning, Method category=Methods using self-supervision only2020.06 | 60.4 | 83.8 | — | |
| Supervised Baseline (Conv)Architecture=Conv, Params=13M, Learning Paradigm=Supervised2021.11 | 60.2 | — | — | |
| SupervisedBackbone=ResNet-50, Evaluation protocol=Fine-tuning2020.06 | 56.4 | 80.4 | — | |
| SupervisedBackbone=ResNet-50, Pre-training=ImageNet-1K, Evaluation Protocol=Fine-tuned2023.04 | 56.4 | 80.4 | — | |
| supervisedBackbone=ResNet-50, Evaluation Protocol=Semi-supervised2026.02 | 56.4 | 80.4 | — | |
| Linear ProbingBackbone=MAE ViT-B, Evaluation Protocol=Linear Probing (LP)2025.06 | 55.9 | — | — | |
| Supervised Baseline (ViT-S)Architecture=ViT-S, Params=23M, Learning Paradigm=Supervised2021.11 | 48.6 | — | — | |
| PCLBackbone=ResNet-50, Evaluation protocol=Fine-tuning, Method category=Methods using self-supervision only2020.06 | — | 86.2 | — |