Image Classification Top-1 Accuracy on CIFAR100 (test)
93.4Top-1 AccuracyWiSE-FT
Evaluation Results
| Method | Links | |
|---|---|---|
| WiSE-FTalpha=optimal2021.09 | 93.4 | |
| WiSE-FTalpha=0.52021.09 | 93.3 | |
| TResNet-LPre-training Dataset=ImageNet-21K-P2021.04 | 92.5 | |
| Standard fine-tuning2021.09 | 92.2 | |
| TResNet-MPre-training Dataset=ImageNet-21K-P2021.04 | 91.7 | |
| OFA595Pre-training Dataset=ImageNet-21K-P2021.04 | 90.3 | |
| ResNet50Pre-training Dataset=ImageNet-21K-P2021.04 | 90.3 | |
| TResNet-LPre-training Dataset=ImageNet-1K2021.04 | 90.2 | |
| TResNet-MPre-training Dataset=ImageNet-1K2021.04 | 89.5 | |
| MobileNetV3Pre-training Dataset=ImageNet-21K-P2021.04 | 88.5 | |
| OFA595Pre-training Dataset=ImageNet-1K2021.04 | 88.3 | |
| ResNet50Pre-training Dataset=ImageNet-1K2021.04 | 86.8 | |
| MobileNetV3Pre-training Dataset=ImageNet-1K2021.04 | 86.7 | |
| Supervised-INEvaluation protocol=Fine-tuned, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 86.4 | |
| BYOLEvaluation protocol=Fine-tuned, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 86.1 | |
| SimCLREvaluation protocol=Fine-tuned, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 85.9 | |
| ReLICv2Evaluation protocol=Fine-tuned, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 85.3 | |
| CDARTSParam (M)=3.9 ± 0.08, Latency (ms)=41.2 ± 0.5, Search Cost (GPU days)=0.3, Search Method=gradient, Search space=DARTS search space2020.06 | 84.31 | |
| PDARTSParam (M)=3.4, Latency (ms)=40.9, Search Cost (GPU days)=0.3, Search Method=gradient, Search space=DARTS search space2020.06 | 83.45 | |
| DenseNet-BCParam (M)=25.6, Search Method=manual2020.06 | 82.82 | |
| Wide ResNetParam (M)=36.5, Search Method=manual2020.06 | 82.7 | |
| ResNeXt-29, 16x64dParam (M)=68.1, Search Method=manual2020.06 | 82.69 | |
| DARTSV2Latency (ms)=3.3, Search Cost (GPU days)=4.0, Search Method=gradient2020.06 | 82.46 | |
| DARTSV1Param (M)=3.3, Search Cost (GPU days)=1.5, Search Method=gradient2020.06 | 82.24 | |
| GDASParam (M)=3.4, Latency (ms)=30.6, Search Cost (GPU days)=4, Search Method=gradient2020.06 | 81.62 | |
| NAONetParam (M)=10.6, Search Cost (GPU days)=200, Search Method=NAO2020.06 | 81.33 | |
| SAMNetwork=ResNet101, Optimizer=SAM, Cutout=true, Batch size=128, Epochs=2502022.03 | 80.82 | |
| CCSSL (FixMatch)Number of Labeled Samples=100002022.03 | 80.68 | |
| FLSDModel=WideResNet2025.03 | 80.44 | |
| RMSGDNetwork=ResNet101, Optimizer=RMSGD, Cutout=true, Batch size=128, Epochs=2502022.03 | 80.36 | |
| DFLModel=WideResNet2025.03 | 80.35 | |
| BSCE-GRAModel=WideResNet2025.03 | 80.28 | |
| Random InitEvaluation protocol=Fine-tuned, Backbone=ResNet-50, Pre-trained=None2022.01 | 80.2 | |
| RMSGDNetwork=ResNet50, Optimizer=RMSGD, Cutout=true, Batch size=128, Epochs=2502022.03 | 80.06 | |
| SGDPNetwork=ResNet101, Optimizer=SGDP, Cutout=true, Batch size=128, Epochs=2502022.03 | 80.03 | |
| BSCEModel=WideResNet2025.03 | 79.96 | |
| SAMNetwork=ResNet34, Optimizer=SAM, Cutout=true, Batch size=128, Epochs=2502022.03 | 79.85 | |
| RMSGDNetwork=ResNet34, Optimizer=RMSGD, Cutout=true, Batch size=128, Epochs=2502022.03 | 79.7 | |
| SGDPNetwork=ResNet34, Optimizer=SGDP, Cutout=true, Batch size=128, Epochs=2502022.03 | 79.67 | |
| RMSGDNetwork=ResNet-50, Cutout=No2022.03 | 79.59 | |
| SGDPNetwork=ResNet50, Optimizer=SGDP, Cutout=true, Batch size=128, Epochs=2502022.03 | 79.52 | |
| CEModel=WideResNet2025.03 | 79.51 | |
| RMSGDNetwork=ResNet-101, Cutout=No2022.03 | 79.36 | |
| SGDNetwork=ResNet101, Optimizer=SGD, Cutout=true, Batch size=128, Epochs=2502022.03 | 79.35 | |
| RMSGDNetwork=ResNet-34, Cutout=No2022.03 | 79.32 | |
| SAMNetwork=ResNet50, Optimizer=SAM, Cutout=true, Batch size=128, Epochs=2502022.03 | 79.26 | |
| CoMatchNumber of Labeled Samples=100002022.03 | 79.14 | |
| MMCEModel=WideResNet2025.03 | 79.14 | |
| SSWPLNumber of Labeled Samples=100002022.03 | 79.12 | |
| ResNet-34 vanillaBackbone=ResNet-34, Model DoF=21M2021.07 | 79.1 | |
| R34-vanillaDoF=21M2021.07 | 79.1 | |
| NNCLREvaluation protocol=Linear evaluation, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 79 | |
| ResNet-34 RPGBackbone=ResNet-34, Model DoF=11M2021.07 | 78.9 | |
| R34-RPGDoF=11M2021.07 | 78.9 | |
| SAMNetwork=SENet18, Optimizer=SAM, Cutout=true, Batch size=128, Epochs=2502022.03 | 78.85 | |
| SGDPNetwork=ResNet18, Optimizer=SGDP, Cutout=true, Batch size=128, Epochs=2502022.03 | 78.82 | |
| SAMNetwork=ResNet18, Optimizer=SAM, Cutout=true, Batch size=128, Epochs=2502022.03 | 78.77 | |
| SGDPNetwork=ResNet-34, Cutout=No2022.03 | 78.74 | |
| BLModel=WideResNet2025.03 | 78.72 | |
| SGDNetwork=ResNet34, Optimizer=SGD, Cutout=true, Batch size=128, Epochs=2502022.03 | 78.63 | |
| RMSGDNetwork=ResNet-18, Cutout=No2022.03 | 78.63 | |
| SGDPNetwork=ResNet-101, Cutout=No2022.03 | 78.6 | |
| RMSGDNetwork=ResNet18, Optimizer=RMSGD, Cutout=true, Batch size=128, Epochs=2502022.03 | 78.53 | |
| R34-RPG.blkDoF=11M2021.07 | 78.5 | |
| SEALGeneration=Gen=Last (Student), Backbone=ResNet-182023.04 | 78.5 | |
| SGDNetwork=ResNet-101, Cutout=No2022.03 | 78.48 | |
| SGDPNetwork=ResNet-50, Cutout=No2022.03 | 78.44 | |
| R34-LegoDoF=11M2021.07 | 78.4 | |
| BYOLEvaluation protocol=Linear evaluation, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 78.4 | |
| SAMNetwork=ResNet-101, Cutout=No2022.03 | 78.38 | |
| SGDNetwork=ResNet50, Optimizer=SGD, Cutout=true, Batch size=128, Epochs=2502022.03 | 78.36 | |
| Supervised-INEvaluation protocol=Linear evaluation, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 78.3 | |
| ReLICv2Evaluation protocol=Linear evaluation, Backbone=ResNet-50, Pre-trained=ImageNet2022.01 | 78.2 | |
| SGDNetwork=ResNet18, Optimizer=SGD, Cutout=true, Batch size=128, Epochs=2502022.03 | 78.16 | |
| SGDPNetwork=ResNet-18, Cutout=No2022.03 | 78.13 | |
| SGDNetwork=ResNet-50, Cutout=No2022.03 | 78.12 | |
| RMSGDNetwork=ResNeXt, Optimizer=RMSGD, Cutout=true, Batch size=128, Epochs=2502022.03 | 78.06 | |
| Teacher (Scratch)Architecture=ResNet34/ResNet182022.09 | 78.05 | |
| SAMNetwork=ResNet-34, Cutout=No2022.03 | 77.98 | |
| SGDNetwork=ResNet-34, Cutout=No2022.03 | 77.88 | |
| SGDNetwork=SENet18, Optimizer=SGD, Cutout=true, Batch size=128, Epochs=2502022.03 | 77.8 | |
| SGDNetwork=ResNet-18, Cutout=No2022.03 | 77.8 | |
| RMSGDNetwork=SENet18, Optimizer=RMSGD, Cutout=true, Batch size=128, Epochs=2502022.03 | 77.77 | |
| FLSDModel=ResNet1102025.03 | 77.77 | |
| AdamPNetwork=ResNet-101, Cutout=No2022.03 | 77.71 | |
| SGDPNetwork=SENet18, Optimizer=SGDP, Cutout=true, Batch size=128, Epochs=2502022.03 | 77.7 | |
| FLSDModel=ResNet502025.03 | 77.69 | |
| ResNet-18 RPGBackbone=ResNet-18, Model DoF=5.5M2021.07 | 77.6 | |
| ResNet-18 vanillaBackbone=ResNet-18, Model DoF=11M2021.07 | 77.6 | |
| R18-vanillaDoF=11M2021.07 | 77.5 | |
| MMCEModel=ResNet502025.03 | 77.49 | |
| AdamPNetwork=ResNet-50, Cutout=No2022.03 | 77.47 | |
| CEModel=ResNet1102025.03 | 77.44 | |
| MMCEModel=ResNet1102025.03 | 77.42 | |
| SAMNetwork=ResNeXt, Optimizer=SAM, Cutout=true, Batch size=128, Epochs=2502022.03 | 77.41 | |
| FixMatchNumber of Labeled Samples=100002022.03 | 77.4 | |
| SAMNetwork=ResNet-50, Cutout=No2022.03 | 77.39 | |
| float32Model=ResNet18, Bit-width=float32, Model Size=43 MB2021.03 | 77.38 | |
| Pham et al.Generation=Gen=Last (Student), Backbone=ResNet-182023.04 | 77.32 | |
| MADArchitecture=ResNet34/ResNet182022.09 | 77.31 |