Semi-supervised Image Classification on ImageNet 1k (val)
78.5Top-5 Acc (1%)SwAV
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| SwAVarchitecture=ResNet-50-MLP, #pretrain epochs=800, batch size=4096, multi-crop augmentation=true2020.05 | 78.5 | 89.9 | — | — | |
| BYOLarchitecture=ResNet-50-MLPbig, #pretrain epochs=1000, batch size=40962020.05 | 78.4 | 89 | — | — | |
| SimCLRarchitecture=ResNet-50-MLP, #pretrain epochs=1000, batch size=40962020.05 | 75.5 | 87.8 | — | — | |
| PCLarchitecture=ResNet-50, #pretrain epochs=2002020.05 | 75.3 | 85.6 | — | — | |
| PCL v2architecture=ResNet-50-MLP, #pretrain epochs=2002020.05 | 73.9 | 85 | — | — | |
| MoCo v2architecture=ResNet-50-MLP, #pretrain epochs=2002020.05 | 66.3 | 84.4 | — | — | |
| PIRLarchitecture=ResNet-50, #pretrain epochs=8002020.05 | 57.2 | 83.8 | — | — | |
| MoCoarchitecture=ResNet-50, #pretrain epochs=2002020.05 | 56.9 | 83 | — | — | |
| SimCLRarchitecture=ResNet-50-MLP, #pretrain epochs=2002020.05 | 56.5 | 82.7 | — | — | |
| S^4L Rotationarchitecture=ResNet-50v2, #pretrain epochs=-2020.05 | 53.4 | 83.8 | — | — | |
| Pseudolabelsarchitecture=ResNet-50v2, #pretrain epochs=-2020.05 | 51.6 | 82.4 | — | — | |
| Supervised baselinearchitecture=ResNet-50, #pretrain epochs=-2020.05 | 48.4 | 80.4 | — | — | |
| VAT + Entropy Min.architecture=ResNet-50v2, #pretrain epochs=-2020.05 | 47 | 83.4 | — | — | |
| Jigsawarchitecture=ResNet-50, #pretrain epochs=902020.05 | 45.3 | 79.3 | — | — | |
| Instance Discriminationarchitecture=ResNet-50, #pretrain epochs=2002020.05 | 39.2 | 77.4 | — | — | |
| Randomarchitecture=ResNet-50, #pretrain epochs=-2020.05 | 22 | 59 | — | — | |
| CoMatchLabeled data ratio=10%2024.10 | — | — | 73.6 | 91.6 | |
| SimMatchLabeled data ratio=10%2024.10 | — | — | 74.1 | 91.5 | |
| UniMatch V2Labeled data ratio=10%2024.10 | — | — | 74.3 | 91.7 |