Lightly ImageNet1k Benchmarks
Note: Evaluation settings are based on these papers:
See the benchmarking scripts for details.
| Model | Backbone | Batch Size | Epochs | Linear Top1 | Finetune Top1 | kNN Top1 | Tensorboard | Checkpoint |
|---|---|---|---|---|---|---|---|---|
| BarlowTwins | Res50 | 256 | 100 | 62.9 | 72.6 | 45.6 | link | link |
| BYOL | Res50 | 256 | 100 | 62.5 | 74.5 | 46.0 | link | link |
| DINO | Res50 | 128 | 100 | 68.2 | 72.5 | 49.9 | link | link |
| MAE | ViT-B/16 | 256 | 100 | 46.0 | 81.3 | 11.2 | link | link |
| MoCoV2 | Res50 | 256 | 100 | 61.5 | 74.3 | 41.8 | link | link |
| SimCLR* | Res50 | 256 | 100 | 63.2 | 73.9 | 44.8 | link | link |
| SimCLR* + DCL | Res50 | 256 | 100 | 65.1 | 73.5 | 49.6 | link | link |
| SimCLR* + DCLW | Res50 | 256 | 100 | 64.5 | 73.2 | 48.5 | link | link |
| SwAV | Res50 | 256 | 100 | 67.2 | 75.4 | 49.5 | link | link |
| TiCo | Res50 | 256 | 100 | 49.7 | 72.7 | 26.6 | link | link |
| VICReg | Res50 | 256 | 100 | 63.0 | 73.7 | 46.3 | link | link |
*We use square root learning rate scaling instead of linear scaling as it yields better results for smaller batch sizes. See Appendix B.1 in the SimCLR paper.