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in1k-benchmark-logs

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Lightly ImageNet1k Benchmarks

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Note: Evaluation settings are based on these papers:

Linear: SimCLR
Finetune: SimCLR
KNN: InstDisc

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.

lightly-ai/in1k-benchmark-logs

Lightly ImageNet1k Benchmarks

lightly-ai/
in1k-benchmark-logs