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Image Classification on 13 Transfer Learning Datasets (val)

48.6Aircraft AccuracyDINO + DILEMMA
14.997623.721332.44541.1687Apr 10, 2022

Evaluation results

MethodLinks
DINO + DILEMMABackbone=ViT-S/16, Evaluation Protocol=Linear probing, Training Duration=same as baseline2022.0448.689.6650.8593.3978.8568.395.3875.4853.7186.5497.4171.476474.82
DINO + DILEMMABackbone=ViT-S/16, Evaluation Protocol=Linear probing2022.0446.9589.5849.0492.4677.2267.595.274.952.5185.9197.0670.0162.8773.93
DINO + SparsityBackbone=ViT-S/16, Evaluation Protocol=Linear probing, Sparsity=True2022.0446.8389.1648.4592.477768.494.774.9751.3285.4596.8566.6561.973.4
MoCoV3 + DILEMMABackbone=ViT-S/16, Evaluation Protocol=Linear probing, Training Duration=same as baseline2022.0446.0290.2943.4494.280.0565.3794.4774.144.1388.5396.7566.364.972.97
DINOBackbone=ViT-S/16, Evaluation Protocol=Linear probing2022.0445.6688.347.0790.6174.0666.2294.547347.3685.8396.6253.7459.970.99
MoCoV3 + SparsityBackbone=ViT-S/16, Evaluation Protocol=Linear probing, Sparsity=True, Training Duration=same as baseline2022.0444.6489.8940.2193.5378.8865.4894.2172.9642.8488.9396.964.5263.2472.02
MoCoV3 + DILEMMABackbone=ViT-S/16, Evaluation Protocol=Linear probing2022.0444.4389.5542.3293.0377.5664.4793.4171.9443.7685.8896.0865.4163.7671.66
MoCoV3 + SparsityBackbone=ViT-S/16, Evaluation Protocol=Linear probing, Sparsity=True2022.0443.2989.2540.1492.2877.365.0593.2571.3942.3886.3596.0964.662.2971.05
MoCoV3Backbone=ViT-S/16, Evaluation Protocol=Linear probing, Positional Embeddings=True2022.0438.787.3528.7291.9775.0964.6391.6767.9733.384.6395.7664.5656.4167.75
MoCoV3Backbone=ViT-S/16, Evaluation Protocol=Linear probing, Positional Embeddings=False2022.0416.2960.795.8831.3613.9650.5960.5131.6418.1253.3470.6120.0215.2534.49