DINO (ViT-B/16)

Reported on 2 benchmarks across 2 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology1 result

Remote SensingonFireRisk
Accuracy (%)· 2023-03-13
63.36
best: 65.29 (MAE (ViT-B/16))
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning arXiv:2303.07035

Computer Code1 result

Remote Sensing Image ClassificationonFireRisk
Accuracy (%)· 2023-03-13
63.36
best: 65.29 (MAE (ViT-B/16))
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning arXiv:2303.07035