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Remote Sensing Image Classification 벤치마크

Remote Sensing Image Classification on FireRisk

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Accuracy (%)

63.2 63.72 64.25 64.77 65.29 2023-03 2026-09 ResNet-50 — 63.2 (2023-03-13) ViT-B/16 — 63.31 (2023-03-13) DINO (ViT-B/16) — 63.36 (2023-03-13) MAE (ViT-B/16) — 65.29 (2023-03-13) ResNet-50 — 63.2 (2023-03-13) ViT-B/16 — 63.31 (2023-03-13) DINO (ViT-B/16) — 63.36 (2023-03-13) MAE (ViT-B/16) — 65.29 (2023-03-13) ResNet-50 — 63.2 (2023-03-13) ViT-B/16 — 63.31 (2023-03-13) DINO (ViT-B/16) — 63.36 (2023-03-13) MAE (ViT-B/16) — 65.29 (2023-03-13)
RankModel Accuracy (%) PaperCodeYear
1 ResNet-50 63.20 FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning charmonyshen/firerisk 2023
2 ViT-B/16 63.31 FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning charmonyshen/firerisk 2023
3 DINO (ViT-B/16) 63.36 FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning charmonyshen/firerisk 2023
4 MAE (ViT-B/16) 65.29 FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning charmonyshen/firerisk 2023
5 ResNet-50 63.20 FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning charmonyshen/firerisk 2023
6 ViT-B/16 63.31 FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning charmonyshen/firerisk 2023
7 DINO (ViT-B/16) 63.36 FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning charmonyshen/firerisk 2023
8 MAE (ViT-B/16) 65.29 FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning charmonyshen/firerisk 2023
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