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Models/OrdinalEntropy

OrdinalEntropy

Reported on 12 benchmarks across 3 tasks · 1 paper

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

Computer Vision8 results

Depth EstimationonNYU-Depth V2
Delta < 1.25· 2023-01-21
0.932
best: 0.989 (UniK3D (FT, metric))
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915
Depth EstimationonNYU-Depth V2
RMSE· 2023-01-21
0.321
best: 0.013 (Defocus/DepthNet (Normalized))
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915
Depth EstimationonNYU-Depth V2
absolute relative error· 2023-01-21
0.089
best: 0.026 (HybridDepth)
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915
Depth EstimationonNYU-Depth V2
log 10· 2023-01-21
0.039
best: 0.059 (SC-DepthV2)
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915
CrowdsonShanghaiTech B
MAE· 2023-01-21
9.1
best: 5.51 (EBC-ZIP-B)
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915
CrowdsonShanghaiTech B
MSE· 2023-01-21
14.5
best: 10.06 (M-SFANet+M-SegNet)
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915
CrowdsonShanghaiTech A
MAE· 2023-01-21
65.6
best: 47.81 (EBC-ZIP-B)
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915
CrowdsonShanghaiTech A
MSE· 2023-01-21
105
best: 76.7 (APGCC)
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915

Methodology4 results

3DonNYU-Depth V2
Delta < 1.25· 2023-01-21
0.932
best: 0.989 (UniK3D (FT, metric))
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915
3DonNYU-Depth V2
RMSE· 2023-01-21
0.321
best: 0.013 (Defocus/DepthNet (Normalized))
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915
3DonNYU-Depth V2
absolute relative error· 2023-01-21
0.089
best: 0.026 (HybridDepth)
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915
3DonNYU-Depth V2
log 10· 2023-01-21
0.039
best: 0.059 (SC-DepthV2)
Improving Deep Regression with Ordinal Entropy arXiv:2301.08915