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Models/MIM-Swin-V2

MIM-Swin-V2

Reported on 26 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.

Computer Vision13 results

Depth EstimationonNYU-Depth V2
Delta < 1.25· 2023-11-07
0.9361
best: 0.989 (UniK3D (FT, metric))
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonNYU-Depth V2
Delta < 1.25^2· 2023-11-07
0.9916
best: 1 (HybridDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonNYU-Depth V2
Delta < 1.25^3· 2023-11-07
0.9981
best: 1 (HybridDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonNYU-Depth V2
RMSE· 2023-11-07
0.3046
best: 0.013 (Defocus/DepthNet (Normalized))
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonNYU-Depth V2
absolute relative error· 2023-11-07
0.0864
best: 0.026 (HybridDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonNYU-Depth V2
log 10· 2023-11-07
0.0365
best: 0.059 (SC-DepthV2)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonKITTI Eigen split
Delta < 1.25· 2023-11-07
0.9757
best: 0.99 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonKITTI Eigen split
Delta < 1.25^2· 2023-11-07
0.9974
best: 0.999 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonKITTI Eigen split
Delta < 1.25^3· 2023-11-07
0.9994
best: 1 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonKITTI Eigen split
RMSE· 2023-11-07
2.0373
best: 1.394 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonKITTI Eigen split
RMSE log· 2023-11-07
0.077
best: 0.048 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonKITTI Eigen split
Sq Rel· 2023-11-07
0.1458
best: 0.224 (SfM-Revisited)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
Depth EstimationonKITTI Eigen split
absolute relative error· 2023-11-07
0.0508
best: 0.029 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938

Methodology13 results

3DonNYU-Depth V2
Delta < 1.25· 2023-11-07
0.9361
best: 0.989 (UniK3D (FT, metric))
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonNYU-Depth V2
Delta < 1.25^2· 2023-11-07
0.9916
best: 1 (HybridDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonNYU-Depth V2
Delta < 1.25^3· 2023-11-07
0.9981
best: 1 (HybridDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonNYU-Depth V2
RMSE· 2023-11-07
0.3046
best: 0.013 (Defocus/DepthNet (Normalized))
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonNYU-Depth V2
absolute relative error· 2023-11-07
0.0864
best: 0.026 (HybridDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonNYU-Depth V2
log 10· 2023-11-07
0.0365
best: 0.059 (SC-DepthV2)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonKITTI Eigen split
Delta < 1.25· 2023-11-07
0.9757
best: 0.99 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonKITTI Eigen split
Delta < 1.25^2· 2023-11-07
0.9974
best: 0.999 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonKITTI Eigen split
Delta < 1.25^3· 2023-11-07
0.9994
best: 1 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonKITTI Eigen split
RMSE· 2023-11-07
2.0373
best: 1.394 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonKITTI Eigen split
RMSE log· 2023-11-07
0.077
best: 0.048 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonKITTI Eigen split
Sq Rel· 2023-11-07
0.1458
best: 0.224 (SfM-Revisited)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938
3DonKITTI Eigen split
absolute relative error· 2023-11-07
0.0508
best: 0.029 (SPIDepth)
Analysis of NaN Divergence in Training Monocular Depth Estimation Model arXiv:2311.03938