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Models/Nimbled-MD2-R50

Nimbled-MD2-R50

Reported on 14 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 Vision7 results

Depth EstimationonKITTI Eigen split unsupervised
Delta < 1.25· 2024-08-26
0.904
best: 0.94 (SPIdepth)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
Depth EstimationonKITTI Eigen split unsupervised
Delta < 1.25^2· 2024-08-26
0.968
best: 0.973 (SPIdepth)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
Depth EstimationonKITTI Eigen split unsupervised
Delta < 1.25^3· 2024-08-26
0.985
best: 0.986 (Jasmine)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
Depth EstimationonKITTI Eigen split unsupervised
RMSE· 2024-08-26
4.377
best: 3.662 (SPIdepth)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
Depth EstimationonKITTI Eigen split unsupervised
RMSE log· 2024-08-26
0.172
best: 0.153 (SPIdepth)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
Depth EstimationonKITTI Eigen split unsupervised
Sq Rel· 2024-08-26
0.721
best: 0.785 (Dyna-DM)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
Depth EstimationonKITTI Eigen split unsupervised
absolute relative error· 2024-08-26
0.097
best: 0.071 (SPIdepth)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177

Methodology7 results

3DonKITTI Eigen split unsupervised
Delta < 1.25· 2024-08-26
0.904
best: 0.94 (SPIdepth)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
3DonKITTI Eigen split unsupervised
Delta < 1.25^2· 2024-08-26
0.968
best: 0.973 (SPIdepth)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
3DonKITTI Eigen split unsupervised
Delta < 1.25^3· 2024-08-26
0.985
best: 0.986 (Jasmine)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
3DonKITTI Eigen split unsupervised
RMSE· 2024-08-26
4.377
best: 3.662 (SPIdepth)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
3DonKITTI Eigen split unsupervised
RMSE log· 2024-08-26
0.172
best: 0.153 (SPIdepth)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
3DonKITTI Eigen split unsupervised
Sq Rel· 2024-08-26
0.721
best: 0.785 (Dyna-DM)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177
3DonKITTI Eigen split unsupervised
absolute relative error· 2024-08-26
0.097
best: 0.071 (SPIdepth)
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training arXiv:2408.14177