A single photograph does not contain depth. A monocular model predicts it anyway, and the
| images | 654 — the complete official NYU v2 test split |
| ground truth | measured depth, uint16 millimetres |
| evaluation range | 0.01–10.0 m, the NYU convention |
| raw model output | disparity — **determined empirically**, Spearman +0.9052 against ground-truth disparity (from the run log), not assumed |
Depth Anything V2 is a relative depth model — affine-invariant *in disparity*, meaning it recovers depth ordering up to an unknown scale and shift. The protocol most write-ups reach for, median-scaling in depth space, fits only the scale and leaves the shift unmodelled.
Both columns below score the same predictions:
δ<1.25 moves from 46.1% to 91.1% on identical predictions. Reported under the wrong protocol, this model looks roughly half as good as it is, and the number would read as a fact about the model rather than about the measurement.
The fitted disparity transform is reported rather than hidden, because it is the thing a single image cannot determine: scale 0.0848 (sd 0.03078), shift 0.25174 (sd 0.09518).
That shift standard deviation, 0.09518, happens to equal abs_rel to five decimals. It is a coincidence, not a copied field: the run computes and prints the fit statistics on a separate path from the metrics, and the log reports them independently.
Code and full results on GitHub
Every number is read from the file the run produced. github.com/hammasbuilds · kaggle.com/muhammadhammas13