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turhancan97/visbench-surface_normal-dinov2_vits14
visbench-surface_normal-dinov2_vits14 is a machine learning model from turhancan97. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for visbench.
A trained probe head, not a backbone. It is the small module VisBench fits on top of frozen dinov2vits14 features to measure what those features carry.
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Updated Aug 7, 2026
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From the Hugging Face model README
dinov2_vits14A trained probe head, not a backbone. It is the small module VisBench fits
on top of frozen dinov2_vits14 features to measure what those features carry.
import visbench
from visbench.hub import load_probe_from_hub
backbone = visbench.get_backbone("dinov2_vits14")
probe = load_probe_from_hub("turhancan97/visbench-surface_normal-dinov2_vits14", backbone=backbone)
These weights were fitted on features from dinov2_vits14, taken with
pooling=mean and feature_mode=dense_only. Loading them against
anything else is refused, because the failure is otherwise silent: a head fitted
on one pooling and fed another has the right shapes and produces a plausible,
wrong number.
| backbone | dinov2_vits14 |
| backbone key | dinov2/dinov2_vits14/224/7764ea0f912e |
| task | surface_normal (mid_level) |
| pooling | mean (requested mean) |
| feature mode | dense_only |
| layers | None |
Reported scores
| metric | value |
|---|---|
d1 | 0.2185 |
d2 | 0.4841 |
d3 | 0.6107 |
mean | 29.4827 |
median | 23.8327 |
rmse | 36.4513 |
Fitted with:
batch_size: 8epochs: 10head: linearhidden_dim: 512layers: Nonelr: 0.0005normal_source: Noneoptimizer: adamwprotocol: probe3duncertainty_aware: Truewarmup_epochs: 1.5weight_decay: 0.0001Generated by VisBench.