lucataco / glpn-nyu

Global-Local Path Networks (GLPN) model trained on NYUv2 for Monocular Depth Estimation

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Input

Output

Examples

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Run time and cost

This model runs on CPU hardware. Predictions typically complete within 78 seconds. The predict time for this model varies significantly based on the inputs.

Readme

GLPN fine-tuned on NYUv2

Global-Local Path Networks (GLPN) model trained on NYUv2 for monocular depth estimation. It was introduced in the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Kim et al. and first released in this repository.

Disclaimer: The team releasing GLPN did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

GLPN uses SegFormer as backbone and adds a lightweight head on top for depth estimation.

model image

Intended uses & limitations

You can use the raw model for monocular depth estimation. See the model hub to look for fine-tuned versions on a task that interests you.

For more code examples, we refer to the documentation.

BibTeX entry and citation info

@article{DBLP:journals/corr/abs-2201-07436,
  author    = {Doyeon Kim and
               Woonghyun Ga and
               Pyunghwan Ahn and
               Donggyu Joo and
               Sehwan Chun and
               Junmo Kim},
  title     = {Global-Local Path Networks for Monocular Depth Estimation with Vertical
               CutDepth},
  journal   = {CoRR},
  volume    = {abs/2201.07436},
  year      = {2022},
  url       = {https://arxiv.org/abs/2201.07436},
  eprinttype = {arXiv},
  eprint    = {2201.07436},
  timestamp = {Fri, 21 Jan 2022 13:57:15 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2201-07436.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}