Feature request
We currently have 2 monocular depth estimation models in the library, namely DPT and GLPN.
It would be great to have a pipeline for this task, with the following API:
from transformers import pipeline
pipe = pipeline("depth-estimation")
pipe("cats.png")
This pipeline could default to the https://huggingface.co/Intel/dpt-large checkpoint. Also check out the Space that showcases the model.
This can be implemented similar to other pipelines. For an example PR that added a pipeline, see #11598.
Motivation
Pipelines are a great way to quickly perform inference with a model for a given task, abstracting away all the complexity.
Your contribution
I can assist with this, together with @Narsil.
Feature request
We currently have 2 monocular depth estimation models in the library, namely DPT and GLPN.
It would be great to have a pipeline for this task, with the following API:
This pipeline could default to the https://huggingface.co/Intel/dpt-large checkpoint. Also check out the Space that showcases the model.
This can be implemented similar to other pipelines. For an example PR that added a pipeline, see #11598.
Motivation
Pipelines are a great way to quickly perform inference with a model for a given task, abstracting away all the complexity.
Your contribution
I can assist with this, together with @Narsil.