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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....
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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.
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1 fonteVerificado 25 de ago.
Artefatos de modelo
2 artefatosTrechos de fonte
2 trechosGLPN uses SegFormer as backbone and adds a lightweight head on top for depth estimation.
model image
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.
Here is how to use this model:
from transformers import GLPNImageProcessor, GLPNForDepthEstimation
import torch
import numpy as np
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
processor = GLPNImageProcessor.from_pretrained("vinvino02/glpn-nyu")
model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu")
# prepare image for the model
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
predicted_depth = outputs.predicted_depth
# interpolate to original size
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=image.size[::-1],
mode="bicubic",
align_corners=False,
)
# visualize the prediction
output = prediction.squeeze().cpu().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)
For more code examples, we refer to the documentation.
@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}
}
--- license: apache-2.0 tags: - vision - depth-estimation widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # 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](https://arxiv.org/abs/2201.07436) by Kim et al. and first released in [this repository](https://github.com/vinvino02/GLPDepth). 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.  ## Intended uses & limitations You can use the raw model for monocular depth estimation. See the [model hub](https://huggingface.co/models?search=glpn) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python from transformers import GLPNImageProcessor, GLPNForDepthEstimation import torch import numpy as np from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = GLPNImageProcessor.from_pretrained("vinvino02/glpn-nyu") model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu") # prepare image for the model inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) predicted_depth = outputs.predicted_depth # interpolate to original size prediction = torch.nn.functional.interpolate( predicted_depth.unsqueeze(1), size=image.size[::-1], mode="bicubic", align_corners=False, ) # visualize the prediction output = prediction.squeeze().cpu().numpy() formatted = (output * 255 / np.max(output)).astype("uint8") depth = Image.fromarray(formatted) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs...
Source context: 2084 downloads · 25 likes · Pipeline depth-estimation · Library transformers · Repo vinvino02/glpn-nyu