GLPN fine-tuned on NYUv2

国际通用本地路径网络（GLPN）模型在NYUv2上进行了微调，用于单目深度估计。该模型由Kim等人在 Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth 论文中介绍，并在 this repository 中首次发布。

免责声明：发布GLPN模型的团队未为此模型编写模型卡，因此此模型卡是由Hugging Face团队编写的。

模型描述

GLPN使用SegFormer作为骨干网络，并在顶部添加了一个轻量级头部用于深度估计。

意图和限制

您可以使用原始模型进行单目深度估计。查看 model hub 以查找您感兴趣的任务的微调版本。

如何使用

以下是如何使用此模型：

from transformers import GLPNFeatureExtractor, 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)

feature_extractor = GLPNFeatureExtractor.from_pretrained("vinvino02/glpn-nyu")
model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu")

# prepare image for the model
inputs = feature_extractor(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)

有关更多代码示例，请参阅 documentation 。

BibTeX条目和引文信息

@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}
}