🚀 具有DINOv2主干的DPT模型

本项目的DPT（密集预测变换器）模型采用DINOv2作为主干，为深度估计任务提供了强大的解决方案。它结合了DPT框架和DINOv2的优势，能有效进行深度估计。

🚀 快速开始

模型详情

具有DINOv2主干的DPT（密集预测变换器）模型，该模型由Oquab等人在论文 DINOv2: Learning Robust Visual Features without Supervision 中提出。

DPT架构。取自 原始论文。

资源

• DINOv2论文
• DPT论文

使用Transformers库调用模型

from transformers import AutoImageProcessor, DPTForDepthEstimation
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)

image_processor = AutoImageProcessor.from_pretrained("facebook/dpt-dinov2-giant-nyu")
model = DPTForDepthEstimation.from_pretrained("facebook/dpt-dinov2-giant-nyu")

# prepare image for the model
inputs = image_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)

📚 详细文档

模型用途

预期用途

该模型旨在展示使用以DINOv2为骨干的DPT框架可以产生强大的深度估计器。

BibTeX引用和引用信息

@misc{oquab2023dinov2,
      title={DINOv2: Learning Robust Visual Features without Supervision}, 
      author={Maxime Oquab and Timothée Darcet and Théo Moutakanni and Huy Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel Haziza and Francisco Massa and Alaaeldin El-Nouby and Mahmoud Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Hervé Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski},
      year={2023},
      eprint={2304.07193},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

📄 许可证

本项目采用Apache-2.0许可证。