Dpt Dinov2 Base Kitti

🚀 具有DINOv2主幹的DPT模型

本項目基於DPT（Dense Prediction Transformer）模型，採用DINOv2作為主幹網絡，能夠實現強大的深度估計功能。該模型的提出參考了相關研究論文，為視覺領域的深度估計任務提供了有效的解決方案。

🚀 快速開始

模型詳情

DPT（密集預測變換器）模型採用DINOv2作為主幹網絡，此模型由Oquab等人在論文 DINOv2: Learning Robust Visual Features without Supervision 中提出。

DPT架構。取自 原論文。

資源鏈接

• DINOv2論文
• DPT論文

使用Transformers庫調用模型

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-base-kitti")
model = DPTForDepthEstimation.from_pretrained("facebook/dpt-dinov2-base-kitti")

# 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)

📚 模型使用

預期用途

該模型旨在展示使用DPT框架並以DINOv2作為主幹網絡可以得到一個強大的深度估計器。

BibTeX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}
}

📄 許可證

本項目採用Apache-2.0許可證。