license: apache-2.0
tags:
- vision
pipeline_tag: depth-estimation
widget:
- inference: false
Depth Anything (large-sized model, Transformers version)
Depth Anything model. It was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al. and first released in this repository.
Online demo is also provided.
Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
Depth Anything leverages the DPT architecture with a DINOv2 backbone.
The model is trained on ~62 million images, obtaining state-of-the-art results for both relative and absolute depth estimation.
Depth Anything overview. Taken from the original paper.
Intended uses & limitations
You can use the raw model for tasks like zero-shot depth estimation. See the model hub to look for other versions on a task that interests you.
How to use
Here is how to use this model to perform zero-shot depth estimation:
from transformers import pipeline
from PIL import Image
import requests
# load pipe
pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-large-hf")
# load image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
# inference
depth = pipe(image)["depth"]
Alternatively, one can use the classes themselves:
from transformers import AutoImageProcessor, AutoModelForDepthEstimation
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("LiheYoung/depth-anything-large-hf")
model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-large-hf")
# 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,
)
For more code examples, we refer to the documentation.
BibTeX entry and citation info
@misc{yang2024depth,
title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data},
author={Lihe Yang and Bingyi Kang and Zilong Huang and Xiaogang Xu and Jiashi Feng and Hengshuang Zhao},
year={2024},
eprint={2401.10891},
archivePrefix={arXiv},
primaryClass={cs.CV}
}