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---
tags:
- depth_anything
- depth-estimation
---
# Depth Anything model, large
The model card for our paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891).
You may also try our [demo](https://huggingface.co/spaces/LiheYoung/Depth-Anything) and visit our [project page](https://depth-anything.github.io/).
## Installation
First, install the Depth Anything package:
```
git clone https://github.com/LiheYoung/Depth-Anything
cd Depth-Anything
pip install -r requirements.txt
```
## Usage
Here's how to run the model:
```python
import numpy as np
from PIL import Image
import cv2
import torch
from depth_anything.dpt import DepthAnything
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
from torchvision.transforms import Compose
model = DepthAnything.from_pretrained("LiheYoung/depth_anything_vitl14")
transform = Compose([
Resize(
width=518,
height=518,
resize_target=False,
keep_aspect_ratio=True,
ensure_multiple_of=14,
resize_method='lower_bound',
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
])
image = Image.open("...")
image = np.array(image) / 255.0
image = transform({'image': image})['image']
image = torch.from_numpy(image).unsqueeze(0)
depth = model(image)
``` |