depth anything small hf EU AI Act Compliance Profile

Depth Anything (small-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.

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_anything_overview.jpg" alt="drawing" width="600"/>

<small> Depth Anything overview. Taken from the <a href="https://arxiv.org/abs/2401.10891">original paper</a>.</small>

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-small-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, AutoModelForDepthEstima

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Tags

Library