depth anything base hf

ProviderLiheYoung
Categorydepth-estimation
Licenseapache-2.0
Downloads48.2K
Stars0

Overview

Depth Anything is a robust monocular depth estimation model designed for high-generalization performance across diverse environments. Unlike models trained on narrow datasets, this base version leverages large-scale unlabeled data to provide consistent relative depth maps from a single RGB image. For developers, this means a reliable tool for adding spatial awareness to computer vision pipelines without requiring stereo camera hardware. It is particularly effective for background blurring, 3D scene reconstruction, and enhancing autonomous navigation systems. Integration is straightforward via the Hugging Face ecosystem, offering a lightweight alternative to more computationally expensive depth sensors while maintaining competitive accuracy across varied indoor and outdoor scenes.

Highlights

  • High-generalization monocular depth estimation
  • Zero-shot performance across diverse scenes
  • Seamless integration via Hugging Face
  • Apache-2.0 license for commercial use
  • Efficient relative depth map generation

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("LiheYoung/depth-anything-base-hf")
tokenizer = AutoTokenizer.from_pretrained("LiheYoung/depth-anything-base-hf")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download LiheYoung/depth-anything-base-hf

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download LiheYoung/depth-anything-base-hf config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('LiheYoung/depth-anything-base-hf')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/LiheYoung/depth-anything-base-hf

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/LiheYoung/depth-anything-base-hf

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('LiheYoung/depth-anything-base-hf')
tokenizer = AutoTokenizer.from_pretrained('LiheYoung/depth-anything-base-hf')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
tags:

  • vision

pipeline_tag: depth-estimation
widget:
  • inference: false

---

Depth Anything (base-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:

python
from transformers import pipeline
from PIL import Image
import requests

load pipe

pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-base-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:

python
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-base-hf")
model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-base-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

bibtex
@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}
}
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