library_name: transformers
tags:
- depth
- absolute depth
pipeline_tag: depth-estimation
Depth Anything V2 (Fine-tuned for Metric Depth Estimation) - Transformers Version
This model represents a fine-tuned version of Depth Anything V2 for indoor metric depth estimation using the synthetic Hypersim datasets.
The model checkpoint is compatible with the transformers library.
Depth Anything V2 was introduced in the paper of the same name by Lihe Yang et al. It uses the same architecture as the original Depth Anything release but employs synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. This fine-tuned version for metric depth estimation was first released in this repository.
Six metric depth models of three scales for indoor and outdoor scenes, respectively, were released and are available:
| Base Model | Params | Indoor (Hypersim) | Outdoor (Virtual KITTI 2) |
|---|---|---|---|
| Depth-Anything-V2-Small | 24.8M | Model Card | Model Card |
| Depth-Anything-V2-Base | 97.5M | Model Card | Model Card |
| Depth-Anything-V2-Large | 335.3M | Model Card | Model Card |
Model description
Depth Anything V2 leverages the DPT architecture with a DINOv2 backbone.
The model is trained on ~600K synthetic labeled images and ~62 million real unlabeled 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.
Requirements
transformers>=4.45.0
Alternatively, use transformers latest version installed from the source:
pip install git+https://github.com/huggingface/transformers
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="depth-anything/Depth-Anything-V2-Metric-Indoor-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, you can use the model and processor classes:
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("depth-anything/Depth-Anything-V2-Metric-Indoor-Base-hf")
model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Metric-Indoor-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, please refer to the documentation.
Citation
@article{depth_anything_v2,
title={Depth Anything V2},
author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
journal={arXiv:2406.09414},
year={2024}
}
@inproceedings{depth_anything_v1,
title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data},
author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
booktitle={CVPR},
year={2024}
}