README.md

<div align="center">
<h1>Depth Anything V2</h1>

[**Lihe Yang**](https://liheyoung.github.io/)<sup>1</sup> · [**Bingyi Kang**](https://bingykang.github.io/)<sup>2&dagger;</sup> · [**Zilong Huang**](http://speedinghzl.github.io/)<sup>2</sup>
<br>
[**Zhen Zhao**](http://zhaozhen.me/) · [**Xiaogang Xu**](https://xiaogang00.github.io/) · [**Jiashi Feng**](https://sites.google.com/site/jshfeng/)<sup>2</sup> · [**Hengshuang Zhao**](https://hszhao.github.io/)<sup>1*</sup>

<sup>1</sup>HKU&emsp;&emsp;&emsp;<sup>2</sup>TikTok
<br>
&dagger;project lead&emsp;*corresponding author

<a href="https://arxiv.org/abs/2406.09414"><img src='https://img.shields.io/badge/arXiv-Depth Anything V2-red' alt='Paper PDF'></a>
<a href='https://depth-anything-v2.github.io'><img src='https://img.shields.io/badge/Project_Page-Depth Anything V2-green' alt='Project Page'></a>
<a href='https://huggingface.co/spaces/depth-anything/Depth-Anything-V2'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
<a href='https://huggingface.co/datasets/depth-anything/DA-2K'><img src='https://img.shields.io/badge/Benchmark-DA--2K-yellow' alt='Benchmark'></a>
</div>

This work presents Depth Anything V2. It significantly outperforms [V1](https://github.com/LiheYoung/Depth-Anything) in fine-grained details and robustness. Compared with SD-based models, it enjoys faster inference speed, fewer parameters, and higher depth accuracy.

![teaser](assets/teaser.png)

## News

- **2024-06-14:** Paper, project page, code, models, demo, and benchmark are all released.


## Pre-trained Models

We provide **four models** of varying scales for robust relative depth estimation:

| Model | Params | Checkpoint |
|:-|-:|:-:|
| Depth-Anything-V2-Small | 24.8M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Small/resolve/main/depth_anything_v2_vits.pth?download=true) |
| Depth-Anything-V2-Base | 97.5M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Base/resolve/main/depth_anything_v2_vitb.pth?download=true) |
| Depth-Anything-V2-Large | 335.3M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Large/resolve/main/depth_anything_v2_vitl.pth?download=true) |
| Depth-Anything-V2-Giant | 1.3B | Coming soon |


### Code snippet to use our models
```python
import cv2
import torch

from depth_anything_v2.dpt import DepthAnythingV2

# take depth-anything-v2-large as an example
model = DepthAnythingV2(encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024])
model.load_state_dict(torch.load('checkpoints/depth_anything_v2_vitl.pth', map_location='cpu'))
model.eval()

raw_img = cv2.imread('your/image/path')
depth = model.infer_image(raw_img) # HxW raw depth map
```

## Usage

### Installation

```bash
git clone https://github.com/DepthAnything/Depth-Anything-V2
cd Depth-Anything-V2
pip install -r requirements.txt
```

### Running

```bash
python run.py --encoder <vits | vitb | vitl | vitg> --img-path <path> --outdir <outdir> [--input-size <size>] [--pred-only] [--grayscale]
```
Options:
- `--img-path`: You can either 1) point it to an image directory storing all interested images, 2) point it to a single image, or 3) point it to a text file storing all image paths.
- `--input-size` (optional): By default, we use input size `518` for model inference. **You can increase the size for even more fine-grained results.**
- `--pred-only` (optional): Only save the predicted depth map, without raw image.
- `--grayscale` (optional): Save the grayscale depth map, without applying color palette.

For example:
```bash
python run.py --encoder vitl --img-path assets/examples --outdir depth_vis
```

**If you want to use Depth Anything V2 on videos:**

```bash
python run_video.py --encoder vitl --video-path assets/examples_video --outdir video_depth_vis
```

*Please note that our larger model has better temporal consistency on videos.*


### Gradio demo

To use our gradio demo locally:

```bash
python app.py
```

You can also try our [online demo](https://huggingface.co/spaces/Depth-Anything/Depth-Anything-V2).

**Note:** Compared to V1, we have made a minor modification to the DINOv2-DPT architecture (originating from this [issue](https://github.com/LiheYoung/Depth-Anything/issues/81)). In V1, we *unintentionally* used features from the last four layers of DINOv2 for decoding. In V2, we use [intermediate features](https://github.com/DepthAnything/Depth-Anything-V2/blob/2cbc36a8ce2cec41d38ee51153f112e87c8e42d8/depth_anything_v2/dpt.py#L164-L169) instead. Although this modification did not improve details or accuracy, we decided to follow this common practice.



## Fine-tuned to Metric Depth Estimation

Please refer to [metric depth estimation](./metric_depth).


## DA-2K Evaluation Benchmark

Please refer to [DA-2K benchmark](./DA-2K.md).

## LICENSE

Depth-Anything-V2-Small model is under the Apache-2.0 license. Depth-Anything-V2-Base/Large/Giant models are under the CC-BY-NC-4.0 license.


## Citation

If you find this project useful, please consider citing:

```bibtex
@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}
}
```