<div align="center">
<h2>Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data</h2>

[**Lihe Yang**](https://liheyoung.github.io/)<sup>1</sup> · [**Bingyi Kang**](https://scholar.google.com/citations?user=NmHgX-wAAAAJ)<sup>2&dagger;</sup> · [**Zilong Huang**](http://speedinghzl.github.io/)<sup>2</sup> · [**Xiaogang Xu**](https://xiaogang00.github.io/)<sup>3,4</sup> · [**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;&emsp;<sup>2</sup>TikTok&emsp;&emsp;&emsp;&emsp;<sup>3</sup>CUHK&emsp;&emsp;&emsp;&emsp;<sup>4</sup>ZJU

&dagger;project lead&emsp;*corresponding author

**CVPR 2024**

<a href="https://arxiv.org/abs/2401.10891"><img src='https://img.shields.io/badge/arXiv-Depth Anything-red' alt='Paper PDF'></a>
<a href='https://depth-anything.github.io'><img src='https://img.shields.io/badge/Project_Page-Depth Anything-green' alt='Project Page'></a>
<a href='https://huggingface.co/spaces/LiheYoung/Depth-Anything'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
<a href='https://huggingface.co/papers/2401.10891'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Paper-yellow'></a>
</div>

This work presents Depth Anything, a highly practical solution for robust monocular depth estimation by training on a combination of 1.5M labeled images and **62M+ unlabeled images**.

![teaser](assets/teaser.png)

<div align="center">
    <a href="https://github.com/DepthAnything/Depth-Anything-V2"><b>Try our latest Depth Anything V2 models!</b></a><br>
</div>

## News

* **2024-06-14:** [Depth Anything V2](https://github.com/DepthAnything/Depth-Anything-V2) is released.
* **2024-02-27:** Depth Anything is accepted by CVPR 2024.
* **2024-02-05:** [Depth Anything Gallery](./gallery.md) is released. Thank all the users!
* **2024-02-02:** Depth Anything serves as the default depth processor for [InstantID](https://github.com/InstantID/InstantID) and [InvokeAI](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.6.1).
* **2024-01-25:** Support [video depth visualization](./run_video.py). An [online demo for video](https://huggingface.co/spaces/JohanDL/Depth-Anything-Video) is also available.
* **2024-01-23:** The new ControlNet based on Depth Anything is integrated into [ControlNet WebUI](https://github.com/Mikubill/sd-webui-controlnet) and [ComfyUI's ControlNet](https://github.com/Fannovel16/comfyui_controlnet_aux).
* **2024-01-23:** Depth Anything [ONNX](https://github.com/fabio-sim/Depth-Anything-ONNX) and [TensorRT](https://github.com/spacewalk01/depth-anything-tensorrt) versions are supported.
* **2024-01-22:** Paper, project page, code, models, and demo ([HuggingFace](https://huggingface.co/spaces/LiheYoung/Depth-Anything), [OpenXLab](https://openxlab.org.cn/apps/detail/yyfan/depth_anything)) are released.


## Features of Depth Anything

***If you need other features, please first check [existing community supports](#community-support).***

- **Relative depth estimation**:
    
    Our foundation models listed [here](https://huggingface.co/spaces/LiheYoung/Depth-Anything/tree/main/checkpoints) can provide relative depth estimation for any given image robustly. Please refer [here](#running) for details.

- **Metric depth estimation**

    We fine-tune our Depth Anything model with metric depth information from NYUv2 or KITTI. It offers strong capabilities of both in-domain and zero-shot metric depth estimation. Please refer [here](./metric_depth) for details.


- **Better depth-conditioned ControlNet**

    We re-train **a better depth-conditioned ControlNet** based on Depth Anything. It offers more precise synthesis than the previous MiDaS-based ControlNet. Please refer [here](./controlnet/) for details. You can also use our new ControlNet based on Depth Anything in [ControlNet WebUI](https://github.com/Mikubill/sd-webui-controlnet) or [ComfyUI's ControlNet](https://github.com/Fannovel16/comfyui_controlnet_aux).

- **Downstream high-level scene understanding**

    The Depth Anything encoder can be fine-tuned to downstream high-level perception tasks, *e.g.*, semantic segmentation, 86.2 mIoU on Cityscapes and 59.4 mIoU on ADE20K. Please refer [here](./semseg/) for details.


## Performance

Here we compare our Depth Anything with the previously best MiDaS v3.1 BEiT<sub>L-512</sub> model.

Please note that the latest MiDaS is also trained on KITTI and NYUv2, while we do not.

| Method | Params | KITTI || NYUv2 || Sintel || DDAD || ETH3D || DIODE ||
|-|-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| | | AbsRel | $\delta_1$ | AbsRel | $\delta_1$ | AbsRel | $\delta_1$ | AbsRel | $\delta_1$ | AbsRel | $\delta_1$ | AbsRel | $\delta_1$ |
| MiDaS | 345.0M | 0.127 | 0.850 | 0.048 | *0.980* | 0.587 | 0.699 | 0.251 | 0.766 | 0.139 | 0.867 | 0.075 | 0.942 | 
| **Ours-S** | 24.8M | 0.080 | 0.936 | 0.053 | 0.972 | 0.464 | 0.739 | 0.247 | 0.768 | 0.127 | **0.885** | 0.076 | 0.939 |
| **Ours-B** | 97.5M | *0.080* | *0.939* | *0.046* | 0.979 | **0.432** | *0.756* | *0.232* | *0.786* | **0.126** | *0.884* | *0.069* | *0.946* |
| **Ours-L** | 335.3M | **0.076** | **0.947** | **0.043** | **0.981** | *0.458* | **0.760** | **0.230** | **0.789** | *0.127* | 0.882 | **0.066** | **0.952** |

We highlight the **best** and *second best* results in **bold** and *italic* respectively (**better results**: AbsRel $\downarrow$ , $\delta_1 \uparrow$).

## Pre-trained models

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

| Model | Params | Inference Time on V100 (ms) | A100 | RTX4090 ([TensorRT](https://github.com/spacewalk01/depth-anything-tensorrt)) |
|:-|-:|:-:|:-:|:-:|
| Depth-Anything-Small | 24.8M | 12 | 8 | 3 |
| Depth-Anything-Base | 97.5M | 13 | 9 | 6 |
| Depth-Anything-Large | 335.3M | 20 | 13 | 12 |

Note that the V100 and A100 inference time (*without TensorRT*) is computed by excluding the pre-processing and post-processing stages, whereas the last column RTX4090 (*with TensorRT*) is computed by including these two stages (please refer to [Depth-Anything-TensorRT](https://github.com/spacewalk01/depth-anything-tensorrt)).

You can easily load our pre-trained models by:
```python
from depth_anything.dpt import DepthAnything

encoder = 'vits' # can also be 'vitb' or 'vitl'
depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{:}14'.format(encoder))
```

Depth Anything is also supported in [``transformers``](https://github.com/huggingface/transformers). You can use it for depth prediction within [3 lines of code](https://huggingface.co/docs/transformers/main/model_doc/depth_anything) (credit to [@niels](https://huggingface.co/nielsr)).

### *No network connection, cannot load these models?*

<details>
<summary>Click here for solutions</summary>

- First, manually download the three checkpoints: [depth-anything-large](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitl14.pth), [depth-anything-base](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitb14.pth), and [depth-anything-small](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vits14.pth).

- Second, upload the folder containing the checkpoints to your remote server.

- Lastly, load the model locally:
```python
from depth_anything.dpt import DepthAnything

model_configs = {
    'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
    'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
    'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}
}

encoder = 'vitl' # or 'vitb', 'vits'
depth_anything = DepthAnything(model_configs[encoder])
depth_anything.load_state_dict(torch.load(f'./checkpoints/depth_anything_{encoder}14.pth'))
```
Note that in this locally loading manner, you also do not have to install the ``huggingface_hub`` package. In this way, please feel free to delete this [line](https://github.com/LiheYoung/Depth-Anything/blob/e7ef4b4b7a0afd8a05ce9564f04c1e5b68268516/depth_anything/dpt.py#L5) and the ``PyTorchModelHubMixin`` in this [line](https://github.com/LiheYoung/Depth-Anything/blob/e7ef4b4b7a0afd8a05ce9564f04c1e5b68268516/depth_anything/dpt.py#L169).
</details>


## Usage 

### Installation

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

### Running

```bash
python run.py --encoder <vits | vitb | vitl> --img-path <img-directory | single-img | txt-file> --outdir <outdir> [--pred-only] [--grayscale]
```
Arguments:
- ``--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.
- ``--pred-only`` is set to save the predicted depth map only. Without it, by default, we visualize both image and its depth map side by side.
- ``--grayscale`` is set to save the grayscale depth map. Without it, by default, we apply a color palette to the depth map.

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

**If you want to use Depth Anything on videos:**
```bash
python run_video.py --encoder vitl --video-path assets/examples_video --outdir video_depth_vis
```

### Gradio demo <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a> 

To use our gradio demo locally:

```bash
python app.py
```

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

### Import Depth Anything to your project

If you want to use Depth Anything in your own project, you can simply follow [``run.py``](run.py) to load our models and define data pre-processing. 

<details>
<summary>Code snippet (note the difference between our data pre-processing and that of MiDaS)</summary>

```python
from depth_anything.dpt import DepthAnything
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet

import cv2
import torch
from torchvision.transforms import Compose

encoder = 'vits' # can also be 'vitb' or 'vitl'
depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{:}14'.format(encoder)).eval()

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 = cv2.cvtColor(cv2.imread('your image path'), cv2.COLOR_BGR2RGB) / 255.0
image = transform({'image': image})['image']
image = torch.from_numpy(image).unsqueeze(0)

# depth shape: 1xHxW
depth = depth_anything(image)
```
</details>

### Do not want to define image pre-processing or download model definition files?

Easily use Depth Anything through [``transformers``](https://github.com/huggingface/transformers) within 3 lines of code! Please refer to [these instructions](https://huggingface.co/docs/transformers/main/model_doc/depth_anything) (credit to [@niels](https://huggingface.co/nielsr)).

**Note:** If you encounter ``KeyError: 'depth_anything'``, please install the latest [``transformers``](https://github.com/huggingface/transformers) from source:
```bash
pip install git+https://github.com/huggingface/transformers.git
```
<details>
<summary>Click here for a brief demo:</summary>

```python
from transformers import pipeline
from PIL import Image

image = Image.open('Your-image-path')
pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-small-hf")
depth = pipe(image)["depth"]
```
</details>

## Community Support

**We sincerely appreciate all the extensions built on our Depth Anything from the community. Thank you a lot!**

Here we list the extensions we have found:
- Depth Anything TensorRT: 
    - https://github.com/spacewalk01/depth-anything-tensorrt
    - https://github.com/thinvy/DepthAnythingTensorrtDeploy
    - https://github.com/daniel89710/trt-depth-anything
- Depth Anything ONNX: https://github.com/fabio-sim/Depth-Anything-ONNX
- Depth Anything in Transformers.js (3D visualization): https://huggingface.co/spaces/Xenova/depth-anything-web
- Depth Anything for video (online demo): https://huggingface.co/spaces/JohanDL/Depth-Anything-Video
- Depth Anything in ControlNet WebUI: https://github.com/Mikubill/sd-webui-controlnet
- Depth Anything in ComfyUI's ControlNet: https://github.com/Fannovel16/comfyui_controlnet_aux
- Depth Anything in X-AnyLabeling: https://github.com/CVHub520/X-AnyLabeling
- Depth Anything in OpenXLab: https://openxlab.org.cn/apps/detail/yyfan/depth_anything
- Depth Anything in OpenVINO: https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/280-depth-anything
- Depth Anything ROS:
    - https://github.com/scepter914/DepthAnything-ROS
    - https://github.com/polatztrk/depth_anything_ros
- Depth Anything Android:
    - https://github.com/FeiGeChuanShu/ncnn-android-depth_anything
    - https://github.com/shubham0204/Depth-Anything-Android
- Depth Anything in TouchDesigner: https://github.com/olegchomp/TDDepthAnything
- LearnOpenCV research article on Depth Anything: https://learnopencv.com/depth-anything
- Learn more about the DPT architecture we used: https://github.com/heyoeyo/muggled_dpt
- Depth Anything in NVIDIA Jetson Orin: https://github.com/ZhuYaoHui1998/jetson-examples/blob/main/reComputer/scripts/depth-anything


If you have your amazing projects supporting or improving (*e.g.*, speed) Depth Anything, please feel free to drop an issue. We will add them here.


## Acknowledgement

We would like to express our deepest gratitude to [AK(@_akhaliq)](https://twitter.com/_akhaliq) and the awesome HuggingFace team ([@niels](https://huggingface.co/nielsr), [@hysts](https://huggingface.co/hysts), and [@yuvraj](https://huggingface.co/ysharma)) for helping improve the online demo and build the HF models.

Besides, we thank the [MagicEdit](https://magic-edit.github.io/) team for providing some video examples for video depth estimation, and [Tiancheng Shen](https://scholar.google.com/citations?user=iRY1YVoAAAAJ) for evaluating the depth maps with MagicEdit.

## Citation

If you find this project useful, please consider citing:

```bibtex
@inproceedings{depthanything,
      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}
}
```
