Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

[**Lihe Yang**](https://liheyoung.github.io/)1 · [**Bingyi Kang**](https://scholar.google.com/citations?user=NmHgX-wAAAAJ)2+ · [**Zilong Huang**](http://speedinghzl.github.io/)2 · [**Xiaogang Xu**](https://xiaogang00.github.io/)3,4 · [**Jiashi Feng**](https://sites.google.com/site/jshfeng/)2 · [**Hengshuang Zhao**](https://hszhao.github.io/)1+ 1The University of Hong Kong · 2TikTok · 3Zhejiang Lab · 4Zhejiang University +corresponding authors **CVPR 2024** Paper PDF Project Page
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) ## News * **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 BEiTL-512 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?*
Click here for solutions - 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).
## 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 --img-path --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 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.
Code snippet (note the difference between our data pre-processing and that of MiDaS) ```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) ```
### 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 ```
Click here for a brief demo: ```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"] ```
## 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 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} } ```