Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation
| Date | Stars |
|---|---|
| 2026-07-31 | 8170 |
| 2026-08-02 | 8170 |
| 2026-08-05 | 8175 |
| 2026-08-06 | 8175 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<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†</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    <sup>2</sup>TikTok    <sup>3</sup>CUHK    <sup>4</sup>ZJU
†project lead *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**.
<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 ControlNetExcerpt of 14,816 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cf86c1e3f9f32a60, desc:depth estimation
