Update README.md

README.md

@@ -1,3 +1,64 @@
----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+
+language:
+- en
+pipeline_tag: depth-estimation
+tags:
+- depth
+- relative depth
+---
+
+# Depth-Anything-V2-Large
+
+## Introduction
+Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model with the following features:
+- more fine-grained details than Depth Anything V1
+- more robust than Depth Anything V1 and SD-based models (e.g., Marigold, Geowizard)
+- more efficient (10x faster) and more lightweight than SD-based models
+- impressive fine-tuned performance with our pre-trained models
+
+## Installation
+
+```bash
+git clone https://huggingface.co/spaces/depth-anything/Depth-Anything-V2
+cd Depth-Anything-V2
+pip install -r requirements.txt
+```
+
+## Usage
+
+Download the [model](https://huggingface.co/depth-anything/Depth-Anything-V2-Large/resolve/main/depth_anything_v2_vitl.pth?download=true) first and put it under the `checkpoints` directory.
+
+```python
+import cv2
+import torch
+
+from depth_anything_v2.dpt import DepthAnythingV2
+
+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
+```
+
+## 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}
+}