Update README.md

README.md

@@ -17,4 +17,48 @@ Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real un
 - more efficient (10x faster) and more lightweight than SD-based models
 - impressive fine-tuned performance with our pre-trained models
 
-For more details, please refer to our [project page](https://depth-anything-v2.github.io/), [github](https://github.com/DepthAnything/Depth-Anything-V2) and [paper](https://arxiv.org/abs/2406.09414).
+## 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-Small/resolve/main/depth_anything_v2_vits.pth?download=true) first and put it under the `checkpoints` directory.
+
+```python
+import cv2
+import torch
+
+from depth_anything_v2.dpt import DepthAnythingV2
+
+# take depth-anything-v2-large as an example
+model = DepthAnythingV2(encoder='vits', features=64, out_channels=[48, 96, 192, 384])
+model.load_state_dict(torch.load('checkpoints/depth_anything_v2_vits.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}
+}