Initial commit

DA-2K.md

@@ -6,14 +6,14 @@
 
 DA-2K is proposed in [Depth Anything V2](https://depth-anything-v2.github.io) to evaluate the relative depth estimation capability. It encompasses eight representative scenarios of `indoor`, `outdoor`, `non_real`, `transparent_reflective`, `adverse_style`, `aerial`, `underwater`, and `object`. It consists of 1K diverse high-quality images and 2K precise pair-wise relative depth annotations.
 
-Please refer to our [paper](https://depth-anything-v2.github.io) for details in constructing this benchmark.
+Please refer to our [paper](https://arxiv.org/abs/2406.09414) for details in constructing this benchmark.
 
 
 ## Usage
 
 Please first [download the benchmark]().
 
-All annotations are stored in [annotations.json](./annotations.json). The annotation file is a JSON object where each key is the path to an image file, and the value is a list of annotations associated with that image. Each annotation describes two points and identifies which point is closer to the camera. The structure is detailed below:
+All annotations are stored in `annotations.json`. The annotation file is a JSON object where each key is the path to an image file, and the value is a list of annotations associated with that image. Each annotation describes two points and identifies which point is closer to the camera. The structure is detailed below:
 
 ```
 {
@@ -45,7 +45,7 @@ If you find this benchmark useful, please consider citing:
 @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 preprint arXiv:},
+  journal={arXiv:2406.09414},
   year={2024}
 }
 ```

README.md

@@ -9,13 +9,13 @@
 <br>
 &dagger;project lead&emsp;*corresponding author
 
-<a href=""><img src='https://img.shields.io/badge/arXiv-Depth Anything V2-red' alt='Paper PDF'></a>
+<a href="https://arxiv.org/abs/2406.09414"><img src='https://img.shields.io/badge/arXiv-Depth Anything V2-red' alt='Paper PDF'></a>
 <a href='https://depth-anything-v2.github.io'><img src='https://img.shields.io/badge/Project_Page-Depth Anything V2-green' alt='Project Page'></a>
 <a href='https://huggingface.co/spaces/depth-anything/Depth-Anything-V2'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
 <a href='https://huggingface.co/datasets/depth-anything/DA-2K'><img src='https://img.shields.io/badge/Benchmark-DA--2K-green' alt='Benchmark'></a>
 </div>
 
-This work presents Depth Anything V2. Compared with V1, this version produces significantly more fine-grained and robust depth predictions. Compared with SD-based models, it is much more efficient and lightweight.
+This work presents Depth Anything V2. It significantly outperforms V1 in fine-grained details and robustness. Compared with SD-based models, it enjoys faster inference speed, fewer parameters, and higher depth accuracy.
 
 ![teaser](assets/teaser.png)
 
@@ -49,7 +49,7 @@ model.load_state_dict(torch.load('checkpoints/depth_anything_v2_vitg.pth', map_l
 model.eval()
 
 raw_img = cv2.imread('your/image/path')
-depth = model.infer_img(raw_img) # HxW raw depth map
+depth = model.infer_image(raw_img) # HxW raw depth map
 ```
 
 ## Usage
@@ -97,7 +97,7 @@ python app.py
 
 You can also try our [online demo](https://huggingface.co/spaces/Depth-Anything/Depth-Anything-V2).
 
-**Note:** Compared to V1, we have made a minor modification to the DINOv2-DPT architecture (originating from this [issue](https://github.com/LiheYoung/Depth-Anything/issues/81)). In V1, we *unintentionally* used features from the last four layers of DINOv2 for decoding. In V2, we use intermediate features instead. Although this modification did not improve details or accuracy, we decided to follow this common practice.
+**Note:** Compared to V1, we have made a minor modification to the DINOv2-DPT architecture (originating from this [issue](https://github.com/LiheYoung/Depth-Anything/issues/81)). In V1, we *unintentionally* used features from the last four layers of DINOv2 for decoding. In V2, we use [intermediate features](https://github.com/DepthAnything/Depth-Anything-V2/blob/2cbc36a8ce2cec41d38ee51153f112e87c8e42d8/depth_anything_v2/dpt.py#L164-L169) instead. Although this modification did not improve details or accuracy, we decided to follow this common practice.
 
 
 
@@ -110,6 +110,10 @@ Please refer to [metric depth estimation](./metric_depth).
 
 Please refer to [DA-2K benchmark](./DA-2K.md).
 
+## LICENSE
+
+Depth-Anything-V2-Small model is under the Apache-2.0 license. Depth-Anything-V2-Base/Large/Giant are under the CC-BY-NC-4.0 license.
+
 
 ## Citation
 
@@ -119,7 +123,7 @@ If you find this project useful, please consider citing:
 @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 preprint arXiv:},
+  journal={arXiv:2406.09414},
   year={2024}
 }
 ```

app.py

@@ -38,7 +38,7 @@ model = model.to(DEVICE).eval()
 
 title = "# Depth Anything V2"
 description = """Official demo for **Depth Anything V2**.
-Please refer to our [paper](), [project page](https://depth-anything-v2.github.io), or [github](https://github.com/DepthAnything/Depth-Anything-V2) for more details."""
+Please refer to our [paper](https://arxiv.org/abs/2406.09414), [project page](https://depth-anything-v2.github.io), or [github](https://github.com/DepthAnything/Depth-Anything-V2) for more details."""
 
 def predict_depth(image):
     return model.infer_image(image)

metric_depth/README.md

@@ -49,7 +49,7 @@ If you find this project useful, please consider citing:
 @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 preprint arXiv:},
+  journal={arXiv:2406.09414},
   year={2024}
 }
 ```