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DA-2K.md

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-# DA-2K Evaluation Benchmark
-
-## Introduction
-
-![DA-2K](assets/DA-2K.png)
-
-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://arxiv.org/abs/2406.09414) for details in constructing this benchmark.
-
-
-## Usage
-
-Please first [download the benchmark](https://huggingface.co/datasets/depth-anything/DA-2K/tree/main).
-
-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:
-
-```
-{
-  "image_path": [
-    {
-      "point1": [h1, w1], # (vertical position, horizontal position)
-      "point2": [h2, w2], # (vertical position, horizontal position)
-      "closer_point": "point1" # we always set "point1" as the closer one
-    },
-    ...
-  ],
-  ...
-}
-```
-
-To visualize the annotations:
-```bash
-python visualize.py [--scene-type <type>]
-```
-
-**Options**
-- `--scene-type <type>` (optional): Specify the scene type (`indoor`, `outdoor`, `non_real`, `transparent_reflective`, `adverse_style`, `aerial`, `underwater`, and `object`). Skip this argument or set <type> as `""` to include all scene types.
-
-## Citation
-
-If you find this benchmark 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}
-}
-```