51 lines
1.9 KiB
Markdown
51 lines
1.9 KiB
Markdown
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# DA-2K Evaluation Benchmark
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## Introduction
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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.
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Please refer to our [paper](https://arxiv.org/abs/2406.09414) for details in constructing this benchmark.
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## Usage
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Please first [download the benchmark](https://huggingface.co/datasets/depth-anything/DA-2K/tree/main).
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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:
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```
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{
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"image_path": [
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{
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"point1": [h1, w1], # (vertical position, horizontal position)
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"point2": [h2, w2], # (vertical position, horizontal position)
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"closer_point": "point1" # we always set "point1" as the closer one
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},
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...
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],
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...
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}
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```
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To visualize the annotations:
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```bash
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python visualize.py [--scene-type <type>]
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```
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**Options**
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- `--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.
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## Citation
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If you find this benchmark useful, please consider citing:
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```bibtex
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@article{depth_anything_v2,
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title={Depth Anything V2},
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author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
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journal={arXiv:2406.09414},
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year={2024}
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}
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```
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