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.gitignore
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*.ttf
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scripts/results/
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scripts/collage*
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*.ttf
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scripts/results/
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scripts/collage*
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vre_dronescapes/
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scripts/dev.ipynb
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README.md
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# Dronescapes dataset
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As introduced in our ICCV 2023 workshop paper: [link](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf)
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# 1. Downloading the data
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## Option 1. Download the pre-processed dataset from HuggingFace repository
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## 2. Using the data
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As per the split from the paper:
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The data is in `data/*` (see the `ls` call above, it should match even if you download from huggingface).
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## 2.1 Using the provided viewer
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The simplest way to explore the data is to use the [provided notebook](scripts/dronescapes_viewer.ipynb). Upon running
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it, you should get a collage with all the default tasks, like
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For a CLI-only method, you can use the provided reader as well:
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# Dronescapes dataset
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As introduced in our ICCV 2023 workshop paper: [link](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf)
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# 1. Downloading the data
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## Option 1. Download the pre-processed dataset from HuggingFace repository
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## 2. Using the data
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As per the split from the paper:
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<details>
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<summary> Split </summary>
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<img src="split.png">
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</details>
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The data is in `data/*` (see the `ls` call above, it should match even if you download from huggingface).
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## 2.1 Using the provided viewer
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The simplest way to explore the data is to use the [provided notebook](scripts/dronescapes_viewer.ipynb). Upon running
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it, you should get a collage with all the default tasks, like the picture at the top.
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For a CLI-only method, you can use the provided reader as well:
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