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--- |
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description: "Pre-processed data for training and evaluating the CLIBD model." |
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--- |
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# Processed datasets for CLIBD |
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Pre-processed data for **[CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale](https://arxiv.org/abs/2405.17537)**. |
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More information: [github.com/bioscan-ml/clibd](https://github.com/bioscan-ml/clibd). |
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### Dataset Sources |
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The data we used to train and test our models include [BIOSCAN-1M](https://huggingface.co/datasets/bioscan-ml/BIOSCAN-1M) and [BIOSCAN-5M](https://huggingface.co/datasets/bioscan-ml/BIOSCAN-1M). In addition, to perform Bayesian Zero Shot Learning, we also employed the INSECT dataset that was introduced in [Fine-grained ZSL with DNA as Side Information](https://github.com/sbadirli/Fine-Grained-ZSL-with-DNA). |
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### Download the datasets |
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To clone the repo: |
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``` |
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GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/bioscan-ml/bioscan-clibd |
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``` |
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For BIOSCAN-1M: |
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``` |
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git lfs pull --include="data/BIOSCAN_1M/**" |
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python merge_bioscan_1m.py |
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``` |
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For BIOSCAN-5M: |
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``` |
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git lfs pull --include="data/BIOSCAN_5M/**" |
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python merge_bioscan_5m.py |
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``` |
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For INSECT: |
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``` |
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git lfs pull --include="data/INSECT/**" |
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``` |
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## Acknowledgement |
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We would like to express our gratitude for the use of the [INSECT](https://indiana-my.sharepoint.com/personal/sbadirli_iu_edu/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fsbadirli%5Fiu%5Fedu%2FDocuments%2FDesktop%2FInsect%20ZSL%20Project%2FNIPS%20Insect%20Data&ga=1) dataset, which played a pivotal role in the completion of our experiments. Additionally, we acknowledge the use and modification of code from the [Fine-Grained-ZSL-with-DNA repository](https://github.com/sbadirli/Fine-Grained-ZSL-with-DNA), which facilitated part of our experimental work. The contributions of these resources have been invaluable to our project, and we appreciate the efforts of all developers and researchers involved. |
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This research was supported by the Government of Canada’s New Frontiers in Research Fund (NFRF) [NFRFT-2020-00073], Canada CIFAR AI Chair grants, and the Pioneer Centre for AI (DNRF grant number P1). This research was also enabled in part by support provided by the Digital Research Alliance of Canada (alliancecan.ca). |
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For more information, please check our [paper](https://arxiv.org/abs/2405.17537). |
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