Datasets:
license: pddl
language:
- en
pretty_name: LILA BC Camera Trap Data
tags:
- biology
- image
- animals
- CV
- camera traps
size_categories:
- 1M<n<10M
Dataset Card for LILA BC Camera Trap Data
Dataset Description
- Homepage:
- Repository: [related project repo]
- Paper:
- Leaderboard:
- Point of Contact:
Dataset Summary
This dataset contains the LILA BC full camera trap information with notebook exploring available data. Images have been deduplicated and reduced down to species designation, with the main CSV filtered to just those with species labels and only one animal per image. This was done by pulling the first instance of an animal so that there are not repeat images of the same animal from essentially the same time.
The deduplicated collection (lila_image_urls_and_labels_species.csv) has 6,365,985 images (compared to the full dataset of 16,833,848 at time of download).
See the LILA BC HF Dataset for more inforamtion and updated data.
This was potentially to use for testingi BioCLIP, but data had been processed elsewhere.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
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Dataset Structure
Data Instances
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Data Fields
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Data Splits
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Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
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Personal and Sensitive Information
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Considerations for Using the Data
Social Impact of Dataset
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Discussion of Biases
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Other Known Limitations
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Additional Information
Dataset Curators
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Licensing Information
[More Information Needed]
Be sure to check the license requirements for the particular data used (as noted in the LILA BC Licensing Information Section). This particular compilation has been marked as dedicated to the public domain by applying the CC0 Public Domain Waiver. However, images may be licensed under different terms (as noted above).
Citation Information
[More Information Needed]
Contributions
The Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.