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Datasets:
imageomics
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STRI-Samples

Tasks:
Image Classification
Modalities:
Image
Text
Formats:
imagefolder
Size:
< 1K
Tags:
butterfly
separated wings
dorsal
ventral
RGB
CV
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metadata
task_categories:
  - image-classification
tags:
  - butterfly
  - separated wings
  - dorsal
  - ventral
  - RGB
  - CV
pretty_name: Smithsonian Tropical Research Institute (STRI) Samples
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - metadata.csv
      - images/*.JPG
license: cc-by-4.0

Dataset Card for Smithsonian Tropical Research Institute (STRI) Samples

Dataset Description

Dataset Summary

Dorsal images of butterfly wings collected by Owen McMillan and members of his lab at the Smithsonian Tropical Research Institute.

Full dataset will be 24,119 RGB images: Dorsal and Ventral images of separated wings. This sample contains 207 dorsal butterfly images used as part of the training data for Imageomics/butterfly_detection_yolo.

Supported Tasks and Leaderboards

Note that this is just a subset of a larger dataset that has yet to be curated. It was used in combination with images from other sources to train the Imageomics/butterfly_detection_yolo model, and is not large or representative enough for use in training on its own yet.

Dataset Structure 

/dataset/
    <images>/
        <img_name_no_ext 1>.JPG
        <img_name_no_ext 2>.JPG
        ...
        <img_name_no_ext 207>.JPG
    metadata.csv

Data Instances

[More Information Needed]

  • Type: JPG

    Data Collection and Processing

    [More Information Needed]

    Photographs of the wings were taken using an Olympus OM-D EM-1 digital camera with an Olympus Zuiko Digital ED 60 mm f/2.8 macro lens.

    Who are the source data producers?

    [More Information Needed]

    The dataset is a collection of images taken of the McMilan Lab's collection of butterfly samples obtained from field collection surveys and several raised in captivity for genetic and other studies.

    Annotations

    There are several annotations in this data that are not standard and are very specific. Further annotation is needed. In general, taxonomic information down to the species and subspecies name is provided but is not always correct and needs double checking at this moment.

    Annotation process

    Damage annotations were completed by Michelle Ramirez for training the Imageomics/butterfly_detection_yolo model she created.

    Who are the annotators?

    [More Information Needed]

    Annotation data (other than the damage labels) was provided by the McMilan Lab.

    Personal and Sensitive Information

    [More Information Needed]

    Considerations for Using the Data

    [More Information Needed]

    Bias, Risks, and Limitations

    This is a small sample of minimally curated butterfly images intended only for wing segmentation and damage detection. It is not intended for species classification.

    Recommendations

    [More Information Needed]

    This is not a complete dataset, nor is it curated for greater use. The purpose of this dataset is to provide the 207 image sample used as part of a larger training set for the Imageomics/butterfly_detection_yolo model. More information on the full project can be found in the GitHub repository.

    Recommended use is only for segmentation.

    Additional Information

    Dataset Curators

    • Christopher Lawrence (Princeton University) - ORCID: 0000-0002-3846-5968
    • Owen McMillan (Smithsonian Tropical Research Institute) - ORCID: 0000-0003-2805-2745
    • Daniel Romero (Smithsonian Tropical Research Institute)
    • Carlos Arias (Smithsonian Tropical Research Institute) - ORCID: 0000-0002-1546-5156

    Licensing Information

    CC BY

    Citation Information

    Christopher Lawrence, Owen McMillan, Daniel Romero, Carlos Arias. (2024). Smithsonian Tropical Research Institute (STRI) Samples. Hugging Face. https://huggingface.co/datasets/imageomics/STRI-Samples.

    Acknowledgements

    This work was supported by US National Science Foundation (NSF) grant IOS 2110532, with additional support from the Imageomics Institute, which is funded by the NSF'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.