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---
license: cc0-1.0
task_categories:
- image-classification
tags:
- butterfly
- heliconius erato
- heliconius melpomene
- full body
- dorsal
- RGB
- bird view
- butterfly view
- bird acuity
- butterfly acuity
- imbalanced
- mimicry
- cv
pretty_name: Curated Gold Standard Cuthill Dataset
size_categories:
- n<1K
---
# Dataset Card for Curated Gold Standard Cuthill Dataset
## Dataset Description
Dorsal full body images of subspecies of _Heliconius erato_ and _Heliconius melpomene_ (18 subspecies total).
960 images with 320 specimens, each photographed in RGB with bird and butterfly acuity included (3 images of each specimen).
Subset of images processed from Cuthill et al. dataset available at [doi:10.5061/dryad.2hp1978](https://doi.org/10.5061/dryad.2hp1978).
**Note:** `dorsal_images_cuthill` contains processed dorsal images from the original Cuthill dataset (all 1,234 specimens).
- **Homepage:**
- **Repository:** [Butterfly-mimicry](https://github.com/Imageomics/Butterfly-mimicry) contains research done using this dataset.
- **Paper:** [Imageomics Approach to Understanding Visual Biological Trait Similarities using Butterfly Mimicry as a Model System](http://rave.ohiolink.edu/etdc/view?acc_num=osu168198420667979)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
![Distribution Summary](https://huggingface.co/datasets/imageomics/Curated_GoldStandard_Hoyal_Cuthill/resolve/main/Curated_GS_Cuthill_Distribution_Snapshot.png)
<!---
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
--->
### Supported Tasks and Leaderboards
_Heliconius erato_ and _Heliconius melpomene_ subspecies identification (image classification), with variable settings for acuity of the observer (bird, butterfly, or human/other).
### Languages
English
## Dataset Structure
```
|-- dorsal_images_cuthill
| |
| |-- 10427965_D_lowres.tif
| |
| |-- 10427966_D_lowres.tif
| |
| | ...
|
|-- Acuities
| |
| |-- train_bird
| | |
| | |-- erato_cyrbia
| | |
| | |-- erato_etylus
| | |
| | | ...
| |
| |-- test_bird
| | |
| | |-- erato_cyrbia
| | |
| | |-- erato_etylus
| | |
| | | ...
| |
| |-- train_butterfly
| | |
| | |-- erato_cyrbia
| | |
| | |-- erato_etylus
| | |
| | | ...
| |
| |-- test_butterfly
| |
| |-- erato_cyrbia
| |
| |-- erato_etylus
| |
| | ...
|
|-- train
| |
| |-- erato_cyrbia
| |
| |-- erato_etylus
| |
| | ...
|
|-- test
|
|-- erato_cyrbia
|
|-- erato_etylus
|
| ...
```
### Data Instances
* Type: PNG
* Size: 128px x 128px
* Background: [210, 210, 210] (gray)
* Fit in frame: Most padding is above and below the image, some on the left and right.
* Ruler or Scale: None
* Color Reference (ColorChecker, white-balance, None): None
### Data Fields
**In `Hoyal_Cuthill_GoldStandard_metadata_cleaned.csv`:**
* `NHM_Specimen`: Natural History Museum Specimen number
* `Image_filename`: filename of image of specimen
* `View`: whether ventral or dorsal view of specimen (all dorsal)
* `Species`: species of specimen (melpomene or erato)
* `Subspecies`: subspecies of the specimen
* `Sex`: sex of the specimen (male or female)
* `addit_taxa_info`: additional taxonomic information (subspecies)
* `type_stat`: indicates "classical" or "example" specimen of species or subspecies ('ST', 'PT', or 'HT', indicating syntypes, paratypes, or holotypes, respectively). This field is mostly null.
* `hybrid_stat`: hybrid status ('valid subspecies', 'subspecies synonym' or 'unknown' (only 1))
* `in_reduced`: whether or not the specimen was used in the second analysis by Cuthill et al. (1 or 0 to indicate yes or no, respectively). This was an effort to remove potential hybrids from their analysis; it does not always match our indication of hybrid status.
* `locality`: where specimen was collected
* `lat`: latitude where specimen was collected
* `lon`: longitude where specimen was collected
* `speciesdesig`: species designation, first initial of species '.' subspecies (eg., 'm. rosina')
`Train_Test_Curated_GoldStandard_Hoyal_Cuthill.csv` has three additional columns:
* `Image_filename_png`: filename of (png) image of specimen, `dorsal_images_cuthill/ + <Image_filename_png>` is the filepath for the processed dorsal image
* `subset`: whether this is part of the training or test set (`train` or `test`)
* `filepath`: the filepath for the train or test image
`Acuity_Curated_GoldStandard_Hoyal_Cuthill.csv` also has `Image_filename_png` and `subset`, but `subset` references the bird and butterfly acuity training and test sets. Additionally, it has columns:
* `bird_filepath`: the filepath for the bird acuity version of the image
* `butterfly_filepath`: the filepath for the butterfly acuity version of the image
### Data Splits
There are 250 images in each training set and 70 in each test set.
RGB training images are in `train` folder and testing are in `test`.
For bird and butterfly acuities, their respective training and test images are in the `train_bird` (`train_butterfly`) and `test_bird` (`test_butterfly`) folders.
All of these folders are further subdivided by the subspecies. Filepaths to access these images are provided in `Train_Test_Curated_GoldStandard_Hoyal_Cuthill.csv` and `Acuity_Curated_GoldStandard_Hoyal_Cuthill.csv`, respectively.
## Dataset Creation
Processing steps included:
1. Hybrid Separation
2. Label Correction
3. Removal of subspecies with no mimic pairs
4. Make background uniform across all images
5. Make image square via padding
### Curation Rationale
This dataset was curated for training a model to classify different species of Heliconius Butterflies and to take into account mimicry between species and acuity of the observer (bird, butterfly, or human/other).
The original data (Cuthill et al. 2019) had misclassified species/subspecies and some locality/ collection sites were outside the known range of the butterflies.
It also contained hybrid and aberrant samples, that had the potential to muddle classification results.
To prevent this, the data was further refined by several Heliconius experts to remove hybrid and aberrant samples.
Lastly, bird and butterfly acuities were added to provide another level of analysis.
### Source Data
Cuthill et al. [doi:10.5061/dryad.2hp1978](https://doi.org/10.5061/dryad.2hp1978).
#### Initial Data Collection and Normalization
Photographers: Robyn Crowther and Sophie Ledger, Natural History Museum, London.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The original data has some misclassified species/subspecies, and had multiple hybrid samples. These samples were removed by hand by Owen McMilan, Christopher Lawrence, Jim Mallet, Krzysztof Kozak.
Some localities were outside the known range of the butterflies, and were removed using QGIS and known subspecies ranges.
#### Who are the annotators?
Christopher Lawrence,
Jim Mallet,
Owen McMilan, and
Krzysztof Kozak.
### Personal and Sensitive Information
None
## Considerations for Using the Data
### Social Impact of Dataset
N/A
### Discussion of Biases
Biased towards species and subspecies within Heliconius. Focused on _Heliconius erato_ and _Heliconius melpomene_.
### Other Known Limitations
* No genetic data available.
* Non-uniform distribution of subspecies (imbalanced).
## Additional Information
### Dataset Curators
* Krzysztof Kozak (University of California Berkeley) - ORCID: 0000-0001-8980-3173
* Christopher Lawrence (Princeton University) - ORCID: 0000-0002-3846-5968
* James Mallet (Harvard University) - ORCID: 0000-0002-3370-0367
* Owen McMillan (Smithsonian Tropical Research Institute) - ORCID: 0000-0003-2805-2745
* David Carlyn (The Ohio State University) - ORCID: 0000-0002-8323-0359
* Mohannad Elhamod (Virginia Tech) - ORCID: 0000-0002-2383-947X
### Licensing Information
This work has been marked as dedicated to the public domain by applying the [CC0 Public Domain Waiver](https://creativecommons.org/publicdomain/zero/1.0/).
### Citation Information
Krzysztof Kozak, Christopher Lawrence, James Mallet, Owen McMillan, David Carlyn, Mohannad Elhamod. (2023), "Curated GoldStandard Hoyal Cuthill", https://huggingface.co/datasets/imageomics/Curated_GoldStandard_Hoyal_Cuthill.
Ramesh Babu, R. (2023). _Imageomics Approach to Understanding Visual Biological Trait Similarities using Butterfly Mimicry as a Model System_ [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu168198420667979
Please also cite the original dataset from which this was adapted and its accompanying paper:
* Hoyal Cuthill, Jennifer F. et al. (2019), Data from: Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model, Dryad, Dataset, https://doi.org/10.5061/dryad.2hp1978.
* Hoyal Cuthill, Jennifer F. et al. (2019), Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model, Science Advances, Article-journal, https://doi.org/10.1126/sciadv.aaw4967.
#### BibTeX
Dataset:
```
@misc{CGSHC23,
author = {Krzysztof Kozak and Christopher Lawrence and James Mallet and Owen McMillan and David Carlyn and Mohannad Elhamod},
title = {Curated GoldStandard Hoyal Cuthill},
year = {2023},
url = {https://huggingface.co/datasets/imageomics/Curated_GoldStandard_Hoyal_Cuthill},
doi = {<doi once generated>},
publisher = {Hugging Face}
}
```
Imageomics paper (Part of thesis work):
```
@masterthesis{ramesh_babu23,
title = {Imageomics Approach to Understanding Visual Biological Trait Similarities using Butterfly Mimicry as a Model System},
author = {Reshma Ramesh Babu},
year = 2023,
month = {May},
note = {Available at \url{http://rave.ohiolink.edu/etdc/view?acc_num=osu168198420667979}},
school = {The Ohio State University},
type = {Master's thesis}
}
```
### Contributions
The [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) Institute program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning).