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Datasets:
imageomics
/
fish-vista

Tasks:
Image Classification
Image Segmentation
Modalities:
Image
Tabular
Text
Formats:
csv
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
fish
traits
processed
RGB
biology
image
DOI:
Libraries:
Datasets
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metadata
task_categories:
  - image-classification
  - image-segmentation
tags:
  - fish
  - traits
  - processed
  - RGB
  - biology
  - image
  - animals
  - CV
pretty_name: Fish-Vista
size_categories:
  - 10K<n<100K
language:
  - en
configs:
  - config_name: species_classification
    data_files:
      - split: train
        path: classification_train.csv
      - split: test
        path: classification_test.csv
      - split: val
        path: classification_val.csv
  - config_name: species_trait_identification
    data_files:
      - split: train
        path: identification_train.csv
      - split: test_insp
        path: identification_test_insp.csv
      - split: test_lvsp
        path: identification_test_lvsp.csv
      - split: val
        path: identification_val.csv
  - config_name: trait_segmentation
    data_files:
      - segmentation_data.csv
      - segmentation_masks/images/*.png

Dataset Card for Fish-Visual Trait Analysis (Fish-Vista)

Dataset Deetails

Dataset Description

The Fish-Visual Trait Analysis (Fish-Vista) dataset is a large, annotated collection of 60K fish images spanning 1900 different species; it supports several challenging and biologically relevant tasks including species classification, trait identification, and trait segmentation. These images have been curated through a sophisticated data processing pipeline applied to a cumulative set of images obtained from various museum collections. Fish-Vista provides fine-grained labels of various visual traits present in each image. It also offers pixel-level annotations of 9 different traits for 2427 fish images, facilitating additional trait segmentation and localization tasks.

The Fish Vista dataset consists of museum fish images from Great Lakes Invasives Network (GLIN), iDigBio, and Morphbank databases. We acquired these images, along with associated metadata including the scientific species names, the taxonomical family the species belong to, and licensing information, from the Fish-AIR repository.

Figure 1
Figure 1. A schematic representation of the different tasks in Fish-Vista Dataset.

Supported Tasks and Leaderboards

Figure 2
Figure 2. Comparison of the fine-grained classification performance of different imbalanced classification methods.
Figure 3
Figure 3. Trait identification performance of different multi-label classification methods.

Languages

English

Dataset Structure 

/dataset/
    segmentation_masks/
        annotations/
        images/
    sample_images/
        filename 1
        filename 2
        ...
        filename n
    classification_train.csv
    classification_test.csv
    classification_val.csv
    identification_train.csv
    identification_test.csv
    identification_val.csv
    segmentation_data.csv
    metadata/
        figures/
            # figures included in README
        data-bib.bib

Notes: [Add instructions for downloading images here]

  • When all images are downloaded and processed, they are contained within a flat directory structure (as demonstrated in sample_images).

Data Instances

  • Species Classification: classification_<split>.csv

    • Approximately 48K images of 419 species for species classification tasks.
    • There are about 35K training, 7.6K test, and 5K validation images.

  • Trait Identification: identification_<split>.csv

    • Approximately 53K images of 682 species for trait identification based on species-level trait labels (i.e., presence/absence of traits based on trait labels for the species from information provided by Phenoscape and FishBase).
    • About 38K training, 8K test_insp (species in training set), 1.6K test_lvsp (species not in training), and 5.3K validation images.
    • Train, test, and validation splits are generated based on traits, so there are 628 species in train, 471 species in test_insp, 51 species in test_lvsp, and 452 in the validation set (4 species only in val).

  • Trait Segmentation: segmentation_data.csv

    • Pixel-level annotations of 9 different traits for 2,427 fish images.
    • About x training, y test and z validation images for the segmentation task
    • These are also used as manually annotated test set for Trait Identification.

  • Image Information

    • Type: JPG
    • Size (x pixels by y pixels): Variable
    • Background (color or none): Uniform (White)

Data Fields

CSV Columns are as follows:

  • filename: Unique filename for our processed images.
  • source_filename: Filename of the source image. Non-unique, since one source filename can result in multiple crops in our processed dataset.
  • original_format: Original format, all jpg/jpeg.
  • arkid: ARKID from FishAIR for the original images. Non-unique, since one source file can result in multiple crops in our processed dataset.
  • verbatim_species: Verbatim species label from FishAIR. This is not the name-resolved species name.
  • species: Scientific species name from FishAIR. This is not the name-resolved species name.
  • family: Taxonomic family
  • source: Source museum collection. GLIN, Idigbio or Morphbank
  • owner: Owner institution within the source collection.
  • standardized_species: Open-tree-taxonomy-resolved species name. This is the species name that we provide for Fish-Vista
  • original_url: URL to download the original, unprocessed image
  • license: License information for the original image
  • adipose_fin: Presence/absence of the adipose fin trait. NA for the classification (FV-419) dataset, since it is only used for identification. 1 indicates presence and 0 indicates absence. This is used for trait identification.
  • pelvic_fin: Presence/absence of the pelvic trait. NA for the classification (FV-419) dataset, since it is only used for identification. 1 indicates presence and 0 indicates absence. This is only used for trait identification.
  • barbel: Presence/absence of the barbel trait. NA for the classification (FV-419) dataset, since it is only used for identification. 1 indicates presence and 0 indicates absence. This is used for trait identification.
  • multiple_dorsal_fin: Presence/absence of the dorsal fin trait. NA for the classification (FV-419) dataset, since it is only used for identification. 1 indicates presence, 0 indicates absence and -1 indicates unknown. This is used for trait identification.

Note:

Data Splits

For each task (or subset), the split is indicated by the CSV name (e.g., classification_<split>.csv). More information is provided in Data Instances, above.

Dataset Creation

Curation Rationale

Fishes are integral to both ecological systems and economic sectors, and studying fish traits is crucial for understanding biodiversity patterns and macro-evolution trends. Currently available fish datasets tend to focus on species classification. They lack finer-grained labels for traits. When segmentation annotations are available in existing datasets, they tend to be for the entire specimen, allowing for segmenation of background, but not trait segmentation. The ultimate goal of Fish-Vista is to provide a clean, carefully curated, high-resolution dataset that can serve as a foundation for accelerating biological discoveries using advances in AI.

Source Data

Images and taxonomic labels were aggregated by Fish-AIR from

Phenoscape and FishBase were used to provide the information on traits at the species level.

Open Tree Taxonomy was used to standardize the species names provided by Fish-AIR.

Data Collection and Processing

Figure 4
Figure 4. An overview of the data processing and filtering pipeline used to obtain Fish-Vista.

We carefully curated a set of 60K images sourced from various museum collections through Fish-AIR, including Great Lakes Invasives Network (GLIN), iDigBio, and Morphbank. Our pipeline incorporates rigorous stages such as duplicate removal, metadata-driven filtering, cropping, background removal using the Segment Anything Model (SAM), and a final manual filtering phase. Fish-Vista supports several biologically meaningful tasks such as species classification, trait identification, and trait segmentation.

Annotations

Annotation process

Phenoscape and FishBase were used to provide the information on species-level traits (the species-trait matrix).

Open Tree Taxonomy was used to standardize the species names provided by Fish-AIR.

Image-level trait segmentations were manually annotated as described below.

The annotation process for the segmentation subset was led by Wasila Dahdul. She provided guidance and oversight to a team of three people from NEON, who used CVAT to label nine external traits in the images. These traits correspond to the following terms for anatomical structures in the UBERON anatomy ontology:

  1. Eye, UBERON_0000019
  2. Head, UBERON_0000033
  3. Barbel, UBERON_2000622
  4. Dorsal fin, UBERON_0003097
  5. Adipose fin, UBERON_2000251
  6. Pectoral fin, UBERON_0000151
  7. Pelvic fin, UBERON_0000152
  8. Anal fin, UBERON_4000163
  9. Caudal fin, UBERON_4000164

Personal and Sensitive Information

None

Considerations for Using the Data

Discussion of Biases and Other Known Limitations

  • This dataset is imbalanced.
  • There are multiple images of the same specimen for many specimens; sometimes this is due to different views (eg., dorsal or ventral side)
    • The master files contain only images that were determined to be unique (at the pixel level) through MD5 checksum. ^This seems to be a holdover from something else--[More Information Needed]

Recommendations

[More Information Needed]

Licensing Information

[More Information Needed]

Citation

[More Information Needed]

BibTeX:

Data

@misc{<ref_code>,
  author = {Kazi Sajeed Mehrab and M. Maruf and Arka Daw and Harish Babu Manogaran and Abhilash Neog and Mridul Khurana and Bahadir Altintas and Yasin Bakış and Elizabeth G Campolongo and Matthew J Thompson and Xiaojun Wang and Hilmar Lapp and Wei-Lun Chao and Paula M. Mabee and Henry L. Bart Jr. and Wasila Dahdul and Anuj Karpatne},
  title = {Fish-Vista: A Multi-Purpose Dataset for Understanding \& Identification of Traits from Images},
  year = {2024},
  url = {https://huggingface.co/datasets/imageomics/fish-vista},
  doi = {<doi once generated>},
  publisher = {Hugging Face}
}

Please be sure to also cite the original data sources using the citations provided in metadata/data-bib.bib.

Acknowledgements

This work was supported by the Imageomics Institute, which 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.

Glossary

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Dataset Card Authors

Kazi Sajeed Mehrab and Elizabeth G. Campolongo

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