---
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: classification_fv_419
  data_files:
  - split: train
    path: classification_train.csv
  - split: test
    path: classification_test.csv
  - split: val
    path: classification_val.csv
- config_name: identification_fv_682
  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: segmentation_fv_1200
  data_files:
  - split: all
    path: segmentation_data.csv
---

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

## Dataset Deetails

### Dataset Description

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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)](https://greatlakesinvasives.org/portal/index.php), [iDigBio](https://www.idigbio.org/), and [Morphbank](https://www.morphbank.net/) 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](https://fishair.org/). 


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### Supported Tasks and Leaderboards
[More Information Needed]

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### Supported Tasks and Leaderboards

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### Languages

English

## Dataset Structure

<!-- Add information about each of these (task, number of images per split, etc.). Perhaps reformat as <task>_<split>.csv.
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* **classification_train.csv:** Information for the approximately x image files.

* **classification_test.csv:** Information for the approximately x image files.

* **classification_val.csv:** Information for the approximately x image files.

* **identification_train.csv:** Information for the approximately x image files.

* **identification_test_insp.csv:** Information for the approximately x image files.

* **identification_test_lvsp.csv:** Information for the approximately x image files.

* **identification_val.csv:** Information for the approximately x image files.

* **segmentation_data.csv:** Information for the approximately x image files.


**Notes:**


### Data Instances

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


#### Preprocessing steps:

### 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 barbel 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`).
      
## Dataset Creation

### Curation Rationale
[More Information Needed]
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### Source Data

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#### Data Collection and Processing
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#### Who are the source data producers?
[More Information Needed]
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Ex: This dataset is a collection of images taken of the butterfly collection housed at the Ohio State University Museum of Biological Diversity. The associated labels and metadata are the information provided with the collection from biologists that study butterflies and supplied the specimens to the museum.
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### Annotations
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Ex: We standardized the taxonomic labels provided by the various data sources to conform to a uniform 7-rank Linnean structure. (Then, under annotation process, describe how this was done: Our sources used different names for the same kingdom (both _Animalia_ and _Metazoa_), so we chose one for all (_Animalia_). -->

#### Annotation process
[More Information Needed]
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#### Who are the annotators?
[More Information Needed]
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### 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.



### Recommendations
[More Information Needed]
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## Licensing Information
[More Information Needed]

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The data (images and text) contain a variety of licensing restrictions mostly within the CC family. Each image and text in this dataset is provided under the least restrictive terms allowed by its licensing requirements as provided to us (i.e, we impose no additional restrictions past those specified by licenses in the license file).

EOL images contain a variety of licenses ranging from [CC0](https://creativecommons.org/publicdomain/zero/1.0/) to [CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/).
For license and citation information by image, see our [license file](https://huggingface.co/datasets/imageomics/treeoflife-10m/blob/main/metadata/licenses.csv).

This dataset (the compilation) has been marked as dedicated to the public domain by applying the [CC0 Public Domain Waiver](https://creativecommons.org/publicdomain/zero/1.0/). However, images may be licensed under different terms (as noted above).
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## Citation
[More Information Needed]

**BibTeX:**
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**Paper**
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  title    = {<title>},
  author   = {<author1 and author2>},
  journal  = {<journal_name>},
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## Acknowledgements

This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) 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). 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.

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## Glossary 

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## More Information 

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## Dataset Card Authors 

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## Dataset Card Contact

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