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--- |
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task_categories: |
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- image-classification |
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- image-segmentation |
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tags: |
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- fish |
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- traits |
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- processed |
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- RGB |
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- biology |
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- image |
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- animals |
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- CV |
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pretty_name: Fish-Vista |
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size_categories: |
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- 10K<n<100K |
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language: |
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- en |
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configs: |
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- config_name: species_classification |
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data_files: |
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- split: train |
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path: classification_train.csv |
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- split: test |
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path: classification_test.csv |
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- split: val |
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path: classification_val.csv |
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- config_name: species_trait_identification |
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data_files: |
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- split: train |
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path: identification_train.csv |
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- split: test_insp |
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path: identification_test_insp.csv |
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- split: test_lvsp |
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path: identification_test_lvsp.csv |
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- split: val |
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path: identification_val.csv |
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- config_name: trait_segmentation |
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data_files: |
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- split: all |
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path: segmentation_data.csv |
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--- |
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|**Figure #.** [Image of <>](https://huggingface.co/imageomics/<data-repo>/raw/main/<filename>) <caption description>.| |
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# Dataset Card for Fish-Visual Trait Analysis (Fish-Vista) |
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## Dataset Deetails |
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### Dataset Description |
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- **Curated by:** list curators (authors for _data_ citation, moved up) |
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- **Language(s) (NLP):** [More Information Needed] |
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- **Homepage:** |
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- **Repository:** [related project repo] |
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- **Paper:** |
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--> |
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<!-- Provide a longer summary of what this dataset is. --> |
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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. |
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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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|**Figure 1.** A schematic representation of the different tasks in Fish-Vista Dataset. | |
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<!--This dataset card has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1), and further altered to suit Imageomics Institute needs.--> |
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### Supported Tasks and Leaderboards |
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<!--[Add some more description. could replace graphs with tables]--> |
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|**Figure 2.** Comparison of the fine-grained classification performance of different imbalanced classification methods. | |
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|**Figure 3.** Trait identification performance of different multi-label classification methods. | |
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<!--- |
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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). |
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---> |
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### Languages |
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English |
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## Dataset Structure |
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``` |
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/dataset/ |
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segmentation_masks/ |
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annotations/ |
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images/ |
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sample_images/ |
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filename 1 |
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filename 2 |
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... |
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filename n |
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classification_train.csv |
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classification_test.csv |
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classification_val.csv |
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identification_train.csv |
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identification_test.csv |
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identification_val.csv |
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segmentation_data.csv |
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metadata/ |
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figures/ |
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# figures included in README |
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data-bib.bib |
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``` |
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**Notes:** |
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[Add instructions for downloading images here] |
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* When all images are downloaded and processed, they are contained within a flat directory structure (as demonstrated in `sample_images`). |
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### Data Instances |
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<!-- Add information about each of these (task, number of images per split, etc.). Perhaps reformat as <task>_<split>.csv. |
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--> |
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* **Species Classification:** `classification_<split>.csv` |
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* Approximately 48K images of 419 species for species classification tasks. |
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* There are about 35K training, 7.6K test, and 5K validation images. |
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* **Species-level Trait Identification:** `identification_<split>.csv` |
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* Approximately 53K images of 682 species for trait identification based on _species-level trait expectation_ (i.e., presence/absence of traits based on expectation for the species from information provided by [Phenoscape]() and [FishBase](https://www.fishbase.se/), not by looking at the images). |
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* About 38K training, 8K `test_insp` (species in training set), 1.6K `test_lvsp` (species not in training), and 5.3K validation images. |
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* 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). |
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* **Image-level Trait Identification:** `segmentation_data.csv` |
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* Pixel-level annotations of 9 different traits for 2,427 fish images. |
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* These are ground-truth _image-level trait IDs_ manually annotated. |
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* **Image Information** |
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* **Type:** JPG |
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* **Size (x pixels by y pixels):** Variable |
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* **Background (color or none):** Uniform (White) |
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### Data Fields |
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CSV Columns are as follows: |
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- `filename`: Unique filename for our processed images. |
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- `source_filename`: Filename of the source image. Non-unique, since one source filename can result in multiple crops in our processed dataset. |
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- `original_format`: Original format, all jpg/jpeg. |
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- `arkid`: ARKID from FishAIR for the original images. Non-unique, since one source file can result in multiple crops in our processed dataset. |
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- `verbatim_species`: Verbatim species label from FishAIR. This is not the name-resolved species name. |
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- `species`: Scientific species name from FishAIR. This is not the name-resolved species name. |
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- `family`: Taxonomic family |
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- `source`: Source museum collection. GLIN, Idigbio or Morphbank |
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- `owner`: Owner institution within the source collection. |
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- `standardized_species`: Open-tree-taxonomy-resolved species name. This is the species name that we provide for Fish-Vista |
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- `original_url`: URL to download the original, unprocessed image |
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- `license`: License information for the original image |
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- `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. |
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- `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. |
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- `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. |
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- `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. |
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**Note:** |
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### Data Splits |
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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](#data-instances), above. |
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## Dataset Creation |
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### Curation Rationale |
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<!-- Motivation for the creation of this dataset. For instance, what you intended to study and why that required curation of a new dataset (or if it's newly collected data and why the data was collected (intended use)), etc. --> |
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Fishes are integral to both ecological systems and economic sectors, and studying fish traits is crucial for understanding biodiversity patterns and macro-evolution trends. |
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Currently available fish datasets tend to focus on species classification, and when annotations are available, they tend to be for the entire specimen, allowing for segmenation of background, but not trait discovery. |
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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. |
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### Source Data |
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<!-- This section describes the source data (e.g., news text and headlines, social media posts, translated sentences, ...). As well as an original source it was created from (e.g., sampling from Zenodo records, compiling images from different aggregators, etc.) --> |
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Images and taxonomic labels were aggregated by [Fish-AIR](https://fishair.org/) from [Great Lakes Invasives Network (GLIN)](https://greatlakesinvasives.org/portal/index.php), [iDigBio](https://www.idigbio.org/), [Morphbank](https://www.morphbank.net/), |
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<!--Original source images are from --> |
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[Illinois Natural History Survey (INHS)](https://biocoll.inhs.illinois.edu/portal/index.php), |
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[Minnesota Biodiversity Atlas, Bell Museum](https://bellatlas.umn.edu/index.php), |
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[UMMZ University of Michigan Museum of Zoology, Division of Fishes](https://ipt.lsa.umich.edu/resource?r=ummz\_fish), |
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[University of Wisconsin-Madison Zoological Museum - Fish](http://zoology.wisc.edu/uwzm/), |
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[FMNH Field Museum of Natural History (Zoology) Fish Collection](https://fmipt.fieldmuseum.org/ipt/resource?r=fmnh_fishes), and |
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[Ohio State University Fish Division, Museum of Biological Diversity (OSUM), Occurrence dataset](https://doi.org/10.15468/subsl8). |
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[Phenoscape](https://kb.phenoscape.org/about/phenoscape/kb) and [FishBase](https://www.fishbase.se/search.php) were used to standardize the species labels and provided the information on expected traits at the species level. |
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#### Data Collection and Processing |
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, re-sizing of images, tools and libraries used, etc. |
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This is what _you_ did to it following collection from the original source; it will be overall processing if you collected the data initially. |
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--> |
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|:--| |
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|**Figure 4.** An overview of the data processing and filtering pipeline used to obtain Fish-Vista. | |
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We carefully curated a set of |
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107K images sourced from various museum collections through [Fish-AIR](https://fishair.org/), including [Great Lakes Invasives Network (GLIN)](https://greatlakesinvasives.org/portal/index.php), [iDigBio](https://www.idigbio.org/), and [Morphbank](https://www.morphbank.net/). |
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Our pipeline incorporates rigorous stages such as duplicate removal, metadata-driven filtering, cropping, background removal using the [Segment Anything Model (SAM)](https://github.com/facebookresearch/segment-anything), and a final |
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manual filtering phase. Fish-Vista supports several biologically meaningful tasks such as species |
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classification, trait identification, and trait segmentation. |
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### Annotations |
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<!-- |
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If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. |
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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_). --> |
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#### Annotation process |
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<!-- This section describes the annotation process such as annotation tools used, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> |
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[Phenoscape](https://kb.phenoscape.org/about/phenoscape/kb) and [FishBase](https://www.fishbase.se/search.php) were used to standardize the species labels provided by Fish-AIR. They also provided the information on expected species-level traits. |
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Image-level traits were manually annotated as described below. |
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#### Who are the annotators? |
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[More Information Needed] |
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<!-- This section describes the people or systems who created the annotations. --> |
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The annotation process for the segmentation subset was led by Wasila Dahdul. She provided guidance and oversight to a team from [NEON](https://www.neonscience.org/about), who used [CVAT](https://zenodo.org/records/7863887) to label traits in the images. |
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### Personal and Sensitive Information |
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None |
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## Considerations for Using the Data |
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### Discussion of Biases and Other Known Limitations |
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- This dataset is imbalanced. |
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- There are multiple images of the same specimen for many specimens; sometimes this is due to different views (eg., dorsal or ventral side) |
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- The master files contain only images that were determined to be unique (at the pixel level) through MD5 checksum. |
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^This seems to be a holdover from something else--[More Information Needed] |
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### Recommendations |
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[More Information Needed] |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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## Licensing Information |
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[More Information Needed] |
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<!-- See notes at top of file about selecting a license. |
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If you choose CC0: This dataset is dedicated to the public domain for the benefit of scientific pursuits. We ask that you cite the dataset and journal paper using the below citations if you make use of it in your research. |
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Be sure to note different licensing of images if they have a different license from the compilation. |
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ex: |
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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). |
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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/). |
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For license and citation information by image, see our [license file](https://huggingface.co/datasets/imageomics/treeoflife-10m/blob/main/metadata/licenses.csv). |
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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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--> |
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## Citation |
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[More Information Needed] |
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**BibTeX:** |
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**Data** |
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``` |
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@misc{<ref_code>, |
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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}, |
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title = {Fish-Vista: A Multi-Purpose Dataset for Understanding \& Identification of Traits from Images}, |
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year = {2024}, |
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url = {https://huggingface.co/datasets/imageomics/fish-vista}, |
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doi = {<doi once generated>}, |
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publisher = {Hugging Face} |
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} |
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``` |
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<!-- |
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-for an associated paper: |
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**Paper** |
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``` |
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@article{<ref_code>, |
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title = {Fish-Vista: A Multi-Purpose Dataset for Understanding \& Identification of Traits from Images}, |
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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}, |
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journal = {<journal_name>}, |
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year = <year>, |
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url = {<DOI_URL>}, |
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doi = {<DOI>} |
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} |
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``` |
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--> |
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Please be sure to also cite the original data sources using the citations provided in [metadata/data-bib.bib](https://huggingface.co/datasets/egrace479/fish-vista-test/blob/main/metadata/data-bib.bib). |
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## Acknowledgements |
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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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<!-- You may also want to credit the source of your data, i.e., if you went to a museum or nature preserve to collect it. --> |
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## Glossary |
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<!-- [optional] If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> |
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## More Information |
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<!-- [optional] Any other relevant information that doesn't fit elsewhere. --> |
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## Dataset Card Authors |
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Kazi Sajeed Mehrab and Elizabeth G. Campolongo |
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## Dataset Card Contact |
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[More Information Needed--optional] |
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<!-- Could include who to contact with questions, but this is also what the "Discussions" tab is for. --> |
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