Planktonzilla-17M

Overview

Planktonzilla-17M is a large-scale, comprehensive dataset combining 17 million plankton images from all publicly available, labeled plankton datasets. This unified collection enables researchers to train robust deep learning models for plankton identification and classification across diverse imaging systems and oceanographic environments.

The dataset integrates imagery from FlowCAM, ISIIS, UVP5/UVP6, ZooScan, ZooCAM, PlanktoScope, IFCB, and lensless microscopy systems, providing a taxonomically consistent and research-ready resource for plankton research. It is part of the Inria Challenge OcéanIA initiative.

Each image includes a standardized taxonomic hierarchy (Kingdom, Phylum, Class, Order, Family, Genus, Species) mapped to the World Register of Marine Species (WoRMS), along with rich metadata including boolean indicators for plankton classification (live plankton vs. non-plankton) and living status (living organisms vs. detritus/inert), as well as geo-environmental metadata capturing collection location, depth, sampling conditions, and environmental context. Complete traceability to source datasets and original labels is preserved for full transparency.

To date, Planktonzilla-17M represents the largest and most taxonomically complete plankton dataset ever assembled, encompassing 601 distinct plankton classes across all major taxonomic groups and covering 201 known species with complete taxonomic lineage. The dataset captures organisms ranging from microplankton (20 μm) to large zooplankton (>300 μm), collected from 2008-2025 across global oceans, coastal regions, and freshwater environments.

---

Taxonomic Standardization (WoRMS)

To ensure consistency across the diverse source datasets, we implemented a rigorous manual standardization process using the World Register of Marine Species (WoRMS).

Instead of redefining labels, we relied on the original class names provided by annotators in each source dataset and manually matched each of these labels to their corresponding taxonomic entry in WoRMS.

Before matching, the original labels were lightly curated to correct typographical errors and normalize naming variations (e.g., synonyms or inconsistent formatting). Then, for each processed label, the closest valid WoRMS record was identified, and its taxonomic lineage was used to populate the dataset fields (Kingdom to Species).

---

Dataset Structure

Each sample in the dataset contains the following features:

Feature	Type	Description
image	image	PIL Image object in RGB format with variable resolution
dataset	string	Source dataset identifier (e.g., "flowcamnet", "isiisnet", "whoi-plankton")
original_label	string	Class label from the original dataset
original_path	string	Original file path in source dataset
Kingdom	string	Taxonomic Kingdom classification
Phylum	string	Taxonomic Phylum classification
Class	string	Taxonomic Class classification
Order	string	Taxonomic Order classification
Family	string	Taxonomic Family classification
Genus	string	Taxonomic Genus classification
Species	string	Taxonomic Species classification
proposed_label	string	Harmonized label across datasets
plankton	bool	Boolean indicating if the object is plankton
root_class	string	High-level image category (e.g., living, detritus, inert, artifact)
qualifier	string	Additional qualifier of the object (e.g., full_body, part, part_head, part_leg, part_skin, larvae, egg, etc. )
Latitude	float32	Geographic latitude of the observation (degrees)
Longitude	float32	Geographic longitude of the observation (degrees)
Depth_min	float32	Minimum sampling depth in meters
Depth_max	float32	Maximum sampling depth in meters
Temperature	float32	Water temperature at sampling location
Humidity	float32	Humidity of the sample
ObjID	string	Unique object identifier from the source dataset (Ecotaxa or IFCB)

---

Source Datasets

Planktonzilla-17M aggregates data from the following imaging systems and oceanographic programs:

Dataset	Imaging System	Images	Classes	License	Source
Global UVP5Net	UVP5	7.41M	254	CC-BY-NC-4.0	SEANOE
WHOI-Plankton	Microscopy	3.56M	103	MIT	GitHub
JEDI System/OCEANS_CPICS	Observatory System	1.92M	95	CC-BY-SA-4.0	JAMSTEC
ZooScanNet	ZooScan	1.45M	120	CC-BY-NC-4.0	SEANOE
ZooCAMNet	ZooCAM	1.29M	93	CC-BY-NC-4.0	SEANOE
UVP6Net	UVP6	634K	54	CC-BY-NC-4.0	SEANOE
ISIISNet	ISIIS	408K	32	CC-BY-NC-4.0	SEANOE
PlanktoScope	PlanktoScope	180K	263	CC-BY-NC-4.0	SEANOE
FlowCAMNet	FlowCAM	141K	38	CC-BY-NC-4.0	SEANOE
MedPlanktonSet	IFCB	77.3K	139	CC-BY-NC-4.0	Zenodo
SYKE-IFCB 2022	IFCB	63.1K	50	CC-BY-NC-4.0	B2SHARE
PlanktonSet 1.0	ISIIS-2	60.7K	121	CC-BY-NC-4.0	NOAA NCEI
SYKE-ZooScan 2024	ZooScan	22.8K	20	CC-BY-NC-4.0	FairData
ZooLake	Dual Scripps Camera	17.9K	35	CC-BY-4.0	EAWAG OpenData
Lensless	Lensless Microscope	6.4K	10	CC-BY-4.0	IBM Box

Geographic Coverage: Global oceans, coastal regions, Mediterranean Sea, Baltic Sea, Lake Greifensee (freshwater), and laboratory deployments.

Temporal Coverage: Data collection spans from 2008 to 2025 across various oceanographic programs.

---

How to Use

Loading with Hugging Face Datasets

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("project-oceania/planktonzilla-17M")

# Access a sample
sample = dataset["train"][0]
image = sample["image"]
species = sample["Species"]
source = sample["dataset"]

---

Citation

Main Dataset

@misc{planktonzilla-17M,
    author       = {Inria Chile},
    title        = {Planktonzilla-17M dataset},
    month        = apr,
    year         = 2026,
    version      = {1.1.0},
    doi          = {TBD},
    url          = {https://huggingface.co/datasets/project-oceania/planktonzilla-17M},
}

Component Datasets

If you use specific source datasets, please also cite them:

FlowCAMNet:

@article{dataset:flowcamnet,
  title      = {FlowCAMNet: plankton images captured with the FlowCAM},
  author     = {Jalabert, Laetitia and Signoret, Guillaume and others},
  journal    = {SEANOE},
  year       = 2024,
  doi        = {10.17882/101961}
}

ISIISNet:

@article{dataset:isiisnet,
  title  = {ISIISNet: plankton images captured with the ISIIS (In-situ Ichthyoplankton Imaging System)},
  author = {Panaïotis, Thelma and others},
  journal = {SEANOE},
  year   = 2024,
  doi    = {10.17882/101950}
}

WHOI-Plankton:

@article{dataset:whoiplankton,
  title     = {WHOI-Plankton - A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification},
  author    = {Orenstein, Eric C. and Beijbom, Oscar and Peacock, E. E. and Sosik, H. M.},
  journal   = {arXiv preprint arXiv:1510.00745},
  year      = 2015
}

ZooLake:

@article{dataset:zoolake,
  title   = {Deep learning classification of lake zooplankton},
  author  = {Kyathanahally, S.P. and Hardeman, T. and Merz, E. and others},
  journal = {Frontiers in Microbiology},
  volume  = 12,
  year    = 2021,
  doi     = {10.3389/fmicb.2021.746297}
}

ZooScanNet:

@article{dataset:zooscannet,
  title      = {ZooScanNet: plankton images captured with the ZooScan},
  author     = {Elineau, A. and Desnos, C. and Jalabert, L. and others},
  journal    = {SEANOE},
  year       = 2017,
  doi        = {10.17882/55741}
}

ZooCAMNet:

@article{dataset:zoocamnet,
  title = {ZooCAMNet: plankton images captured with the ZooCAM},
  author = {Romagnan, J.-B. and Panaïotis, T. and others},
  journal = {SEANOE},
  year = 2024,
  doi = {10.17882/101928}
}

Lensless:

@article{dataset:lensless,
  title     = {Annotation-free learning of plankton for classification and anomaly detection},
  author    = {Pastore, V.P. and Zimmerman, T.G. and Biswas, S.K. and Bianco, S.},
  journal   = {Scientific Reports},
  volume    = 10,
  number    = 1,
  pages     = 12142,
  year      = 2020,
  doi       = {10.1038/s41598-020-68662-3}
}

Global UVP5Net:

@article{dataset:globaluvp5net,
  title = {A global consistent database of plankton and detritus from in situ imaging by the Underwater Vision Profiler 5},
  journal = {SEANOE},
  year = 2020
}

PlanktonSet 1.0:

@article{dataset:planktonset1,
  title = {PlanktonSet 1.0: Plankton imagery data collected from F.G. Walton Smith},
  author = {Cowen, R.K. and Sponaugle, S. and Robinson, K.L. and others},
  year = 2015
}

PlanktoScope:

@article{dataset:planktoscope,
  title = {PlanktoScope reference: plankton images captured with the PlanktoScope},
  author = {Zaccomer, Hugo and others},
  journal = {SEANOE},
  year = 2025,
  doi = {10.17882/110078}
}

JEDI System/OCEANS_CPICS:

@misc{dataset:jedioceans,
  title = {Plankton image dataset from a cabled observatory system (JEDI System/OCEANS)},
  author = {Yamasaki, H.},
  year = 2022,
  doi = {10.48518/00014}
}

SYKE-IFCB 2022:

@article{dataset:sykeifcb,
  title = {Towards operational phytoplankton recognition with automated high-throughput imaging},
  author = {Kraft, K. and others},
  journal = {Frontiers in Marine Science},
  volume = 9,
  year = 2022,
  doi = {10.3389/fmars.2022.867695}
}

SYKE-ZooScan 2024:

@article{dataset:sykezoos,
  title = {Open-Set Plankton Recognition},
  author = {Kareinen, J. and Skyttä, A. and Eerola, T. and others},
  journal = {Out of Distribution Generalization in Computer Vision Workshop at ECCV 2024},
  year = 2024
}

MedPlanktonSet:

@dataset{dataset:medplanktonset,
  author = {Mente, M.S. and Houliez, E. and Scalco, E. and others},
  title = {MedPlanktonSet - A dataset of labeled IFCB images from the Mediterranean Sea},
  year = 2025,
  doi = {10.5281/zenodo.15471023}
}

---

Support & Contact

For questions, issues, or feedback regarding this dataset:

• Hugging Face Hub: project-oceania
• GitHub Issues: Planktonzilla Repository
• Project Website: OcéanIA Initiative
• Email: Contact through OcéanIA project channels

---

Acknowledgments

We gratefully acknowledge all researchers and institutions who contributed the individual datasets that comprise Planktonzilla-17M.

This dataset represents the collaborative effort of the global oceanographic research community. We thank all scientists, taxonomists, and data curators who made their datasets publicly available to advance marine science and plankton research.

We also encourage continued community contributions to help complete and refine the remaining taxonomic annotations

---

License: CC-BY-NC-4.0
Last Updated: April 2026
Maintained by: Inria Chile - OcéanIA Project