patcdaniel/synchro-April2025-cluster-labeled-highMag

IFCB Plankton Labeled (Cluster-Sorted)

This dataset contains labeled images of phytoplankton collected with the Planktivore Imaging System. Images were preprocessed with a zero-padding and resized to the standard size used for ViT_b_16

The dataset was originally constructed by clustering unlabeled ROI images using deep features from a ViT model.
Clusters were then saved locally and manually curated into taxonomic labels and higher-order groups.

Dataset Summary

• Modality: Images (PNG)
• Source: Planktivore ROI captures
• Curation process:
  1. Extracted deep features with a ViT backbone.
  2. Applied clustering (UMAP + HDBSCAN) to group morphologically similar images.
  3. Exported clusters to local folders.
  4. Manually reviewed and sorted each cluster into taxonomic categories (label) and broader groups (group).

Columns

• image: The plankton ROI image.
• label: Fine-grained label (taxon).
• group: Higher-order grouping (e.g. diatoms, dinoflagellates, ciliates).

Example

from datasets import load_dataset

ds = load_dataset("patcdaniel/synchro-April2025-cluster-labeled-highMag")
sample = ds["train"][0]
sample["image"].show()
print("Label:", ds["train"].features["label"].int2str(sample["label"]))
print("Group:", ds["train"].features["group"].int2str(sample["group"]))