---
annotations_creators:
- expert-generated
language:
- en
license: cc-by-4.0
pretty_name: IFCB Plankton Labeled (Cluster-Sorted)
task_categories:
- image-classification
tags:
- plankton
- microscopy
- ifcb
- clustering
- manual-sorting
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Akashiwo
          '1': Asterionellopsis
          '2': Centric
          '3': Ceratium
          '4': Chaetoceros
          '5': Ciliates
          '6': Detonula
          '7': Diatom_aggregate
          '8': Dictyocha
          '9': Dinophysis
          '10': Ditylum
          '11': Eucampia
          '12': Guinardia
          '13': Gymnodinium
          '14': Gyrodinium
          '15': Heterocapsa
          '16': Lauderia_Hemiaulus
          '17': Leptocylindrus
          '18': NanoPlankton
          '19': Pennate
          '20': Phaeocystis
          '21': Prorocentrum
          '22': Protoperidinium
          '23': Psuedo-nitzschia
          '24': Rhizosolenia_Proboscia
          '25': Skeletonema
          '26': Stephanopyxis
          '27': Thalassionema
          '28': Thalassiosira
          '29': Tintinnid
          '30': Unknown_detritus
          '31': Unknown_full_image
          '32': Unknown_phyto
  - name: group
    dtype: string
  - name: filename
    dtype: string
  splits:
  - name: train
    num_bytes: 286871203.36
    num_examples: 7895
  download_size: 291443502
  dataset_size: 286871203.36
---

# 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
```python
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"]))
```