Datasets:
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---
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"]))
``` |