envision-classifier 0.3.2

Few-shot classifier for detecting eye imaging datasets

envision-classifier

SetFit few-shot classifier for identifying eye imaging datasets from scientific metadata.

Part of the EyeACT project by the FAIR Data Innovations Hub.

Installation

pip install envision-classifier

Python API

from envision_classifier import EyeImagingClassifier

# Downloads model from HuggingFace on first use
clf = EyeImagingClassifier()

# Classify a single record
result = clf.classify("Retinal OCT dataset for diabetic retinopathy")
print(result)
# {'label': 'EYE_IMAGING', 'confidence': 0.98,
#  'probabilities': {'EYE_IMAGING': 0.98, 'NEGATIVE': 0.02}}

# Classify a batch
results = clf.classify_batch([
    "Retinal fundus photography dataset for glaucoma screening",
    "COVID-19 genome sequencing data",
    {"title": "OCT images", "description": "Macular degeneration scans"},
])

# Use a local model instead of downloading
clf = EyeImagingClassifier(model_path="./my_model")

CLI

After installing, the envision-classifier command is available:

# Classify a text string
envision-classifier classify --text "Retinal OCT dataset for diabetic retinopathy"

# Classify from a JSON file
envision-classifier classify records.json

# Pipe JSON via stdin
echo '{"title": "Fundus images", "description": "DR screening"}' | envision-classifier classify

# Train a new model from built-in training data
envision-classifier train --output ./my_model

# Show model info and training data counts
envision-classifier info

Classification Labels

Label	Description
EYE_IMAGING	Actual eye imaging datasets (fundus, OCT, OCTA, cornea)
NEGATIVE	Everything else (software, non-imaging eye data, unrelated domains)

Model

• Base model: sentence-transformers/all-mpnet-base-v2 (768-dim)
• Training data: 891 curated examples (262 EYE_IMAGING, 629 NEGATIVE) from Zenodo, Figshare, Dryad, Kaggle, and NEI
• Test accuracy: 0.961, EYE_IMAGING F1: 0.936
• Spot-check: 30/33 (90.9%)
• Model weights: fairdataihub/envision-eye-imaging-classifier

Multi-Repository Results

Applied across 6 repositories via envision-discovery:

Source	EYE_IMAGING	NEGATIVE	Total
Zenodo	60	455	515
DataCite	752	1,084	1,836
Figshare	1,049	951	2,000
Kaggle	248	484	732
Dryad	32	57	89
NEI	686	976	1,662

Classification is based on metadata only (titles, descriptions, keywords, and file types inspected inside archives via HTTP Range requests) -- no dataset files are downloaded.

Related

• envision-discovery -- Full pipeline (scraping + classification + export)
• Model on HuggingFace

License

MIT