🎯 Few-shot Learning
Overview
Rare species are the norm, not the exception!
The few-shot benchmark isolates species with fewer than five training samples. The task: Can your model generalize with minimal supervision?
Use Cases
- Identification of rare, endangered, or newly observed fungi
- General few-shot learning research (prototypical networks, embedding-based methods, etc.)
Data & Splits
- Only species with <5 training images are included.
- Standard temporal splits: train (up to 2021), validation (2022), test (2023).
Evaluation Protocol
- Primary Metric: Top-1 accuracy
- Secondary Metrics: Macro F1-score, Top-3 accuracy
Baselines & Results
Includes nearest-neighbor, centroid-prototype, and standard classifiers on strong feature extractors (CLIP, BioCLIP, DINOv2, BEiT).
See Baselines & Models for results and example code.
Quick Start
- Few-shot split provided in the dataset package and Kaggle.