# sicara/easy-few-shot-learning

Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.

Repository: https://github.com/sicara/easy-few-shot-learning
Canonical: https://ross.abutalabs.com/products/easy-few-shot-learning
Language: Python
License: MIT
License Family: permissive
Topics: few-shot-learning, few-shot-recognition, few-shot-classifcation, meta-learning, machine-learning, pytorch, image-classification, deep-learning
Last push: 2024-11-13T14:02:24+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2039, "days_push": 658, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1313, forks 170 (observed 2026-08-28T04:04:20.245454+00:00)

## What it is
A Python library (easyfsl) with ready-to-use code and tutorial notebooks for few-shot image classification and meta-learning, built on PyTorch. It pairs each implementation with explanatory notebooks so learners can follow along step by step.

## Use cases
- learn few-shot learning from scratch with tutorials
- train a few-shot image classifier in pytorch
- run episodic training for prototypical networks
- evaluate few-shot models on benchmark datasets
- apply meta-learning to a small image dataset
- get clear reference code for few-shot classification research

## When to choose
- you are new to few-shot learning and want guided notebooks
- you need simple, well-documented few-shot classification code for a project
- you want a lightweight PyTorch library rather than a huge research codebase

## When to avoid
- you need many state-of-the-art few-shot methods in one framework
- you need production-scale training pipelines or distributed training
- your task is not image classification

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, computer-vision, tutorials
- platform: python
- tags: few-shot-learning, meta-learning, pytorch, image-classification, tutorial-notebooks

## Member repositories
- sicara/easy-few-shot-learning (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:20.245454+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:48:55.788604+00:00, confidence not recorded.
  - readme: https://github.com/sicara/easy-few-shot-learning (fetched 2026-08-28T04:04:20.245454+00:00, sha 9ca7cdbcbf0a)
- Data as of 2026-08-30T08:39:29.467469+00:00.