tuto-fsl

Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification. This repository is made for you if:

• you're new to few-shot learning and want to learn;
• or you're looking for reliable, clear and easily usable code that you can use for your projects.

Don't get lost in large repositories with hundreds of methods and no explanation on how to use them. Here, we want each line of code to be covered by a tutorial.

What's in there?

Notebooks: learn and practice

You want to learn few-shot learning and don't know where to start? Start with our tutorials.

• First steps into few-shot image classification

  

Code that you can use and understand

Models:

• AbstractMetaLearner: an abstract class with methods that can be used for any meta-trainable algorithm

• Prototypical Networks

Helpers:

• EasySet: a ready-to-use Dataset object to handle datasets of images with a class-wise directory split
• TaskSampler: samples batches in the shape of few-shot classification tasks

Datasets to test your model

• CU-Birds: we provide a script to download and extract the dataset, along with a meta-train/meta-val/meta-test split along classes. The dataset is ready-to-use with EasySet.

Roadmap

• Implement unit tests
• Integrate more methods:
  • Matching Networks
  • Relation Networks
  • MAML
  • Transductive Propagation Network
• Integrate non-episodic training
• Integrate more benchmarks:
  • miniImageNet
  • tieredImageNet
  • Meta-Dataset

Contribute

This project is very open to contributions! You can help in various ways:

• raise issues
• resolve issues already opened
• tackle new features from the roadmap
• fix typos, improve code quality