Are you diving into the fascinating world of few-shot learning and searching for accessible, practical code examples? Look no further! The EasyFSL GitHub repository is designed to be your ultimate resource. Whether you’re a newcomer eager to grasp the fundamentals or a seasoned practitioner seeking reliable, well-documented code for your projects, EasyFSL provides a clear and efficient pathway to mastering few-shot image classification. Forget navigating complex, sprawling codebases – EasyFSL prioritizes clarity and usability, ensuring every line of code is backed by insightful tutorials.
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What Can You Find in EasyFSL?
EasyFSL is more than just a code repository; it’s a comprehensive toolkit designed to accelerate your journey into few-shot learning. Here’s a glimpse of what awaits you:
Interactive Notebooks: Learn by Doing
For those new to the field or seeking a hands-on learning experience, EasyFSL’s tutorial notebooks are the perfect starting point. These notebooks are designed to be both educational and practical, allowing you to learn and experiment directly within the code.
| Notebook | Description | Colab |
|---|---|---|
| First steps into few-shot image classification | Your entry point to few-shot learning, covering the essential concepts in under 15 minutes. | |
| Example of episodic training | A practical guide to implementing episodic training with EasyFSL, ideal for building your own training scripts. | |
| Example of classical training | Learn how to design classical training scripts using EasyFSL, providing a solid foundation for various few-shot approaches. | |
| Test with pre-extracted embeddings | Discover the efficiency of inference with pre-extracted embeddings in EasyFSL, optimizing your workflow for faster experimentation. |
Colab badges linking to interactive notebooks for EasyFSL tutorials, providing users with immediate access to hands-on coding and learning experiences.
These interactive notebooks, accessible directly in your browser via Google Colab, cover fundamental aspects of few-shot learning. They range from building your first classifier to implementing episodic and classical training paradigms, and even performing inference using pre-extracted embeddings. Each notebook serves as a stepping stone, guiding you through the practical application of few-shot learning concepts.
Ready-to-Use Code for State-of-the-Art Methods
EasyFSL stands out by offering an extensive collection of state-of-the-art few-shot learning methods. Boasting 11 built-in methods, it positions itself as the most comprehensive open-source library in its domain. This rich selection empowers you to explore and implement cutting-edge techniques without getting bogged down in implementation details.
Furthermore, the library provides a FewShotClassifier class, streamlining the development of custom few-shot classification algorithms. Complementing this, EasyFSL includes a range of commonly used architectures, providing building blocks for your own innovative models. For a deeper dive into the included methods and their performance, refer to the benchmark section within the repository.
Streamlined Data Loading Tools
Data handling in few-shot learning presents unique challenges. EasyFSL addresses this with dedicated tools that simplify the process of loading data in the task-specific format required for few-shot classification. Instead of wrestling with data preparation, EasyFSL provides utilities that handle the intricacies, allowing you to focus on model development and experimentation.
Curated Datasets for Benchmarking
Navigating the landscape of datasets in few-shot learning can be overwhelming. EasyFSL simplifies this by providing easy access to several commonly used datasets. These datasets are not only readily available but also come with clear instructions for download and usage within EasyFSL. Datasets include:
- CU-Birds: Downloadable and ready to use with standard train/validation/test splits.
- tieredImageNet: Compatible with ILSVRC2015, requiring users to ensure correct path configurations.
- miniImageNet: Also compatible with ILSVRC2015, with options to load directly into RAM for faster training.
- Danish Fungi: A more recent and challenging dataset, with provided download links and usage guidelines.
These datasets enable you to rigorously test and compare different few-shot learning methods under standardized conditions.
Benchmark Scripts for Reproducibility
To ensure transparency and facilitate comparative analysis, EasyFSL includes scripts to reproduce the benchmark results presented in the repository. These scripts cover two key stages:
scripts/predict_embeddings.py: Extracts embeddings from a dataset using a pre-trained backbone.scripts/benchmark_methods.py: Evaluates method performance on a test dataset using pre-extracted embeddings.
These scripts empower you to verify the reported benchmarks and evaluate your own models using the same methodology. Additionally, the repository offers a collection of utility functions that the developers have found valuable in their research, further enhancing the practical utility of EasyFSL.
Quick Start with EasyFSL
Ready to get started? Here’s a simple pathway to integrate EasyFSL into your workflow:
- Installation: Install EasyFSL using pip with the command:
pip install easyfslor by forking the GitHub repository for direct access and modification. - Data Download: Choose and download your desired datasets following the provided instructions for each dataset.
- Scripting: Leverage the example notebooks for episodic and classical training to design your training and evaluation scripts, adapting them to your specific research questions.
Contribute to the EasyFSL Open Source Project
EasyFSL thrives on community contributions. As an open-source project, it welcomes and encourages participation from users like you. There are numerous ways to contribute and help enhance EasyFSL:
- Issue Reporting: Raise issues for bugs, feature requests, or areas for improvement.
- Issue Resolution: Tackle existing issues to help improve the library’s robustness and functionality.
- Feature Development: Contribute new features from the project roadmap to expand EasyFSL’s capabilities.
- Code Enhancement: Improve code quality, fix typos, and enhance documentation to make EasyFSL even more user-friendly.
By contributing, you become an integral part of the EasyFSL community, helping to advance the field of accessible few-shot learning resources.
Benchmarking Performance
EasyFSL provides comprehensive benchmarks for a range of few-shot learning methods. These benchmarks offer insights into the performance of different methods on standard datasets. Key aspects of the benchmarking include:
- Method Hyperparameters: All method hyperparameters are meticulously documented in a JSON configuration file, ensuring transparency and reproducibility.
- Feature Extraction: Inference times are calculated based on 1000 tasks using pre-extracted features, providing a standardized performance measure.
- Backbone Consistency: All methods utilize the same custom ResNet12 backbone, ensuring fair comparisons across different techniques.
miniImageNet & tieredImageNet Results
The benchmark results on miniImageNet and tieredImageNet highlight the effectiveness of various methods. The table below summarizes the inductive and transductive performance of each method, along with their inference times.
| Method | Ind / Trans | miniImagenet 1-shot | miniImagenet 5-shot | tieredImagenet 1-shot | tieredImagenet 5-shot | Time |
|---|---|---|---|---|---|---|
| ProtoNet | Inductive | 63.6 | 80.4 | 60.2 | 77.4 | 6s |
| SimpleShot | Inductive | 63.6 | 80.5 | 60.2 | 77.4 | 6s |
| MatchingNet | Inductive | – | – | – | – | – |
| RelationNet | Inductive | – | – | – | – | – |
| Finetune | Inductive | 63.3 | 80.5 | 59.8 | 77.5 | 1mn33s |
| FEAT | Inductive | 64.7 | 80.1 | 61.3 | 76.2 | 3s |
| BD-CSPN | Transductive | 69.8 | 82.2 | 66.3 | 79.1 | 7s |
| LaplacianShot | Transductive | 69.8 | 82.3 | 66.2 | 79.2 | 9s |
| PT-MAP | Transductive | 76.1 | 84.2 | 71.7 | 80.7 | 39mn40s |
| TIM | Transductive | 74.3 | 84.2 | 70.7 | 80.7 | 3mn05s |
| Transductive Finetuning | Transductive | 63.0 | 80.6 | 59.1 | 77.5 | 30s |
Benchmark table displaying performance metrics of various few-shot learning methods on miniImageNet and tieredImageNet datasets, allowing for direct method comparison.
To reproduce these benchmarks, follow the detailed steps provided in the repository, including downloading pre-trained weights, extracting features, and running the evaluation scripts.
EasyFSL is your all-in-one solution for navigating the landscape of few-shot learning code on GitHub. Dive in, explore the notebooks, utilize the code, and contribute to the community!
