Are you diving into the world of few-shot learning and seeking a clear, practical starting point? Look no further! The EasyFSL GitHub repository is designed to accelerate your journey into few-shot image classification. Whether you’re a newcomer eager to learn the fundamentals or a seasoned practitioner in need of reliable, user-friendly code for your projects, EasyFSL offers a wealth of resources to get you up and running quickly. Forget navigating through complex repositories with overwhelming methods and unclear instructions. EasyFSL prioritizes clarity and usability, ensuring every line of code is supported by comprehensive tutorials.
Exploring EasyFSL: Your Few-Shot Learning Toolkit on GitHub
What exactly does EasyFSL bring to the table? This GitHub repository is packed with features designed to make few-shot learning accessible and efficient.
Interactive Notebooks: Learn and Experiment
If you’re just starting out with few-shot learning, EasyFSL’s tutorial notebooks are the perfect launchpad. They offer hands-on experience and guide you through the essential concepts step-by-step.
| Notebook | Description | Colab |
|---|---|---|
| First steps into few-shot image classification | A concise and accessible introduction to Few-Shot Learning, achievable in under 15 minutes. | |
| Example of episodic training | A practical starting point for designing episodic training scripts using the EasyFSL library. | |
| Example of classical training | A helpful template for building classical training scripts within the EasyFSL framework. | |
| Test with pre-extracted embeddings | Learn how to optimize inference by pre-extracting embeddings, a common practice in few-shot learning with frozen backbones. |
Ready-to-Use and Understandable Code
EasyFSL stands out by offering a comprehensive collection of state-of-the-art few-shot learning methods. With 11 integrated methods, it’s arguably the most extensive open-source library available for few-shot learning.
To simplify your own implementations, the repository includes a FewShotClassifier class and a selection of commonly used architectures. These components are designed to be modular and easy to grasp, facilitating a smoother learning curve and faster development.
Key Features:
- Variety of Methods: Explore and implement a wide array of cutting-edge few-shot learning techniques.
- Modular Design: Utilize the
FewShotClassifierclass to streamline the development of new algorithms. - Pre-built Architectures: Leverage readily available, commonly used architectures to accelerate your projects.
Datasets for Model Testing and Evaluation
Navigating the numerous datasets in few-shot learning can be daunting. EasyFSL simplifies this by providing a curated collection of datasets that are widely used in the field. These datasets are readily accessible, downloadable, and seamlessly integrable with EasyFSL.
Supported Datasets:
- CU-Birds: Downloadable with a provided recipe and includes a standard train/val/test split.
from easyfsl.datasets import CUB train_set = CUB(split="train", training=True) test_set = CUB(split="test", training=False) - tieredImageNet: Requires the ILSVRC2015 dataset and specification files are provided for easy setup.
from easyfsl.datasets import TieredImageNet train_set = TieredImageNet(split="train", training=True) test_set = TieredImageNet(split="test", training=False) - miniImageNet: Similar to tieredImageNet, using ILSVRC2015 and provided specification files. Option to load on RAM for faster training.
from easyfsl.datasets import MiniImageNet train_set = MiniImageNet(root="where/imagenet/is", split="train", training=True) test_set = MiniImageNet(root="where/imagenet/is", split="test", training=False) - Danish Fungi: A more recent benchmark dataset, downloadable with metadata provided for EasyFSL integration.
from easyfsl.datasets import DanishFungi dataset = DanishFungi(root="where/fungi/is")
Benchmark Scripts and Utilities
EasyFSL includes scripts to reproduce benchmark results and utility functions that are helpful in few-shot learning research, further accelerating your experimentation and development process.
scripts/predict_embeddings.py: Extract embeddings from datasets using pre-trained backbones.scripts/benchmark_methods.py: Evaluate methods on test datasets using pre-extracted embeddings.easyfsl/utils.py: Collection of useful utility functions for common tasks in few-shot learning.
Quick Start with EasyFSL
Ready to get started? Here’s how to quickly set up and begin using EasyFSL for your few-shot learning projects:
- Installation: Install the package using pip:
pip install easyfslor fork the GitHub repository for direct access and modifications. - Data Download: Download the datasets you intend to use as described in the Datasets section.
- Script Design: Develop your training and evaluation scripts, leveraging the example notebooks for episodic training and classical training as templates.
Contribute to EasyFSL
The EasyFSL project thrives on community contributions! There are numerous ways you can help enhance this valuable resource:
- Report and raise issues you encounter.
- Help resolve existing issues.
- Contribute to the roadmap by tackling new features.
- Improve code quality, fix typos, and enhance documentation.
Performance Benchmarks
EasyFSL has been used to benchmark a range of few-shot learning methods. The following table summarizes the performance on miniImageNet and tieredImageNet datasets using a ResNet12 backbone. Inference times are indicative and measured over 1000 tasks.
Benchmark Results on miniImageNet & tieredImageNet
| Method | Ind / Trans | miniImagenet1-shot | miniImagenet5-shot | tieredImagenet1-shot | tieredImagenet5-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 |
Note: Best inductive and transductive results are bolded for each column.
For detailed instructions on reproducing these benchmarks, refer to the GitHub repository.
Conclusion
EasyFSL on GitHub is an invaluable resource for anyone learning or working with few-shot learning. Its focus on usability, comprehensive documentation, and inclusion of state-of-the-art methods and benchmark datasets makes it an excellent toolkit for both educational purposes and practical applications. Start exploring EasyFSL today and accelerate your journey into the exciting field of few-shot learning!
