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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.
| Date | Stars |
|---|---|
| 2026-07-24 | 1312 |
| 2026-07-25 | 1312 |
| 2026-07-28 | 1312 |
| 2026-07-30 | 1312 |
| 2026-08-06 | 1312 |
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# Easy Few-Shot Learning       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. | Notebook | Description | Colab | |------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [First steps into few-shot image classification](notebooks/my_first_few_shot_classifier.ipynb) | Basically Few-Shot Learning 101, in less than 15min. | [](https://colab.research.google.com/github/sicara/easy-few-shot-learning/blob/master/notebooks/my_first_few_shot_classifier.ipynb) | | [Example of episodic training](notebooks/episodic_training.ipynb) | Use it as a starting point if you want to design a script for episodic training using EasyFSL. | [](https://colab.research.google.com/github/sicara/easy-few-shot-learning/blob/master/notebooks/episodic_training.ipynb) | | [Example of classical training](notebooks/classical_training.ipynb) | Use it as a starting point if you want to design a script for classical training using EasyFSL. | [](https://colab.research.google.com/github/sicara/easy-few-shot-learning/blob/master/notebooks/classical_training.ipynb) | | [Test with pre-extracted embeddings](notebooks/inference_with_extracted_embeddings.ipynb) | Most few-shot methods use a frozen backbone a
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Dmytro Durach · Ukraine
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:95f92e933f2b2b4f, topic:deep-learning, topic:pytorch
matched fp:95f92e933f2b2b4f, topic:image-classification, desc:image classification, readme:image classification