mmasana/FACIL
Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.
This framework helps machine learning researchers and practitioners analyze different strategies for Class-Incremental Learning (CIL). It takes in existing CIL approaches and datasets, allowing you to train models on a sequence of new classes over time without forgetting old ones. The output is a performance evaluation of various incremental learning methods.
563 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or engineer developing and evaluating new class-incremental learning algorithms or comparing existing ones.
Not ideal if you are looking for a pre-trained model for immediate use in an application or a simple, non-incremental supervised learning solution.
Stars
563
Forks
103
Language
Python
License
MIT
Category
Last pushed
May 26, 2023
Commits (30d)
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