jax-models
Unofficial JAX/Flax implementations of deep learning research papers for vision transformers and other architectures.
What is jax-models?
JAX Models is an open-source Python library providing unofficial JAX/Flax implementations of deep learning research papers, particularly in computer vision. It solves the problem of research papers lacking code or using incompatible frameworks by offering ready-to-use model architectures and layers. The project aims to accelerate experimentation and reproducibility for researchers and developers working with modern neural network designs.
Machine learning researchers, deep learning practitioners, and students who want to experiment with or reproduce recent vision architectures using JAX/Flax. It's particularly valuable for those working with transformer-based models and seeking clean, modular implementations.
Developers choose JAX Models because it provides a curated collection of state-of-the-art model implementations in JAX/Flax that are often missing from official releases. The library offers a consistent API for loading pretrained models and modular components, saving significant implementation time while ensuring proper citation and attribution.
Overview
Unofficial JAX implementations of deep learning research papers
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