Image by @miamiamia0103

Autoencoders have proven successful across diverse applications such as data reconstruction, anomaly detection, and feature extraction, however, these advancements remain largely dispersed among various fields, lacking a cohesive tool that unites them. Rapidae is an open-source Python library created to facilitate the use, development, and benchmarking of autoencoder models. Through a simple environment, it is suitable for educational purposes yet flexible to meet research needs. Rapidae is mainly oriented to image and time-series research, in the form of precise and imprecise data and is designed to be backend-agnostic, supporting a seamless transition between TensorFlow, PyTorch, and JAX, thus accommodating a broad range of user preferences and existing workflows. In addition to its computational flexibility, Rapidae offers a graphical user interface, which lowers the barrier to entry for new learners and enhances the productivity of seasoned practitioners.

🚨Call for contributions🚨

If you want to add your model to the package or collaborate in the package development feel free to shoot me a message at costanahuel@uniovi.es or just open an issue or a pull request. I´ll be happy to collaborate with you.

Nahuel Costa
Nahuel Costa
Machine Learning & AI researcher