BiaPy: Accessible deep learning on bioimages

BiaPy is an open source library and application that streamlines the use of common deep-learning workflows for a large variety of bioimage analysis tasks, including 2D and 3D semantic segmentation, instance segmentation, object detection, image denoising, single image super-resolution, self-supervised learning (for model pretraining), image classification and image-to-image translation.

BiaPy is a versatile platform designed to accommodate both proficient computer scientists and users less experienced in programming. It offers diverse and user-friendly access points to our workflows.

Find a comprehensive overview of BiaPy and its functionality in the following videos:

🚀 Get started

• Quick start
• Installation
• How it works
• Select workflow
• BioImage Model Zoo
• FAQ & Troubleshooting

⚙️ Workflow configuration

• Image classification
• Image denoising
• Image-to-image translation
• Instance segmentation
• Object detection
• Self-supervision
• Semantic segmentation
• Single image super-resolution

📘 Tutorials

• Classification
• Denoising
• Image to image
• Instance segmentation
• Self-supervision
• Semantic segmentation
• Object detection
• Super-resolution

🛠️ For developers

• YAML configuration
• Library use
• API Reference
• Differences between CPU and GPU execution
• How to contribute

📖 Citation

Please note that BiaPy is based on a publication. If you use it successfully for your research please be so kind to cite our work:

Franco-Barranco, D., Andrés-San Román, J.A., Hidalgo-Cenalmor, I.,
Backová, L., González-Marfil, A., Caporal, C., Chessel, A., Gómez-Gálvez, P.,
Escudero, L.M., Wei, D., Muñoz-Barrutia, A. and Arganda-Carreras, I.
BiaPy: accessible deep learning on bioimages. Nat Methods 22, 1124-1126 (2025).
https://doi.org/10.1038/s41592-025-02699-y