About BNNR
The team, the mission, and the technology behind Bulletproof Neural Network Recipe.
What is BNNR?
BNNR (Bulletproof Neural Network Recipe) is an open-source toolkit that makes PyTorch vision models production-ready through a closed-loop pipeline: Train → Explain → Improve → Prove.
BNNR trains a baseline model, explains its decisions with XAI, iteratively improves it with intelligent augmentation, and proves the result with structured reports and a live dashboard. Only changes that measurably improve performance are kept — no guesswork, no manual tuning.
What makes BNNR unique is its combination of novel augmentations (ChurchNoise, ProCAM, DifPresets, and more), XAI explainability (OptiCAM heatmaps showing why the model improves), XAI-driven augmentations (ICD and AICD that use saliency maps to intelligently modify training images), and a real-time dashboard for monitoring the entire pipeline.
BNNR supports single-label and multi-label classification, as well as object detection (YOLO, Faster R-CNN, RetinaNet, SSD), with task-appropriate metrics and the same train–explain–improve loop.
Team
Mateusz Walo
Founder & Lead Developer
Architect behind BNNR's core engine, XAI pipeline, and model improvement loop. Passionate about making neural networks more robust and explainable.
Diana Morzhak
Software Developer & QA Engineer
Responsible for feature development, quality assurance, and end-to-end testing — ensuring reliability across classification and detection workflows.
Dominika Zydorczyk
Marketing & Communications Specialist
Drives community outreach, content strategy, and brand presence for BNNR across social channels and developer communities.
Zuzanna Saczuk
Graphic Designer & Brand Lead
Creator of BNNR's visual identity — from the molecular logo and neon branding to UI design and all visual assets.
Tech Stack
MIT License
BNNR is free and open-source software released under the MIT License. Use it freely in personal and commercial projects. Contributions are always welcome.