Bulletproof Neural Network Recipe
BNNR automatically improves your PyTorch vision models using XAI — find what your model gets wrong, fix it with intelligent augmentation, and prove the result with structured reports and a live dashboard.
Zero flags: python -m bnnr demo for a ~1 min CIFAR-10 run with live dashboard, or python -m bnnr quickstart for the interactive wizard — getting started.
pip install "bnnr[dashboard]"Original demo recording with audio (4K, ~53s) — press Play
XAI-driven augmentations
ICD and AICD use saliency maps to guide augmentation — not random flips and crops. See interactive before/after previews on the playground.
Intelligent Coarse Dropout
Masks high-saliency regions the model already relies on, forcing it to learn from surrounding context instead of shortcuts.
Anti-ICD
Masks low-saliency background and irrelevant textures, sharpening focus on the features that actually discriminate classes.
How BNNR works
A closed-loop pipeline that trains, diagnoses, repairs, and proves — so you ship models that actually work in production.
Train your model
Start with your PyTorch model and data. BNNR trains a baseline, then iteratively evaluates candidate augmentations — keeping only those that measurably improve performance.
Understand with XAI
OptiCAM, GradCAM, NMF, and CRAFT saliency maps reveal what the model focuses on. Per-class diagnoses expose blind spots and biases invisible to accuracy alone.
Fix with ICD / AICD
Intelligent Coarse Dropout (ICD) masks salient regions, forcing the model to learn from context. Anti-ICD (AICD) sharpens focus on key features. Both are XAI-driven and automatic.
Prove with evidence
A structured report with metrics, XAI heatmaps, branch decisions, and before/after comparisons. Shareable, auditable, and ready for stakeholders or regulatory review.
Everything you need for better models
BNNR combines XAI diagnostics, intelligent augmentation, and real-time monitoring into a single, cohesive toolkit.
Auto-Augment Search
Iterative branching strategy tests augmentations against a baseline, keeping only those that improve performance. No manual tuning needed.
Image Classification
End-to-end workflow from baseline training to stakeholder-ready reports, metrics, and dashboard views.
Object Detection
Full detection pipeline with YOLO and torchvision models (Faster R-CNN, RetinaNet, SSD). Bbox-aware augmentations, detection ICD/AICD, mAP metrics, and XAI saliency.
XAI Explainability
OptiCAM, GradCAM, NMF, and CRAFT heatmaps show why the model improves. Per-class diagnoses with severity and trend analysis.
ICD & AICD Augmentations
XAI-driven augmentations that use saliency maps to intelligently mask or focus regions. Forces the model to learn robust features instead of shortcuts.
8 Novel Augmentations
Unique texture-rich transforms: ChurchNoise, TeaStains, LuxferGlass, ProCAM, DifPresets, Smugs, Drust, and BasicAugmentation — designed for real-world domains.
GPU-Native Speed
ChurchNoise, ProCAM, DifPresets, ICD, and AICD run natively on CUDA tensors. Kornia integration for differentiable augmentations.
Real-time Dashboard
Live monitoring with 512×512 high-res previews, branch decision trees, per-class metrics, and XAI insights. Accessible from your phone via QR code.
Auditable Reports
Structured JSON reports with metrics, XAI heatmaps, and augmentation decisions. Export static dashboards for stakeholder review or regulatory compliance.
Simple yet powerful API
Start from the CLI with python -m bnnr demo, or plug BNNR into your existing PyTorch training loop with quick_run.
# Zero flags — CIFAR-10 demo, ICD preset, live dashboard (~1 min)
python -m bnnr demo
# Interactive wizard (dataset, preset, sample limits)
python -m bnnr quickstart
# Full training with built-in defaults
python -m bnnr train --dataset cifar10 --preset light --with-dashboardNovel augmentations
Beyond standard flips and crops — texture-rich transforms and XAI-driven augmentations that force your model to learn better features.