Full demo with audio (4K): bnnr.dev
BNNR (Bulletproof Neural Network Recipe)
Train → Explain → Improve → Prove
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.
Supported tasks (v0.7.0): single-label classification, multi-label classification, and object detection (COCO-mini / YOLO). See Documentation.
Sample analyze report (no install): live HTML preview
Quickstart
pip install "bnnr[dashboard]"
# Zero flags — CIFAR-10 demo CNN, ICD preset, live dashboard (~1 min)
python3 -m bnnr demo
python3 -m bnnr quickstart
python3 -m bnnr train --dataset cifar10 --preset light --with-dashboard
Open http://127.0.0.1:8080/ for the live dashboard.
Already have a checkpoint? python3 -m bnnr analyze --model checkpoints/best.pt --data cifar10 --output ./analysis_out — docs.
XAI-driven augmentations (ICD & AICD)
BNNR uses saliency maps to guide augmentation — not random flips and crops.
ICD — masks the regions the model already focuses on (highest saliency), forcing it to learn from context instead of shortcuts.
AICD — masks low-saliency background and irrelevant textures, sharpening focus on discriminative features.
Benchmarks
| Dataset | Without BNNR | BNNR branch search | RandAugment |
|---|---|---|---|
| CIFAR-10 | 75.3% | 81.4% | 72.5% |
Median val accuracy, 3 seeds, demo CNN (methodology).
Live dashboard
Real metrics from a BNNR training run — branch tree, charts, XAI previews, and dataset insights.
| Overview | Branch Tree | Metrics |
|---|---|---|
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| Samples & XAI | Analysis | Dataset Insight |
|---|---|---|
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What makes BNNR different
- XAI-driven augmentation (ICD / AICD) — augmentations guided by saliency maps; no other PyTorch toolkit combines explainability and data augmentation this way.
- Auto-augmentation search — iterative branching keeps only augmentations that measurably improve your validation metric.
- Auditable reports — structured JSON reports with metrics, XAI heatmaps, and branch decisions for stakeholders or compliance review.
Links
| Resource | URL |
|---|---|
| Website | bnnr.dev |
| BNNR on LinkedIn | |
| Documentation | docs/README.md |
| Examples | docs/examples.md |
| Roadmap (Q2–Q3 2026) | docs/roadmap.md |
| Colab (classification) | Open in Colab |
| API reference | docs/api_reference.md |
Model analysis (bnnr analyze) |
docs/analyze.md |
| Integrations (Grad-CAM, Ultralytics YOLO) | docs/integrations.md |
| Sample analyze report (live HTML) | raw.githack.com preview |
| GitHub Discussions | Q&A and showcase |
Python API
import bnnr
result = bnnr.quick_run(model, train_loader, val_loader)
print(result.best_metrics)
Advanced: Golden path and API reference.
Documentation
Install from source, CLI reference, full doc index
Install from source
git clone https://github.com/bnnr-team/bnnr.git
cd bnnr
(cd dashboard_web && npm ci && npm run build)
pip install -e ".[dev,dashboard]"
The PyPI wheel ships the bnnr package only. Runnable scripts (examples/), notebooks, and the documentation tree (docs/) live in this repository.
Main CLI commands
python3 -m bnnr --help
python3 -m bnnr train --help
python3 -m bnnr analyze --help
python3 -m bnnr report --help
python3 -m bnnr list-datasets
python3 -m bnnr list-augmentations -v
python3 -m bnnr list-presets
python3 -m bnnr dashboard serve --run-dir reports --port 8080
python3 -m bnnr dashboard export --run-dir reports/run_YYYYMMDD_HHMMSS --output exported_dashboard
Doc index
- Getting started
- Configuration
- CLI
- Dashboard
- Augmentations
- Detection
- Analyze (standalone diagnostics)
- Examples
- Notebooks
- Artifacts
- Troubleshooting
Requirements
- Python
>=3.10 - Core:
torch,torchvision,numpy,typer,pydantic,pyyaml,grad-cam - Dashboard extra:
fastapi,uvicorn,websockets,qrcode
Citation
- BNNR software: software entry — DOI 10.5281/zenodo.20581372
- ICD or AICD: method paper — DOI 10.5281/zenodo.20581077
Full BibTeX: docs/citation.md. GitHub: Cite this repository via CITATION.cff.
License
MIT License — use BNNR freely in research, production, and commercial projects.
1 maintainer
Release files for bnnr 0.7.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| bnnr-0.7.0.tar.gz | 14.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| bnnr-0.7.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 15.9 MB
Release files / bnnr-0.7.0.tar.gz
| Download URL | bnnr-0.7.0.tar.gz |
|---|---|
| Size | 14.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Download URL | bnnr-0.7.0-py3-none-any.whl |
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| Size | 1.9 MB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 9, 2026.
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