anomavision 3.0.27

Advanced Anomaly Detection Environment - Deep learning library for state-of-the-art anomaly detection algorithms

🚀 AnomaVision: Edge-Ready Visual Anomaly Detection

🔥 Lightweight, fast, and production-ready anomaly detection — powered by PaDiM. Deploy anywhere: edge devices, servers, or the cloud.

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✨ Features

• 🎯 State-of-the-art PaDiM algorithm
• ⚡ 3× faster inference than Anomalib (CPU benchmarks)
• 📦 Multi-backend exports: PyTorch, ONNX, TorchScript, OpenVINO, TorchRT
• 🎨 Visualizations: anomaly heatmaps, bounding boxes, ROC curves
• 🖥️ Unified Python + CLI workflows
• 🌍 Edge-first design with compact .pth models

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🆚 Why AnomaVision over Anomalib?

• ⚡ 3× faster inference on CPU (MVTec & Visa benchmarks)
• 📦 Smaller models (30 MB vs 40 MB) with lower memory usage
• 🎯 Higher AUROC across most classes on both MVTec AD and Visa datasets
• 🌍 Edge-first design → optimized for ONNX, TorchScript, and OpenVINO
• 🛡️ Production-ready with clean API, CLI, and deployment options

👉 See detailed results in Benchmarks.

👉 Download: AnomaVision vs Anomalib — A Comprehensive Performance Analysis (PDF)

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🖥️ C++ Inference with ONNX

AnomaVision isn’t just Python 🚀 — it also provides a C++ implementation for ONNX Runtime + OpenCV.

• ⚡ Real-time edge inference (~25 FPS on CPU)
• 🖼️ Full pipeline: preprocessing → inference → postprocessing → visualization
• 📦 Modular design (Config, Preprocessor, ONNXModel, Postprocessor, Visualizer, App)
• 🌍 Perfect for edge devices and production environments without Python

👉 See full guide: Quickstart — C++ Inference

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📚 Documentation

📖 Full docs are available in the /docs folder.

• Installation
• Quick Start
• CLI Reference
• API Reference
• Configuration Guide
• Benchmarks
• Contributing

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⚡ Quick Example

Train with a config file:

python train.py --config config.yml

## Save:
# Full model → padim_model.pt
# Compact stats-only model → padim_model.pth
# Config snapshot → config.yml

Run detection:

python detect.py --config config.yml

Evaluate performance:

python eval.py --config config.yml

Export to ONNX:

python export.py --config export_config.yml

➡ For more examples, see Quick Start.

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📊 Benchmarks (Summary)

MVTec AD (15 classes)

• Image AUROC: AV 0.85 ↑ vs AL 0.81
• Pixel AUROC: AV 0.96 ↑ vs AL 0.94
• FPS: AV 43 ↑ vs AL 13

Visa (12 classes)

• Image AUROC: AV 0.81 ↑ vs AL 0.78
• Pixel AUROC: AV 0.96 ↑ vs AL 0.95
• FPS: AV 45 ↑ vs AL 13

📊 Full tables & plots → Benchmarks

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🤝 Contributing

We welcome contributions!

• See Contributing Guide for high-level steps

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🙏 Acknowledgments

AnomaVision is built on top of the excellent Anodet repository. We thank the original authors for their contributions to open-source anomaly detection research, which laid the foundation for this work.

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📜 Citation

If you use AnomaVision in your research, please cite:

@software{anomavision2025,
  title={AnomaVision: Edge-Ready Visual Anomaly Detection},
  author={DeepKnowledge Contributors},
  year={2025},
  url={https://github.com/DeepKnowledge1/AnomaVision},
}

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💬 Community & Support

• 📢 Discussions
• 🐛 Issues
• 📧 [e-mail]

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👉 Start with Quick Start and build your first anomaly detection pipeline in 5 minutes!