Open-source software for next-generation AI monitoring in computer vision
Incorporate explainable AI methods, visual feature engineering, and advanced outlier detection, with comprehensive logging and powerful visualization tools.
Obz AI package features:
A comprehensive toolkit for monitoring, explaining, and improving computer vision systems with cutting-edge AI observability features.
Explainable AI (XAI)
Generate visual explanations for your computer vision models with state-of-the-art XAI techniques. Understand what your models see and why they make specific decisions.
Model Monitoring
Track model performance and system health in real-time. Get alerted to performance degradation before it impacts your users.
Outlier Detection
Automatically identify anomalous inputs, edge cases, and data quality issues that could affect your model performance.
Vision-First Design
Built specifically for computer vision workflows including CNNs, Vision Transformers (ViT), and other deep learning architectures.
PyTorch Integration
Seamlessly integrate with your existing PyTorch workflows. Works with your current training and inference pipelines.
Automated Logging
Connect to a web dashboard with just a few lines of code for quick visualization and analytics of your model behavior.
Getting Started
Get the obzai package up and running in your vision AI pipeline in minutes. Choose your integration path and start monitoring immediately.
Get started in seconds with pip
Need help getting started? Check out our comprehensive documentation.
Core Capabilities
Comprehensive tools for explainable AI, data quality monitoring, and production deployment
XAI Tools
Generate visual explanations for your computer vision models using attention maps, CDAM, and saliency techniques.
Data Inspector
Monitor input data quality, detect outliers, and ensure consistent model performance with advanced feature extraction.
Dashboard Integration
Connect to the Obz AI cloud platform for comprehensive monitoring and collaborative workflows.
XAI Evaluation
Assess the quality and reliability of your explainability methods with fidelity and compactness metrics.
Selected XAI Tools Comparison
Attention Map
CDAM
Saliency Map
XAI Regions
| XAI Method | ViT Models | CNN Models | Gradient-Based |
|---|---|---|---|
| Attention Map | |||
| CDAM | |||
| Saliency Map | |||
| XAI Regions |
All XAI tools support region-based analysis and can be evaluated with fidelity and compactness metrics.
Ready to Get Started?
Join the growing community of developers building transparent and reliable computer vision systems with the obzai package.
“AI engineers need better tools to ensure transparent and reliable computer vision systems. The obzai package makes it easy for developers to monitor, explain, and improve their vision models.”
Contributing to obzai
We welcome feedback and contributions from the community! Whether you're reporting issues, sharing feedback, or helping other users, your input helps make the obzai package better for everyone.
Community Guidelines
Follow these guidelines to ensure effective collaboration and maintain our community standards
Professional Communication
We maintain respectful and constructive communication in all interactions.
Detailed Reporting
Provide comprehensive information when reporting issues or suggesting features.
Quality Focus
Help us maintain high standards for documentation and user experience.
Collaborative Approach
Work together to build the best possible computer vision monitoring tools.
Questions or Need Help?
The Obz AI team is here to help you succeed with the obzai package. Don't hesitate to reach out with questions or feedback.