About
Magika leverages deep learning to accurately detect a comprehensive range of file content types. It outperforms traditional detection tools by achieving over 99% precision and recall. The tool operates efficiently, scanning millions of files per second and provides results entirely in the user's browser for privacy, not uploading files elsewhere. It is designed for easy integration into Python and JavaScript environments, and supports command-line usage, providing a versatile solution for developers and analysts in need of reliable content type classification. A detailed paper outlining its training and performance metrics will be released soon to further understand its capabilities.
Competitive Advantage
Magika provides unmatched precision and recalls in file type detection using a deep learning approach, operating effectively even on a single CPU.
Use Cases
Pros
- High accuracy and precision
- Runs efficiently on a single CPU
- Works entirely in the browser for privacy
- Supports multiple programming environments
Cons
- Limited to non-polyglot files
- No real-time monitoring
- Complexity in handling diverse content types
- Reliance on deep learning model size
Tags
Pricing
Features and Benefits
Deep Learning for Content Detection
Uses advanced deep learning models to accurately classify and detect file types, ensuring high precision and recall.
Browser-Based Processing
Allows users to process files directly in their browser without needing to upload them to a server, ensuring file privacy.
Multi-Environment Integration
Supports integration with Python and JavaScript environments, providing flexibility for developers.
Target Audience
Frequently Asked Questions
Magika is a tool that uses deep learning to detect common file content types with high accuracy.
Magika boasts over 99% precision and recall in content type detection.
No, Magika processes files entirely in the user's browser.
You can integrate Magika as a Python package or use its JavaScript library.
Yes, you can use Magika from the command line after installing it via pip.
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