Magika is a deep learning-based tool for detecting and classifying various file content types. Developed by Google, it's designed to outperform traditional file type detection tools by providing enhanced accuracy across a broad range of content types.
Magika is designed for efficiency, allowing for quick operation even on a single CPU. Users can test out Magika's capabilities from their browser. Uploaded files remains secure as the processing is entirely performed browser-side with no uploads to external servers.
A unique feature of Magika is its installability as a Python package, allowing users to run it readily from their command line. It can also be leveraged in Python or JavaScript codebases, making it a versatile tool in a developer's kit.
Magika is a game-changer that allows precise file content type detection with comprehensive support including language-specific files, executables, document types, image and video data, and audio bitstream data, among others.
Reports indicate that a similar version of Magika is in use at Google, scanning millions of files per second for accurate content-type tagging. Plans are underway to release a detailed paper explaining how Magika was trained and its performance on large datasets.Despite its capabilities, users should note that Magika is designed to output a single content type for a file, therefore polyglot files will not be mapped to two or more categories.
Despite this, it remains a powerful tool in content type detection using deep learning. For users wanting to cite Magika, a citation guide is available on the project's GitHub page.

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Pros & Cons
Outperforms traditional tools
Enhanced accuracy
Efficient operation
Operates on single CPU
Browser-side file processing
High file security
Installs as Python package
Command-line operation
Python or JavaScript integration
Comprehensive file type support
Scans millions files/second
Language-specific file support
Executable, document, image, video support
Audio bitstream data support
99%+ average precision
99%+ average recall
Demo option in browser
Detailed performance paper
Citable with citation guide
Faster file-type identification
Commands to install
Example outputs provided
JavaScript library usage
Single content output
Model details disclosed
Model owners clarified
Detailed performance metrics
Limitations specified
Use cases identified
Outputs file total size
Content type probability displayed
Outputs individual file precision
Outputs individual file recall
Detailed quantitative analysis
Can process large datasets
Designed for developer usage
Deep learning-based precision
Output compatible with data tagging
Can process polyglot files
Comprehensive support for executable types
Scaled successfully at Google
Optimized for Python and JavaScript
Processed in client-side browser
Consistently updated and maintained
Fast even on single CPU
Handles document files effectively
Support for audio and video data
Recognizes language-specific files
Single content-type output limitation
Browser-side-only processing
No support for external servers
Lack of detailed training documentation
Python and JavaScript only
Alternatives
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