Magika | Awesome Repository

Magika is an AI content type classifier and MIME type prediction engine that uses deep learning to identify file formats based on binary data. It analyzes byte sequences through a neural network to predict the content type of a file and provide associated confidence scores.

The system features a foreign function interface that allows the core detection logic to be integrated across different programming languages. It includes a mechanism for configuring detection sensitivity and per-type thresholds to balance precision and recall.

The project provides capabilities for bulk file analysis via recursive directory scanning and security content inspection. It supports the loading of model assets from local paths or remote URLs and includes a utility to list all supported content type labels.

Features

Content Type Detection - Identifies the content type of files using a deep learning model to return accurate MIME types and confidence scores.
Deep Learning Classifiers - Uses a neural network to classify file types and predict MIME labels with associated confidence scores.
File Type Validators - Provides accurate MIME types and confidence scores by identifying the content type of files via deep learning.
MIME Type Predictors - Predicts MIME types from file bytes using a deep learning model.

Features

Content Type Detection - Identifies the content type of files using a deep learning model to return accurate MIME types and confidence scores.
Deep Learning Classifiers - Uses a neural network to classify file types and predict MIME labels with associated confidence scores.
File Type Validators - Provides accurate MIME types and confidence scores by identifying the content type of files via deep learning.
MIME Type Predictors - Predicts MIME types from file bytes using a deep learning model.