MediaPipe Vs ML Kit

MediaPipe Vs ML Kit: A Comparison of Pose Estimation Tools

Choose MediaPipe for:

Advanced Real-Time Media Processing:

If your application requires complex media processing in real-time, such as multi-hand tracking, advanced face detection, pose estimation, or augmented reality features, MediaPipe is well-suited due to its focus on performance and its range of pre-built models optimised for these tasks.

Cross-Platform Applications:

For applications that need to run across various platforms, including web, iOS, Android, and edge devices, MediaPipe’s cross-platform support makes it a compelling choice.

Custom Pipeline Requirements:

If you need to build custom processing pipelines for sensory data (like combining different types of detectors and trackers in unique ways), MediaPipe’s flexible framework allows for more customised integration and processing flows.

On-Device Inference:

Applications that require on-device processing without the need for server-side computation can benefit from MediaPipe’s optimised models and processing pipelines, ensuring low latency and high performance.

Choose ML Kit for:

Straightforward Integration with Mobile Apps:

If you’re developing an iOS or Android application that requires the integration of machine learning features like text recognition, barcode scanning, or face detection, ML Kit provides an easy-to-use API and pre-built models that can be quickly integrated into your mobile app.

Firebase Integration:

For projects already using Firebase, ML Kit seamlessly integrates with other Firebase services, making it an attractive choice for developers looking to leverage Google’s cloud capabilities alongside on-device machine learning.

Common ML Tasks:

If your application’s machine learning needs are covered by ML Kit’s pre-built models (such as text recognition, image labelling, or object detection), ML Kit offers a straightforward and efficient solution without the need for extensive machine learning expertise.

Custom TensorFlow Lite Models:

For developers who have custom TensorFlow Lite models and want to deploy them in their mobile applications, ML Kit supports the integration of these models, providing a pathway for custom machine learning tasks beyond the pre-built options.

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Key Features of MediaPipe and ML Kit:



ML Kit

Developed by



Open Source


Partial (Some components)

Platforms Supported

Web, iOS, Android, Edge devices

iOS, Android

Core Use Cases

Machine learning / computer vision capabilities for mobile developers

Machine learning / computer vision capabilities for mobile developers

Key Features

Pose Estimation, face detection and hand tracking.

Text recognition

Face detection

Barcode scanning

Image labelling 

Object detection and tracking

Custom Model Support

Generally used with provided models for specific tasks.

Yes, supports custom TensorFlow Lite models

Pre-trained Models

Comes with various pre-trained models for media processing tasks

Offers a variety of pre-trained models for ease of use in common mobile development tasks

Community & Support

Active community, GitHub issues, and discussions for support

Active community, Firebase support for ML Kit-specific issues

Integration Difficulty

May require more setup and configuration for specific use cases

Designed for ease of use and integration into existing Firebase projects

Real-time Performance

Highly optimised for real-time performance on various devices

Good performance, but specific to mobile devices and depends on the task

How QuickPose can be used

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