PoseEstimation-CoreML

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PoseEstimation-CoreML is an Open Source & Models tool that enables real-time pose estimation on iOS devices using Core ML. It allows developers to infer body poses from camera feeds or images.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is PoseEstimation-CoreML?

PoseEstimation-CoreML is an open-source project designed for inferencing pose estimation on iOS devices utilizing Apple's Core ML framework. This tool allows developers to estimate body poses from still images and real-time video feeds captured by the device's camera. Key features include visualizing poses as heatmaps or lines and points, and the ability to capture and match poses. It supports various models like cpm and hourglass, providing performance metrics across different iPhone models. The project offers clear instructions for integrating models, handling camera permissions, and performing inferences using the Vision framework, making it a valuable resource for iOS machine learning development.

Best used for

Ideal for developers who need to integrate real-time pose estimation into iOS applications, experiment with Core ML models, and visualize human body keypoints. Especially valuable for creating interactive experiences, fitness tracking, or augmented reality applications on mobile devices.

Common actions

implement pose estimation

develop iOS ML apps

integrate Core ML models

analyze human movement

"AI Agents"face swappingcollaborationopen-sourcedeepfakeworkflowsautomated workflowlow-code/no-codegithub copilot

Capabilities

Key features

Real-time pose inference
Still image pose estimation
Visualize as heatmaps
Visualize as lines
Pose capturing
Pose matching

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for running PoseEstimation-CoreML?

To run PoseEstimation-CoreML, you need Xcode 9.2 or later and iOS 11.0 or later. The project is built using Swift 4. These requirements ensure compatibility with the Core ML framework and the necessary development tools for iOS applications.

Can I use my own pose estimation model with this project?

Yes, the project is designed to allow you to use your own custom pose estimation models. You will need to import your model into the project, and the compiler will automatically generate a model helper class for easy access and integration within your code.

How does PoseEstimation-CoreML perform on different iOS devices?

Performance varies by device and model. For instance, on an iPhone 11 Pro, the cpm model has an inference time of 5ms and an FPS of 15, while on an iPhone 6S+, it's 44ms inference time and 6 FPS. Detailed performance tables are available in the project's README.

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