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tensorflowliteswift

Introduction

Welcome to the documentation for TensorFlowLiteSwift! This library provides Swift bindings for TensorFlow Lite, an open-source deep learning framework for on-device inference.

Installation

CocoaPods

To install TensorFlowLiteSwift using CocoaPods, add the following line to your Podfile:

“`ruby
pod ‘TensorFlowLiteSwift’
“`

Then run the command:

“`bash
pod install
“`

Using Swift Package Manager

You can also use Swift Package Manager to add TensorFlowLiteSwift to your project. Simply add the package dependency in your Package.swift file:

“`swift
dependencies: [
.package(url: “https://github.com/tensorflow/swift.git”, .branch(“tensorflow-0.17.0”)),
]
“`

Usage

Importing TensorFlowLiteSwift

First, import the TensorFlowLiteSwift module into your Swift file:

“`swift
import TensorFlowLiteSwift
“`

Loading a TensorFlow Lite Model

To use a TensorFlow Lite model, you need to load it first:

“`swift
guard let modelPath = Bundle.main.path(forResource: “my_model”, ofType: “tflite”) else {
fatalError(“Model not found”)
}

do {
let model = try Interpreter(modelPath: modelPath)
} catch {
fatalError(“Failed to load model: \(error)”)
}
“`

Running Inference

With a loaded model, you can perform inference by providing input data and retrieving the output:

“`swift
guard let inputData = … // Prepare your input data as a suitable input representation for the model

do {
try model.invoke(inputData: inputData)

let outputData = try model.outputData

// Process the output data as needed
} catch {
fatalError(“Error during inference: \(error)”)
}
“`

API Reference

Below, you will find the list of available classes and methods in the TensorFlowLiteSwift library:

  • Interpreter
  • Interpreter.Options
  • Tensor
  • DataType

Examples

Here are some examples demonstrating the usage of TensorFlowLiteSwift:

  • Performing image classification using a pre-trained model
  • Running object detection on a live camera feed
  • Using a custom TensorFlow Lite model trained for a specific task

Conclusion

Congratulations! You should now have a good understanding of how to use TensorFlowLiteSwift in your Swift projects. Enjoy exploring the capabilities of TensorFlow Lite and building powerful machine learning applications! If you encounter any issues or have further questions, please refer to the official TensorFlowLiteSwift documentation or community forums for assistance.