Introduction
Welcome to the official documentation for TensorFlowLiteObjC!
TensorFlowLiteObjC is a library that allows you to utilize TensorFlow Lite models in your iOS and macOS applications using Objective-C or Swift.
Features
- Simple and easy-to-use API
- Support for running TensorFlow Lite models on iOS and macOS devices
- Compatibility with both Objective-C and Swift
- Integration with Xcode for seamless development
- Support for various TensorFlow Lite model formats
- Efficient performance optimized for mobile devices
Getting Started
To get started with TensorFlowLiteObjC, follow these steps:
- Install TensorFlowLiteObjC using CocoaPods or integrate it manually into your Xcode project.
- Import the TensorFlowLiteObjC module in your code.
- Load a TensorFlow Lite model into your application.
- Perform inferences on input data using the loaded model.
Installation
To integrate TensorFlowLiteObjC using CocoaPods, add the following line to your Podfile:
“`ruby
pod ‘TensorFlowLiteObjC’
“`
Alternatively, you can manually integrate TensorFlowLiteObjC into your Xcode project by following the provided step-by-step guide on the official GitHub repository.
Importing the Library
To use TensorFlowLiteObjC in your code, import the TensorFlowLiteObjC module using the following statement:
“`swift
import TensorFlowLiteObjC
“`
Loading a Model
In order to load a TensorFlow Lite model into your application, you can use the following code snippet:
“`swift
let modelPath = Bundle.main.path(forResource: “model”, ofType: “tflite”)!
let model: TFLiteModel = try! TFLiteModel(modelPath: modelPath)
“`
Performing Inferences
Once the model is loaded, you can use it to perform inferences on input data. Here’s an example code snippet:
“`swift
let interpreter = try! TFLiteInterpreter(model: model)
try! interpreter.allocateTensors()
// Provide input data and run inferences
// …
// Retrieve output data from the interpreter
// …
“`
Additional Functionality
TensorFlowLiteObjC provides various additional functionality to enhance your machine learning applications, such as:
- Quantization support
- Input and output tensor details
- Error handling and debugging
- Model metadata extraction
For more detailed usage information and examples, refer to the official documentation and code samples on the official GitHub repository.
Conclusion
Congratulations! You are now equipped with the basic knowledge required to use TensorFlowLiteObjC and integrate TensorFlow Lite models into your iOS and macOS applications. Start exploring the possibilities and have fun building powerful machine learning features!