iOS quickstart

To get started with TensorFlow Lite on iOS, we recommend exploring the following example:

<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios">iOS image classification example</a>

For an explanation of the source code, you should also read TensorFlow Lite iOS image classification.

This example app uses image classification to continuously classify whatever it sees from the device's rear-facing camera, displaying the top most probable classifications. It allows the user to choose between a floating point or quantized model and select the number of threads to perform inference on.

Note: Additional iOS applications demonstrating TensorFlow Lite in a variety of use cases are available in Examples.

Add TensorFlow Lite to your Swift or Objective-C project

TensorFlow Lite offers native iOS libraries written in Swift and Objective-C. Start writing your own iOS code using the Swift image classification example as a starting point.

The sections below demonstrate how to add TensorFlow Lite Swift or Objective-C to your project:

CocoaPods developers

In your Podfile, add the TensorFlow Lite pod. Then, run pod install.

Swift

use_frameworks!
pod 'TensorFlowLiteSwift'

Objective-C

pod 'TensorFlowLiteObjC'

Specifying versions

There are stable releases, and nightly releases available for both TensorFlowLiteSwift and TensorFlowLiteObjC pods. If you do not specify a version constraint as in the above examples, CocoaPods will pull the latest stable release by default.

You can also specify a version constraint. For example, if you wish to depend on version 2.10.0, you can write the dependency as:

pod 'TensorFlowLiteSwift', '~> 2.10.0'

This will ensure the latest available 2.x.y version of the TensorFlowLiteSwift pod is used in your app. Alternatively, if you want to depend on the nightly builds, you can write:

pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly'

From 2.4.0 version and latest nightly releases, by default GPU and Core ML delegates are excluded from the pod to reduce the binary size. You can include them by specifying subspec:

pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly', :subspecs => ['CoreML', 'Metal']

This will allow you to use the latest features added to TensorFlow Lite. Note that once the Podfile.lock file is created when you run pod install command for the first time, the nightly library version will be locked at the current date's version. If you wish to update the nightly library to the newer one, you should run pod update command.

For more information on different ways of specifying version constraints, see Specifying pod versions.

Bazel developers

In your BUILD file, add the TensorFlowLite dependency to your target.

Swift

swift_library(
  deps = [
      "//tensorflow/lite/swift:TensorFlowLite",
  ],
)

Objective-C

objc_library(
  deps = [
      "//tensorflow/lite/objc:TensorFlowLite",
  ],
)

C/C++ API

Alternatively, you can use C API or C++ API

# Using C API directly
objc_library(
  deps = [
      "//tensorflow/lite/c:c_api",
  ],
)

# Using C++ API directly
objc_library(
  deps = [
      "//tensorflow/lite:framework",
  ],
)

Import the library

For Swift files, import the TensorFlow Lite module:

import TensorFlowLite

For Objective-C files, import the umbrella header:

#import "TFLTensorFlowLite.h"

Or, the module if you set CLANG_ENABLE_MODULES = YES in your Xcode project:

@import TFLTensorFlowLite;

Note: For CocoaPods developers who want to import the Objective-C TensorFlow Lite module, you must also include use_frameworks! in your Podfile.