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In your BUILD file, add the TensorFlowLite dependency to your target. If you wish to update the nightly library to the newer one, youįor more information on different ways of specifying version constraints, see That once the Podfile.lock file is created when you run pod install commandįor the first time, the nightly library version will be locked at the currentĭate's version. This will allow you to use the latest features added to TensorFlow Lite. Specifying subspec: pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly', :subspecs => Alternatively, if you want to depend on the nightlyīuilds, you can write: pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly'įrom 2.4.0 version and latest nightly releases, by defaultĪre excluded from the pod to reduce the binary size. This will ensure the latest available 2.x.y version of the TensorFlowLiteSwift Version 2.10.0, you can write the dependency as: pod 'TensorFlowLiteSwift', '~> 2.10.0' You can also specify a version constraint. Version constraint as in the above examples, CocoaPods will pull the latest TensorFlowLiteSwift and TensorFlowLiteObjC pods. There are stable releases, and nightly releases available for both In your Podfile, add the TensorFlow Lite pod.

UNLEASH THE LIGHT DOWNLOAD THE LAST VERSION FOR IPHONE HOW TO

The sections below demonstrate how to add TensorFlow Lite Swift or Objective-C Start writing your own iOS code using the TensorFlow Lite offers native iOS libraries written in Add TensorFlow Lite to your Swift or Objective-C project Note: Additional iOS applications demonstrating TensorFlow Lite in a variety of Model and select the number of threads to perform inference on. To continuously classify whatever it sees from the device's rear-facing camera,ĭisplaying the top most probable classifications.

TensorFlow Lite iOS image classification. To get started with TensorFlow Lite on iOS, we recommend exploring the followingįor an explanation of the source code, you should also read

Unleash the Light download the last version for iphone