# 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](https://github.com/tensorflow/examples/blob/master/lite/examples/image_classification/ios/EXPLORE_THE_CODE.md).

This example app uses
[image classification](https://www.tensorflow.org/lite/models/image_classification/overview)
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](https://www.tensorflow.org/lite/performance/post_training_quantization)
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](https://www.tensorflow.org/lite/examples).

## Add TensorFlow Lite to your Swift or Objective-C project

TensorFlow Lite offers native iOS libraries written in
[Swift](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/swift)
and
[Objective-C](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/objc).
Start writing your own iOS code using the [Swift image classification example](https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios)
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

```ruby
use_frameworks!
pod 'TensorFlowLiteSwift'
```

#### Objective-C

```ruby
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 2.0.0, you can write the dependency as:

```ruby
pod 'TensorFlowLiteSwift', '~> 2.0.0'
```

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:

```ruby
pod 'TensorFlowLiteSwift', '0.0.1-nightly'
```

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](https://guides.cocoapods.org/using/the-podfile.html#specifying-pod-versions).

### Bazel developers

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

#### Swift

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

#### Objective-C

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

### Import the library

For Swift files, import the TensorFlow Lite module:

```swift
import TensorFlowLite
```

For Objective-C files, import the umbrella header:

```objectivec
#import "TFLTensorFlowLite.h"
```

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

```objectivec
@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`.
