Mobile iOS
Deploy your trained Roboflow model in your iOS app
The Roboflow Mobile iOS SDK is a great option if you are developing an iOS application where having a model running on the edge (iPad or iPhone) is needed for faster inference or to unlock a new suite of features, capabilities, and use cases (like augmented reality).
Native mobile applications with custom computer vision models embedded in them allows developers to give their apps the sense of sight.
The following task types are supported by the hosted API:
Task Type | Supported by iOS SDK Deployment |
---|---|
Object Detection | |
Classification | |
Instance Segmentation | |
Semantic Segmentation | |
All iOS devices support on-device inference, but those older than the iPhone 8 (A11 Bionic Processor) will fall back to the less energy efficient gpu engine.
Roboflow requires a minimum iOS version of 15.4.
You can develop against the Roboflow Hosted Inference API. It uses the same trained models as on-device inference.
"CocoaPods is built with Ruby and it will be installable with the default Ruby available on macOS. You can use a Ruby Version manager, however, we recommend that you use the standard Ruby available on macOS unless you know what you're doing. Using the default Ruby install will require you to use
sudo
when installing gems. (This is only an issue for the duration of the gem installation, though.)" - CocoaPodsThe "Sudo-less" installation is an option, if you do not want to grant RubyGems admin privileges for this process. However, note that the
sudo
installation is more typical.Check that CocoaPods is successfully installed by entering
pod --version
in your Terminal.First, run
pod init
in your project directory.Make sure the
Podfile
specifies the platform :ios, '15.4'
Next, add
pod 'Roboflow'
to your Podfile
.If you do not have the XCode Command Line Tools installed, run
xcode-select --install
in your Terminal.This will return:
xcode-select: error: command line tools are alreadyinstalled, use "Software Update" to install updates
if the Command Line Tools are already present on your system. 
Terminal after successful installation of the Podfile

The project directory after successful installation of the Podfile
- If it returns this error: "You may have encountered a bug in the Ruby interpreter or extension libraries," then first run
brew install cocoapods
, and then runpod install
and open the generated.xcworkspace
file in XCode.- Check that CocoaPods is successfully installed by entering
pod --version
in your Terminal.
Navigate to the
.xcworkspace
file in XCode.
Next, import Roboflow by adding
import Roboflow
. to the .xcworkspace
file.Then, create an instance of the Roboflow API with
let rf = Roboflow(apiKey: "API_KEY")
. For modelVersion
, replace YOUR-MODEL-VERSION-#
with the integer value of your model's version number.Completion Handler Usage:
import Roboflow
...
//initalize with your API Key
let rf = RoboflowMobile(apiKey: "API_KEY")
var model: RFObjectDetectionModel?
...
//model is your model's project name
rf.load(model: "YOUR-MODEL-ID", modelVersion: YOUR-MODEL-VERSION-#) { [self] model, error, modelName, modelType in
if error != nil {
print(error?.localizedDescription as Any)
} else {
model?.configure(threshold: threshold, overlap: overlap, maxObjects: maxObjects)
self.model = model
}
}
...
//model?.detect takes a UIImage and runs inference on it
let img = UIImage(named: "example.jpeg")
model?.detect(image: img!) { predictions, error in
if error != nil {
print(error)
} else {
print(predictions)
}
}
Asynchronous Usage:
To use asynchronously, you must be invoking your Roboflow model within an asynchronous block.
import Roboflow
...
//initalize with your API Key
let rf = RoboflowMobile(apiKey: "API_KEY")
...
//model is your model's project name
let (model, loadingError, modelName, modelType) = await rf.load(model: "YOUR-MODEL-ID", modelVersion: YOUR-MODEL-VERSION-#)
model!.configure(threshold: threshold, overlap: overlap, maxObjects: maxObjects)
...
//model?.detect takes a UIImage and runs inference on it
let img = UIImage(named: "example.jpeg")
let (predictions, predictionError) = await model!.detect(image: img!)
print(predictions)
Predictions Format:
x:Float //center of object x
y:Float //center of object y
width:Float
height:Float
className:String
confidence:Float
color:UIColor
box:CGRect
We also provide an example of integrating this SDK into an expo app with React Native here. You may find this useful when considering the construction of your own downstream application.
expo-cli
supports the following Node.js versions:>=12.13.0 <15.0.0
(Maintenance LTS) and>=16.0.0 <17.0.0
(Active LTS)- The yarn package must be installed for Node.js (
npm install -g yarn
)
Download CashCounter, our example iOS app that counts US coins and bills, as an example of how you could deploy a computer vision model to an iPhone. You'll see examples of visualizing bounding boxes, FPS, object counting, image upload, and more.
Last modified 3mo ago