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Artificial intelligenceMachine learning
by Bryan Escatel, Max Katz, Peter Moskovits Published September 2, 2019
My name is Bryan Escatel, and I’m a senior at Menlo Atherton High School and an intern working with the Cognitive Applications team at IBM. Ever since I started at IBM, I’ve wanted to learn how to make and develop my own app. In the process of developing this app, I’ve had many ups and downs–and I struggled immensely at first. However, thanks to Upkar Lidder’s help, I created a visual recognition app that uses IBM Watson Visual Recognition service to analyze images, objects and other content.
Install the following:
The IBM Cloud Mobile services SDK uses CocoaPods to manage and configure dependencies.
Search “Terminal” on the search bar on you computer.
git clone the repo and cd into it by running the following command:
git clone github.com/IBM/watson-visual-recognition-ios.git &&
Run the following command to build the dependencies and frameworks:
carthage update --platform iOS
Note: Carthage can be installed with Homebrew.
Create the following services: Watson Visual Recognition. Copy the API Key from the credentials and add it to Credentials.plist
Launch Xcode using the terminal: open “Watson Vision.xcodeproj”
To run the simulator, select an iOS device from the dropdown and click the ► button
Now you’re able to click and drag photos into the photo gallery and select those photos from the app.
Since the simulator does not have access to a camera, and the app relies on the camera to test the classifier, you should run it on a real device. To do this, you’ll need to sign in the application and authenticate with your Apple ID:
Switch to the General tab in the project editor (The blue icon on the top left).
Under the Signing section, click Add Account.
After signing in with your Apple ID and password, you’ll need to create a certificate to sign your app (in the General tab) and follow the next few steps:
You’re now ready to use the app.
Example 1: The object scanned was a can. As you can see, IBM Watson detected a 67 percent probability of a can.
Example 2: The object scanned was a shoe.
As you can see, IBM Watson detected an 81 percent probability of a shoe.
Updating your app is fairly simple. All you need to do is teach and train your model. The app and model will update all at once when you push the train button so it’s all ready to go.
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