How CoreML Can Improve iOS Machine Learning Abilities

Mobile application developers today are leveraging modern, robust tools to improve the app development process. They continuously look for compelling strategies to build interactive apps that can engage more with the users and provide personalized experiences. Machine Learning (ML) algorithms are one of such ways that offer innovative and exciting possibilities in the iOS app development.

At Apple WWDC (Worldwide Developer Conference) in 2017, a new machine learning framework called CoreML was announced. It can make it easier to integrate machine learning models in iOS applications.

Businesses across domains like education, healthcare, eCommerce, and more can leverage this CoreML framework to improve their app performance, While developers can create advanced mobile apps by writing a few lines of code.

Let’s look at how machine learning can influence iOS mobile app development with its CoreML capabilities.

Implementing Machine Learning Models in iOS App Development

The CoreML framework makes it easy to implement machine learning models in iOS applications. It helps app developers to execute a specific type of processes for decision-making and data predictions.

However, developers need pre-trained models to integrate them into the app development practices to make efficient predictions through apps.

Let’s take a use case to showcase the influence of mobile apps using ML.

Image Classification Use Case

Here, we would train the ML model to classify pictures using two categories ferrets and hamsters. For this, we need to create a directory Image_Classification’ where more than 1000 images are stored, along with two separate directories training’ and test’ to train and test the model. The naming convention plays a crucial role here in identifying pictures and filter them as per the name of the directories.

Now, follow the steps below:

1. Open Xcode and create a new playground
2. When asked, select macOS’ and Single View’ options
3. Delete all the existing content and write the below code

Import CreateMLUI

Let builder = MLImageClassifierBuilder()

Builder.showInlineview()

1. Run the playground
2. Open Assistant Editor
3. In the interface, drag and drop the training’ directory where the message is displayed Drop Images to Begin Training’

The above process makes the Core ML model to work on the images. Remember that the pictures you have uploaded in the directory should have the same features to make the model work properly.

Once the model identifies the image features, it works to classify them under the possible categories – ferrets and hamsters in our case. This process is called Logistic Regression,’ and it does not take much time to complete.

After the process is over, a report card is generated. It shows the model accuracy in two metrics;

1. Training– It shows the accuracy of the model.
2. Validation– Here, the accuracy of the model is shown for identifying random images. Such images have irrelevant features and are not part of the training process.

Below that, you can see the message displaying Drop Images To Begin Testing.’ Here, drag and drop the images for testing. The trained model would automatically start working on the images and classify them.

You can see the output as below on your screen:

Number of examples: 340

Number of classes: 2

Accuracy: 98.69%

Confusion Matrix

True/Pred	Ferret	Hamster
Ferret	168	2
Hamster	2	168

Precision Recall

Class	Precision (%)	Recall (%)
Ferret	98.69	98.69
Hamster	98.69	98.69

Here, the confusion matrix shows that the two images of hamsters were classified as ferrets incorrectly and vice versa. Remember, you might get slightly different results than this because random images were chosen within the training’ directory.

Once all the processes are done, click on the arrow at the top left of the view and follow the instructions to save the model.

Embedding ML Model in the iOS App

When you identify the working of the model correctly, it is the time to integrate it into the iOS app. Capture the video stream directly from the iPhone camera and upload it within the model. It will then classify the objects as ferret or hamster. Open the directory FerretHamsterClassifier to check everything is implemented correctly.

Remember that we have trained our model to identify ferrets and hamsters only. So, even if it is classifying your phone or chair as a ferret or hamster, it is working correctly. You can train this model with as many objects as you want by following the process mentioned above.

Other Use Cases

Similar to the image classification, you can train the ML model for an image to text conversion, real-time text analysis, and many different use cases and implement them in your iOS app development practices. Along with Core ML, other machine learning frameworks like Create ML, NLP, Vision, and more can help you to improve the application development process for various industries.

Conclusion

Mobile users are not fully aware, or they do not worry about how machine learning is influencing the apps they use daily on their devices. However, almost all of the smartphone applications leverage the capability of ML to function in a more personalized way. Therefore, iOS mobile development companies and developers must consider the modern, efficient, and smart approaches to improve mobile application development with machine learning capabilities.