MoodTracker: Creating and Evaluating Emotion Classification Models
In this demo, you’ll work on building emotion classification models for the MoodTracker app. The objective is to create three image classifiers: one with default settings using a dataset with two labels, one with all augmentations enabled for the same two-label dataset, and a third using a three-label dataset with all augmentations. This approach will help clarify the effects of augmentations and additional labels on model accuracy.
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Building the Image Classifiers
Open Xcode and then select “Xcode” > “Open Developer Tool” > “Create ML” to access the Create ML app. Click “New Document” or navigate to “File” > “New” > “Project”. Select the “Image Classification” template and proceed by clicking “Next”. Name it EmotionsImageClassifier and choose the save location. The Create ML app will now display the three main parts as seen previously.
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Evaluating Model Performance
After training the models, evaluate their performance by checking metrics and testing with real data in the preview section. Open the “Evaluation” tab of the second classifier and press “Testing” to review the results for the testing data. You can see the test accuracy and other statistics, including the lowest precision type.
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This content was released on Sep 18 2024. The official support period is 6-months
from this date.
In this demo, you’ll go step-by-step into creating image classification models for
the MoodTracker app using Create ML. By the end, you’ll be ready to integrate the model
into the MoodTracker app for real-time emotion detection from user-uploaded or camera-taken photos.
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