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Answer: Increase the number of training epochs, add more images to the dataset, and apply geometric transformations.
To improve the model's performance, increasing the number of training epochs allows the model to learn more from the data. Adding more images to the dataset, especially if some classes are underrepresented, helps in achieving a balanced dataset. Applying geometric transformations like rotations, flips, and zooms can help in making the model more robust to variations in image orientation and perspective.
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Consider a scenario where you have trained a custom image classification model using Azure AI Vision. The model is intended to classify images of animals into different species. After training, you notice that the model's performance is not meeting the expected accuracy levels. What steps would you take to improve the model's performance, including potential adjustments to the training process and data augmentation techniques?
A
Increase the number of training epochs, add more images to the dataset, and apply geometric transformations.
B
Decrease the number of training epochs, remove some images from the dataset, and apply color adjustments.
C
Increase the number of training epochs, balance the dataset classes, and apply noise injection.
D
Decrease the number of training epochs, balance the dataset classes, and apply geometric transformations.
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