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Question 173 - MLS-C01 discussion

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A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of

1,000 hand-labeled images covering 10 distinct items. The training results were poor.

Which machine learning approach fulfills the company's long-term needs?

A.
Convert the images to grayscale and retrain the model
Answers
A.
Convert the images to grayscale and retrain the model
B.
Reduce the number of distinct items from 10 to 2, build the model, and iterate
Answers
B.
Reduce the number of distinct items from 10 to 2, build the model, and iterate
C.
Attach different colored labels to each item, take the images again, and build the model
Answers
C.
Attach different colored labels to each item, take the images again, and build the model
D.
Augment training data for each item using image variants like inversions and translations, build the model, and iterate.
Answers
D.
Augment training data for each item using image variants like inversions and translations, build the model, and iterate.
Suggested answer: D

Explanation:

Data augmentation is a technique that can increase the size and diversity of the training data by applying various transformations to the original images, such as inversions, translations, rotations, scaling, cropping, flipping, and color variations. Data augmentation can help improve the performance and generalization of image classification models by reducing overfitting and introducing more variability to the data. Data augmentation is especially useful when the original data is limited or imbalanced, as in the case of the company's problem. By augmenting the training data for each item using image variants, the company can build a more robust and accurate model that can recognize the items on the store shelves from different angles, positions, and lighting conditions. The company can also iterate on the model by adding more data or fine-tuning the hyperparameters to achieve better results.

References:

Build high performing image classification models using Amazon SageMaker JumpStart

The Effectiveness of Data Augmentation in Image Classification using Deep Learning

Data augmentation for improving deep learning in image classification problem

Class-Adaptive Data Augmentation for Image Classification

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asked 16/09/2024
Jorge Rojas Gallegos
30 questions
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