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Question 269 - MLS-C01 discussion

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An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.

Which steps would improve the accuracy of the solution? (Choose three.)

A.
Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.
Answers
A.
Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.
B.
Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.
Answers
B.
Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.
C.
Augment the images in the dataset. Use open-source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.
Answers
C.
Augment the images in the dataset. Use open-source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.
D.
Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.
Answers
D.
Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.
E.
Use Amazon Rekognition Custom Labels to train a new model.
Answers
E.
Use Amazon Rekognition Custom Labels to train a new model.
F.
Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.
Answers
F.
Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.
Suggested answer: C, E, F

Explanation:

Option C is correct because augmenting the images in the dataset can help the model learn more features and generalize better to new products. Image augmentation is a common technique to increase the diversity and size of the training data.

Option E is correct because Amazon Rekognition Custom Labels can train a custom model to detect specific objects and scenes that are relevant to the business use case. It can also leverage the existing models from Amazon Rekognition that are trained on tens of millions of images across many categories.

Option F is correct because class imbalance can affect the performance and accuracy of the model, as it can cause the model to be biased towards the majority class and ignore the minority class. Applying oversampling or undersampling can help balance the classes and improve the model's ability to learn from the data.

Option A is incorrect because the semantic segmentation algorithm is used to assign a label to every pixel in an image, not to classify the whole image into a category. Semantic segmentation is useful for applications such as autonomous driving, medical imaging, and satellite imagery analysis.

Option B is incorrect because the DetectLabels API is a general-purpose image analysis service that can detect objects, scenes, and concepts in an image, but it cannot be customized to the specific product lines of the ecommerce company. The DetectLabels API is based on the pre-trained models from Amazon Rekognition, which may not cover all the categories that the company needs.

Option D is incorrect because normalizing the pixels and scaling the images are preprocessing steps that should be done before training the model, not after. These steps can help improve the model's convergence and performance, but they are not sufficient to increase the accuracy of the model on new products.

References:

:Image Augmentation - Amazon SageMaker

:Amazon Rekognition Custom Labels Features

: [Handling Imbalanced Datasets in Machine Learning]

: [Semantic Segmentation - Amazon SageMaker]

: [DetectLabels - Amazon Rekognition]

: [Image Classification - MXNet - Amazon SageMaker]

: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

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asked 16/09/2024
Praveen Achankunju
44 questions
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