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.

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.

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.

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: BCE

Community Answer: CDF

Reference:
https://docs.aws.amazon.com/rekognition/latest/dg/how-it-works-types.html
 https://towardsdatascience.com/image-processing-techniques-for-computer-vision-11f92f511e21
 https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/training-model.html


This question is in MLS-C01 AWS Certified Machine Learning – Specialty Exam
For getting AWS Certified Machine Learning – Specialty Certificate


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