Topic 1 Question 157
3 つ選択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?
Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.
Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.
Augment the images in the dataset. Use open source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.
Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.
Use Amazon Rekognition Custom Labels to train a new model.
Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.
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コメント(12)
B CE is correct.
👍 7tgaos2022/06/01A. This is for a different kind of problem. No need to use it B. No, because it is not pretrained on our data and won't improve it. C. YES. D. Maybe? It is a quick and dirty solution doing on a notebook, but it is quick and the normalization helps for this tasks. E. Maybe? But we have trained a model, so we might suspect this option won't be much better than our solution as is. F. YES.
I would go with CDF
👍 4edvardo2022/05/04IMO BDE are correct: A - Its not a segmentation problem, we just know the item B - Its a prebuilt model so is quick C - Yes D - Create a sagemaker notebook is not worth E - Yes is an amazon prebuilt model F - Its not worth on future, will u check it after new item is added? Its not worth
👍 4vanluigi2022/05/20
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