Learn how to improve Amazon SageMaker image classification models for less common animal species using transfer learning and augmented manifest files in JSON Lines format.
Table of Contents
Question
A company is creating an application to identify, count, and classify animal images that are uploaded to the company’s website. The company is using the Amazon SageMaker image classification algorithm with an ImageNetV2 convolutional neural network (CNN). The solution works well for most animal images but does not recognize many animal species that are less common.
The company obtains 10,000 labeled images of less common animal species and stores the images in Amazon S3. A machine learning (ML) engineer needs to incorporate the images into the model by using Pipe mode in SageMaker.
Which combination of steps should the ML engineer take to train the model? (Choose two.)
A. Use a ResNet model. Initiate full training mode by initializing the network with random weights.
B. Use an Inception model that is available with the SageMaker image classification algorithm.
C. Create a .lst file that contains a list of image files and corresponding class labels. Upload the .lst file to Amazon S3.
D. Initiate transfer learning. Train the model by using the images of less common species.
E. Use an augmented manifest file in JSON Lines format.
Answer
D. Initiate transfer learning. Train the model by using the images of less common species.
E. Use an augmented manifest file in JSON Lines format.
Explanation
To incorporate the 10,000 labeled images of less common animal species into the existing SageMaker image classification model using Pipe mode, the ML engineer should take the following steps:
D. Initiate transfer learning. Train the model by using the images of less common species. Transfer learning allows the model to leverage the knowledge gained from the ImageNetV2 CNN and adapt it to the specific task of classifying less common animal species. By fine-tuning the pre-trained model with the new labeled images, the model can learn to recognize the unique features of these animals more effectively.
E. Use an augmented manifest file in JSON Lines format. An augmented manifest file in JSON Lines format provides a structured way to specify the location of the input data (images) and their corresponding labels. Each line in the file represents a single record, making it easy for SageMaker to process the data in Pipe mode. The manifest file should include the S3 URI of each image and its associated class label.
By combining transfer learning and using an augmented manifest file, the ML engineer can efficiently incorporate the new labeled images into the existing model, enabling it to recognize a wider range of animal species with improved accuracy.
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