Question

A retail company wants to build an image classifier that identifies which of 40 product categories appears in a photo. They have only about 3,000 labeled images total (roughly 75 per category) and limited GPU budget. The ML team needs the fastest path to a reasonably accurate model without collecting more labeled data. Which modeling approach best fits this situation?

Accepted Answer

Use transfer learning with SageMaker's built-in image classification algorithm in transfer-learning mode, fine-tuning a pretrained network on the labeled images

Answer

Train a deep convolutional neural network from scratch on the 3,000 images using random weight initialization

Answer

Use the SageMaker K-Means algorithm to cluster the images into 40 groups and label the clusters by category

Answer

Use the SageMaker Linear Learner algorithm on the raw pixel values to classify the 40 categories