Built-in SageMaker Algorithms for Computer Vision

SageMaker provides image processing algorithms that are used for image classification, object detection, and computer vision.

• Image Classification - MXNet—uses example data with answers (referred to as a supervised algorithm). Use this algorithm to classify images.

• Image Classification - TensorFlow—uses pretrained TensorFlow Hub models to fine-tune for specific tasks (referred to as a supervised algorithm). Use this algorithm to classify images.

• Object Detection - MXNet—detects and classifies objects in images using a single deep neural network. It is a supervised learning algorithm that takes images as input and identifies all instances of objects within the image scene.

• Object Detection - TensorFlow—detects bounding boxes and object labels in an image. It is a supervised learning algorithm that supports transfer learning with available pretrained TensorFlow models.

• Semantic Segmentation Algorithm—provides a fine-grained, pixel-level approach to developing computer vision applications.

Algorithm name	Channel name	Training input mode	File type	Instance class	Parallelizable
Image Classification - MXNet	train and validation, (optionally) train_lst, validation_lst, and model	File or Pipe	recordIO or image files (.jpg or .png)	GPU	Yes
Image Classification - TensorFlow	training and validation	File	image files (.jpg, .jpeg, or .png)	CPU or GPU	Yes (only across multiple GPUs on a single instance)
Object Detection	train and validation, (optionally) train_annotation, validation_annotation, and model	File or Pipe	recordIO or image files (.jpg or .png)	GPU	Yes
Object Detection - TensorFlow	training and validation	File	image files (.jpg, .jpeg, or .png)	GPU	Yes (only across multiple GPUs on a single instance)
Semantic Segmentation	train and validation, train_annotation, validation_annotation, and (optionally) label_map and model	File or Pipe	Image files	GPU (single instance only)	No