Image Classification - MxNet

The Amazon SageMaker image classification algorithm is a supervised learning algorithm that supports multi-label classification. It takes an image as input and outputs one or more labels assigned to that image. It uses a convolutional neural network that can be trained from scratch or trained using transfer learning when a large number of training images are not available.

The recommended input format for the Amazon SageMaker image classification algorithms is Apache MXNet RecordIO. However, you can also use raw images in .jpg or .png format. Refer to this discussion for a broad overview of efficient data preparation and loading for machine learning systems.

For a sample notebook that uses the SageMaker image classification algorithm to train a model on the caltech-256 dataset and then to deploy it to perform inferences, see the End-to-End Multiclass Image Classification Example. For instructions how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see Use Amazon SageMaker Notebook Instances. Once you have created a notebook instance and opened it, select the SageMaker Examples tab to see a list of all the SageMaker samples. The example image classification notebooks are located in the Introduction to Amazon algorithms section. To open a notebook, click on its Use tab and select Create copy.

For detailed documentation, please refer to the Sagemaker Image Classification Algorithm