Object Detection - MxNet GluonCV¶
The Amazon SageMaker Object Detection algorithm 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. The object is categorized into one of the classes in a specified collection with a confidence score that it belongs to the class. Its location and scale in the image are indicated by a rectangular bounding box. It uses the Single Shot multibox Detector (SSD) framework and supports two base networks: VGG and ResNet. The network can be trained from scratch, or trained with models that have been pre-trained on the ImageNet dataset.
For a sample notebook that shows how to use the SageMaker Object Detection algorithm to train and host a model on the Caltech Birds (CUB 200 2011) dataset using the Single Shot multibox Detector algorithm, see Amazon SageMaker Object Detection for Bird Species. 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 object detection example notebook using the Object Detection algorithm is 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 Object Detection Algorithm