################################## 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 `__