Object Detection - TensorFlow - Amazon SageMaker
Amazon EC2 instance recommendation for the Object Detection - TensorFlow algorithmSample notebooks

Object Detection - TensorFlow

The Amazon SageMaker Object Detection - TensorFlow algorithm is a supervised learning algorithm that supports transfer learning with many pretrained models from the TensorFlow Model Garden. Use transfer learning to fine-tune one of the available pretrained models on your own dataset, even if a large amount of image data is not available. The object detection algorithm takes an image as input and outputs a list of bounding boxes. Training datasets must consist of images in .jpg, .jpeg, or .png format. This page includes information about Amazon EC2 instance recommendations and sample notebooks for Object Detection - TensorFlow.

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Amazon EC2 instance recommendation for the Object Detection - TensorFlow algorithm

The Object Detection - TensorFlow algorithm supports all GPU instances for training, including:

  • ml.p2.xlarge

  • ml.p2.16xlarge

  • ml.p3.2xlarge

  • ml.p3.16xlarge

We recommend GPU instances with more memory for training with large batch sizes. Both CPU (such as M5) and GPU (P2 or P3) instances can be used for inference. For a comprehensive list of SageMaker training and inference instances across AWS Regions, see Amazon SageMaker Pricing.

Object Detection - TensorFlow sample notebooks

For more information about how to use the SageMaker Object Detection - TensorFlow algorithm for transfer learning on a custom dataset, see the Introduction to SageMaker TensorFlow - Object Detection notebook.

For instructions how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see Amazon SageMaker Notebook Instances. After you have created a notebook instance and opened it, select the SageMaker Examples tab to see a list of all the SageMaker samples. To open a notebook, choose its Use tab and choose Create copy.