Object Detection - TensorFlow
The Amazon SageMaker AI Object Detection - TensorFlow algorithm is a supervised learning algorithm that
supports transfer learning with many pretrained models from the TensorFlow Model Gardenjpg
, .jpeg
, or .png
format. This page includes
information about Amazon EC2 instance recommendations and sample notebooks for Object Detection -
TensorFlow.
Topics
- How to use the SageMaker AI Object Detection - TensorFlow algorithm
- Input and output interface for the Object Detection - TensorFlow algorithm
- Amazon EC2 instance recommendation for the Object Detection - TensorFlow algorithm
- Object Detection - TensorFlow sample notebooks
- How Object Detection - TensorFlow Works
- TensorFlow Models
- Object Detection - TensorFlow Hyperparameters
- Tune an Object Detection - TensorFlow model
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 AI Pricing
Object Detection - TensorFlow sample notebooks
For more information about how to use the SageMaker AI Object Detection - TensorFlow algorithm
for transfer learning on a custom dataset, see the Introduction to SageMaker TensorFlow - Object Detection
For instructions how to create and access Jupyter notebook instances that you can use to run the example in SageMaker AI, see Amazon SageMaker Notebook Instances. After you have created a notebook instance and opened it, select the SageMaker AI Examples tab to see a list of all the SageMaker AI samples. To open a notebook, choose its Use tab and choose Create copy.