thingumajig / amazon-sagemaker-tensorflow-form-field-det

Train and deploy models using TensorFlow 2 with the Object Detection API on Amazon SageMaker

TensorFlow 2 Object Detection API SageMaker for form field detection

Instructions

Follow the step-by-step guide by executing the notebooks in the following folders:

• 1_prepare_data/prepare_data.ipynb - TODO for training --- not ready
• 2_train_model/train_model.ipynb - TODO for training --- not ready
• 3_predict/deploy_endpoint.ipynb
• 2_train_model/model-exporter.ipynb - to export existing model

Monitor model training jobs with Tensorboard

SageMaker debugger allows you to capture TensorBoard data into a chosen S3 location and monitor the training progress in real-time with TensorBoard.
See 2_train_model/train_model.ipynb for command details.

You can start the TensorBoard server from your notebook with the following command:

job_artifacts_path = estimator.latest_job_tensorboard_artifacts_path()
tensorboard_s3_output_path = f'{job_artifacts_path}/train'

!F_CPP_MIN_LOG_LEVEL=3 AWS_REGION=<ADD YOUR REGION HERE> tensorboard --logdir=$tensorboard_s3_output_path

TensorBoard server will run on your local notebook instance and you can open it by visiting the following url (the default port is typically 6006:

https://your-notebook-instance-name.notebook.your-region.sagemaker.aws/proxy/6006/