From the Rekognition Immersion day Pre-requisite launch the Cloudformation stack in the "Launch Amazon SageMaker Notebook Instance" section.

Note: You don't need to do the "Download necessary notebooks" section on that page.

Next we will Create a s3 bucket.

As you create a model training job, you will save the following in an Amazon S3 bucket:

• The model training data
• Model artifacts, which Amazon SageMaker generates during model training

You can store the training data and artifacts in a single bucket or in two separate buckets. For exercises in this guide, one bucket is sufficient. You can use existing buckets or create new ones.

Follow the instructions in Create a Bucket in the Amazon Simple Storage Service Console User Guide. Include sagemaker in the bucket name; for example, sagemaker-datetime.

Click on "Open JupyterLab"

Select Git > Clone Repository

Enter git url "https://github.com/aws-samples/amazon-rekognition-workshops" in the dialog box and click "Clone"

After cloning is complete, verify that directory named "amazon-rekognition-workshops" is created

Click on 'Open Jupyter'

Click "amazon-rekognition-workshops" to open the folder

Click "ObjectDetection" to open the folder

Click on 'ground_truth_object_detection_tutorial.ipynb' to open the notebook in the browser

Open the 'ground_truth_object_detection_turtorial.ipynb' and follow the instructions in the Notebook.

See CONTRIBUTING for more information.

This library is licensed under the MIT-0 License. See the LICENSE file.