Sign in
Sign in
or
Create a new account
Categories
Migration Mapping Assistant Your Saved List Partners Sell in AWS Marketplace Amazon Web Services Home Help
Amazon SageMaker ×

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Visual Shelf Monitoring
Free Trial

By: Agmis Latest Version: v1.0

The model identifies whether products are present on a store shelve.

Product Overview

The model is used to monitor product stock levels on a store shelve. The model can identify if the particular product is present or if the shelve is empty. Together with stock management software, the model can be used to notify shop managers when stock on a shelve is running low and the shelve needs to be restocked.

Key Data

Version
By Agmis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Input

Supported content types: image/jpeg, image/png, application/x-image

Output

Content type: application/json Sample output:

{
   "output":[
      {
         "class_id":[
            2
],
         "class_label":"Half empty"
}
]
}

Invoking endpoint

AWS CLI Command

You can invoke endpoint using AWS CLI:

aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.jpg --content-type image/jpeg --accept application/json out.json

Substitute the following parameters:

  • "endpoint-name" - name of the inference endpoint where the model is deployed
  • input.jpg - input image to do the inference on
  • image/jpeg - MIME type of the given input image (above)
  • out.json - filename where the inference results are written to

Python

Real-time inference snippet (more detailed example can be found in sample notebook):

runtime = boto3.Session().client(service_name='runtime.sagemaker')
bytes_image = open('path-to-img', 'rb').read()
response = runtime.invoke_endpoint(EndpointName='endpoint-name',      
ContentType='image/jpeg', Body=bytes_image)
response = response['Body'].read()
results = json.loads(response)

Resources

If you have any questions feel free to contact us info@agmis.eu or fill our https://agmis.lt/contacts/

Support Information

AWS Infrastructure

AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services. Learn more

Refund Policy

We do not offer refunds at this time

Customer Reviews