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Consume the Image Classification Retrained Model

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Consume the Image Classification Retrained Model
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Consume the Image Classification Retrained Model


Discover how to consume the retrained model for the Image Classification Retraining scenario

You will learn

  • Call an API from a REST client like Postman
  • The basics about Machine Learning Foundation Service for Image Classification in the Retraining scenario context

Step 1: The Image Classification Service

Just like the Image Classification service, the retrained Image Classification service calculates and returns a list of classifications along with their probabilities for a given image using your predefined categories.

The only difference is in the URL to be called, where you will need to append the following /models/{model name}/versions/{model version}.

This is the list of accepted file extensions:

Name Description
Archive file zip, tar, gz, tgz
Image file jpg, jpe, jpeg, png, gif, bmp

The images should be RGB, or 8-bit gray scale.

If an archive file is provided, no additional files can be provided.

The input file (or the archive file) is provided using form data (as an element named files in the form data).

The service will return a JSON response that includes the top classification with the associated scores.

For more details, you can check the Inference Service for Image Classification on the SAP API Business Hub.

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Step 2: Call the API

Open a new tab in Postman.

Make sure that the my-ml-foundation environment is selected.

On the Authorization tab, select Bearer Token, then enter {{OAuthToken}} as value.


Note:: the OAuthToken environment variable can be retrieved following the Get your OAuth Access Token using a REST Client tutorial.

Fill in the following additional information:

Field Name Value

Note As a reminder, the URL depends on you Cloud Platform landscape region but for the trial landscape only Europe (Frankfurt) provide access to the Machine Learning Foundation services.

On the Body tab, keep form-data selected. Add a new key named files and switch it to File instead of Text (default).

Select one of the image file in the flowers/try/roses directory.


Click on Send.

You should receive a response that includes a series of predictions entries for the input files with only the five flowers category the model was retrained for:

"results": [
        "label": "roses",
        "score": 0.9573449492454529
        "label": "tulips",
        "score": 0.0396801233291626
        "label": "sunflowers",
        "score": 0.0027627646923065186
        "label": "dandelion",
        "score": 0.00018524238839745522
        "label": "daisy",
        "score": 0.0000269435749942204
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Step 3: Validate your results

Provide an answer to the question below then click on Validate.

Paste the full response returned by the request.

Next Steps

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