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πŸš€ Visual Recognition Sample Application

This Node.js app demonstrates some of the Visual Recognition service features.

Travis semantic-release

DEPRECATED: this repo is no longer actively maintained. It can still be used as reference, but may contain outdated or unpatched code.

This demo application has been replaced by a newer version, and thus this repo has been deprecated. You can view the updated application running live here. The new application has not been made public yet, so in the meantime this repo can still be used as reference.

The Visual Recognition Service uses deep learning algorithms to analyze images for scenes, objects, faces, text, and other subjects that can give you insights into your visual content. You can organize image libraries, understand an individual image, and create custom classifiers for specific results that are tailored to your needs.


  1. Sign up for an IBM Cloud account.
  2. Download the IBM Cloud CLI.
  3. Create an instance of the Visual Recognition service and get your credentials:
    • Go to the Visual Recognition page in the IBM Cloud Catalog.
    • Log in to your IBM Cloud account.
    • Click Create.
    • Click Show to view the service credentials.
    • Copy the apikey value.
    • Copy the url value.

Configuring the application

  1. In the application folder, copy the .env.example file and create a file called .env

    cp .env.example .env
  2. Open the .env file and add the service credentials that you obtained in the previous step.

    Example .env file that configures the apikey and url for a Visual Recognition service instance hosted in the US East region:


Running locally

  1. Install the dependencies

    npm install
  2. Run the application

    npm start
  3. View the application in a browser at localhost:3000

Deploying to IBM Cloud as a Cloud Foundry Application

  1. Login to IBM Cloud with the IBM Cloud CLI

    ibmcloud login
  2. Target a Cloud Foundry organization and space.

    ibmcloud target --cf
  3. Edit the manifest.yml file. Change the name field to something unique.
    For example, - name: my-app-name.

  4. Deploy the application

    ibmcloud app push
  5. View the application online at the app URL.
    For example:

Environment Variables

  • VISUAL_RECOGNITION_IAM_API_KEY : This is the IAM API key for the vision service, used if you don't have one in your IBM Cloud account.
  • PRESERVE_CLASSIFIERS : Set if you don't want classifiers to be deleted after one hour. (optional)
  • PORT : The port the server should run on. (optional, defaults to 3000)
  • OVERRIDE_CLASSIFIER_ID : Set to a classifer ID if you want to always use a custom classifier. This classifier will be used instead of training a new one. (optional)

Changing the Included Images

Sample Images

The sample images are the first 7 images when the site loads. They are called from a Jade mixin found in views/mixins/sampleImages.jade. If you just want to replace those images with different images, you can replace them in public/images/samples and they are numbered 1 - 7 and are jpg formatted.

Custom Classifier Bundles

Adding new/different custom classifer bundles is much more invovled. You can follow the template of the existing bundles found in views/includes/train.jade.

Or, you can train a custom classifier using the api or the form and then use the classifier ID.

Getting the Classifier ID

When you train a custom classifier, the name of the classifier is displayed in the test form.

Classifier ID Tooltip

If you hover your mouse over the classifier name, the classifier ID will be shown in the tooltip. You can also click on the name, and it will toggle between the classifier name and the classifier ID.

You can then use this custom classifier id by placing it after the hash in the request URL. For example, lets say you are running the system locally, so the base URL is http://localhost:3000 and then you train a classifier. This newly trained classifier might have an id like SatelliteImagery_859438478. If you wanted to use this classifier instead of training a new one, you can navigate to http://localhost:3000/train#SatelliteImagery_859438478 and use the training form with your existing classifier.


This sample code is licensed under Apache 2.0. Full license text is available in LICENSE.



Open Source @ IBM

Find more open source projects on the IBM Github Page.