|Number of watchers on Github||347|
|Number of open issues||6|
|Average time to close an issue||5 days|
|Average time to merge a PR||1 day|
|Open pull requests||16+|
|Closed pull requests||50+|
|Last commit||over 1 year ago|
|Repo Created||over 4 years ago|
|Repo Last Updated||over 1 year ago|
|Organization / Author||watson-developer-cloud|
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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.
Give it a try! Click the button below to fork into IBM DevOps Services and deploy your own copy of this application on the IBM Cloud.
You need a IBM Cloud account. If you don't have one, sign up. Experimental Watson Services are free to use.
Download and install the Cloud-foundry CLI tool if you haven't already.
manifest.yml file and change
<application-name> to something unique. The name you use determines the URL of your application. For example,
--- declared-services: visual-recognition-service: label: watson_vision_combined plan: free applications: - name: <application-name> path: . command: npm start memory: 512M services: - visual-recognition-service env: NODE_ENV: production
Connect to the IBM Cloud with the command line tool.
cf api https://api.ng.bluemix.net cf login
Create the Visual Recognition service in the IBM Cloud.
cf create-service watson_vision_combined free visual-recognition-service cf create-service-key visual-recognition-service myKey cf service-key visual-recognition-service myKey
.env file in the root directory by copying the sample
.env.example file using the following command:
cp .env.example .env
You will update the
.env with the information you retrieved in steps 5 and 6
.env file will look something like the following:
Point your browser to http://localhost:3000.
Optional: Push the application to the IBM Cloud:
After completing the steps above, you are ready to test your application. Start a browser and enter the URL of your application.
<your application name>.mybluemix.net
For more details about developing applications that use Watson Developer Cloud services in the IBM Cloud, see Getting started with Watson Developer Cloud and the IBM Cloud.
VISUAL_RECOGNITION_API_KEY: This is the 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)
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
Adding new/different custom classifer bundles is much more invovled.
You can follow the template of the existing bundles found in
Or, you can train a custom classifier using the api or the form and then use the classifier ID.
When you train a custom classifier, the name of the classifier is displayed in the test form.
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.
Find more open source projects on the IBM Github Page.
Allow training with custom images