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    • Introduction

    Last updated: 2021-03-02

    IBM Watson™ Visual Recognition is discontinued. Existing instances are supported until 1 December 2021, but as of 7 January 2021, you can't create instances. Any instance that is provisioned on 1 December 2021 will be deleted.

    The IBM Watson Visual Recognition service uses deep learning algorithms to identify scenes and objects in images that you upload to the service. You can create and train a custom classifier to identify subjects that suit your needs.

    This documentation describes Java SDK major version 9. For more information about how to update your code from the previous version, see the migration guide.

    This documentation describes Node SDK major version 6. For more information about how to update your code from the previous version, see the migration guide.

    This documentation describes Python SDK major version 5. For more information about how to update your code from the previous version, see the migration guide.

    This documentation describes Ruby SDK major version 2. For more information about how to update your code from the previous version, see the migration guide.

    This documentation describes .NET Standard SDK major version 5. For more information about how to update your code from the previous version, see the migration guide.

    This documentation describes Go SDK major version 2. For more information about how to update your code from the previous version, see the migration guide.

    This documentation describes Swift SDK major version 4. For more information about how to update your code from the previous version, see the migration guide.

    This documentation describes Unity SDK major version 5. For more information about how to update your code from the previous version, see the migration guide.

    The IBM Watson Unity SDK has the following requirements.

    • The SDK requires Unity version 2018.2 or later to support Transport Layer Security (TLS) 1.2.
      • Set the project settings for both the Scripting Runtime Version and the Api Compatibility Level to .NET 4.x Equivalent.
      • For more information, see TLS 1.0 support.
    • The SDK doesn't support the WebGL projects. Change your build settings to any platform except WebGL.

    For more information about how to install and configure the SDK and SDK Core, see https://github.com/watson-developer-cloud/unity-sdk.

    The code examples on this tab use the client library that is provided for Java.

    Maven

    <dependency>
  <groupId>com.ibm.watson</groupId>
  <artifactId>ibm-watson</artifactId>
  <version>9.0.2</version>
</dependency>

    Gradle

    compile 'com.ibm.watson:ibm-watson:9.0.2'

    GitHub

    https://github.com/watson-developer-cloud/java-sdk

    The code examples on this tab use the client library that is provided for Node.js.

    Installation

    npm install ibm-watson@^6.0.3

    GitHub

    https://github.com/watson-developer-cloud/node-sdk

    The code examples on this tab use the client library that is provided for Python.

    Installation

    pip install --upgrade "ibm-watson>=5.1.0"

    GitHub

    https://github.com/watson-developer-cloud/python-sdk

    The code examples on this tab use the client library that is provided for Ruby.

    Installation

    gem install ibm_watson

    GitHub

    https://github.com/watson-developer-cloud/ruby-sdk

    The code examples on this tab use the client library that is provided for Go.

    go get -u github.com/watson-developer-cloud/go-sdk@v2.0.2

    GitHub

    https://github.com/watson-developer-cloud/go-sdk

    The code examples on this tab use the client library that is provided for Swift.

    Cocoapods

    pod 'IBMWatsonVisualRecognitionV3', '~> 4.1.0'

    Carthage

    github "watson-developer-cloud/swift-sdk" ~> 4.1.0

    Swift Package Manager

    .package(url: "https://github.com/watson-developer-cloud/swift-sdk", from: "4.1.0")

    GitHub

    https://github.com/watson-developer-cloud/swift-sdk

    The code examples on this tab use the client library that is provided for .NET Standard.

    Package Manager

    Install-Package IBM.Watson.VisualRecognition.v3 -Version 5.1.0

    .NET CLI

    dotnet add package IBM.Watson.VisualRecognition.v3 --version 5.1.0

    PackageReference

    <PackageReference Include="IBM.Watson.VisualRecognition.v3" Version="5.1.0" />

    GitHub

    https://github.com/watson-developer-cloud/dotnet-standard-sdk

    The code examples on this tab use the client library that is provided for Unity.

    GitHub

    https://github.com/watson-developer-cloud/unity-sdk

    https://github.com/IBM/unity-sdk-core

    Authentication

    You authenticate to the API by using IBM Cloud Identity and Access Management (IAM).

    You can pass either a bearer token in an authorization header or an API key. Tokens support authenticated requests without embedding service credentials in every call. API keys use basic authentication. For more information, see Authenticating to Watson services.

    • For testing and development, you can pass an API key directly.
    • For production use, unless you use the Watson SDKs, use an IAM token.

    If you pass in an API key, use apikey for the username and the value of the API key as the password. For example, if the API key is f5sAznhrKQyvBFFaZbtF60m5tzLbqWhyALQawBg5TjRI in the service credentials, include the credentials in your call like this:

    curl -u "apikey:f5sAznhrKQyvBFFaZbtF60m5tzLbqWhyALQawBg5TjRI"

    IBM Cloud Dedicated instances for Visual Recognition authenticate by providing the username and password for the service instance.

    For IBM Cloud instances, the SDK provides initialization methods for each form of authentication.

    • Use the API key to have the SDK manage the lifecycle of the access token. The SDK requests an access token, ensures that the access token is valid, and refreshes it if necessary.
    • Use the access token to manage the lifecycle yourself. You must periodically refresh the token.

    For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.

    Replace {apikey} and {url} with your service credentials.

    curl -X {request_method} -u "apikey:{apikey}" "{url}/v3/{method}"

    SDK managing the IAM token. Replace {apikey}, {version}, and {url}.

    IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
VisualRecognition visualRecognition = new VisualRecognition("{version}", authenticator);
visualRecognition.setServiceUrl("{url}");

    SDK managing the IAM token. Replace {apikey}, {version}, and {url}.

    const VisualRecognitionV3 = require('ibm-watson/visual-recognition/v3');
const { IamAuthenticator } = require('ibm-watson/auth');

const visualRecognition = new VisualRecognitionV3({
  version: '{version}',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
});

    SDK managing the IAM token. Replace {apikey}, {version}, and {url}.

    from ibm_watson import VisualRecognitionV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

authenticator = IAMAuthenticator('{apikey}')
visual_recognition = VisualRecognitionV3(
    version='{version}',
    authenticator=authenticator
)

visual_recognition.set_service_url('{url}')

    SDK managing the IAM token. Replace {apikey}, {version}, and {url}.

    require "ibm_watson/authenticators"
require "ibm_watson/visual_recognition_v3"
include IBMWatson

authenticator = Authenticators::IamAuthenticator.new(
  apikey: "{apikey}"
)
visual_recognition = VisualRecognitionV3.new(
  version: "{version}",
  authenticator: authenticator
)
visual_recognition.service_url = "{url}"

    SDK managing the IAM token. Replace {apikey}, {version}, and {url}.

    import (
  "github.com/IBM/go-sdk-core/core"
  "github.com/watson-developer-cloud/go-sdk/visualrecognitionv3"
)

func main() {
  authenticator := &core.IamAuthenticator{
    ApiKey: "{apikey}",
  }

  options := &visualrecognitionv3.VisualRecognitionV3Options{
    Version: "{version}",
    Authenticator: authenticator,
  }

  visualRecognition, visualRecognitionErr := visualrecognitionv3.NewVisualRecognitionV3(options)

  if visualRecognitionErr != nil {
    panic(visualRecognitionErr)
  }

  visualRecognition.SetServiceURL("{url}")
}

    SDK managing the IAM token. Replace {apikey}, {version}, and {url}.

    let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let visualRecognition = VisualRecognition(version: "{version}", authenticator: authenticator)
visualRecognition.serviceURL = "{url}"

    SDK managing the IAM token. Replace {apikey}, {version}, and {url}.

    IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

VisualRecognitionService visualRecognition = new VisualRecognitionService("{version}", authenticator);
visualRecognition.SetServiceUrl("{url}");

    SDK managing the IAM token. Replace {apikey}, {version}, and {url}.

    var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var visualRecognition = new VisualRecognitionService("{version}", authenticator);
visualRecognition.SetServiceUrl("{url}");

    Access between services

    Your application might use more than one Watson service. You can grant access between services and you can grant access to more than one service for your applications.

    For IBM Cloud services, the method to grant access between Watson services varies depending on the type of API key. For more information, see IAM access.

    • To grant access between IBM Cloud services, create an authorization between the services. For more information, see Granting access between services.
    • To grant access to your services by applications without using user credentials, create a service ID, add an API key, and assign access policies. For more information, see Creating and working with service IDs.

    Make sure that you use an endpoint URL that includes the service instance ID (for example, https://api.us-south.visual-recognition.watson.cloud.ibm.com/instances/6bbda3b3-d572-45e1-8c54-22d6ed9e52c2). You can find the instance ID in two places:

    • By clicking the service instance row in the Resource list. The instance ID is the GUID in the details pane.

    • By clicking the name of the service instance in the list and looking at the credentials URL.

      If you don't see the instance ID in the URL, you can add new credentials from the Service credentials page.

    Endpoint URLs

    Identify the base URL for your service instance.

    IBM Cloud URLs

    The base URLs come from the service instance. To find the URL, view the service credentials by clicking the name of the service in the Resource list. Use the value of the URL. Add the method to form the complete API endpoint for your request.

    The following example URL represents a Visual Recognition instance that is hosted in Frankfurt:

    https://api.eu-de.visual-recognition.watson.cloud.ibm.com/instances/6bbda3b3-d572-45e1-8c54-22d6ed9e52c2

    The following URLs represent the base URLs for Visual Recognition. When you call the API, use the URL that corresponds to the location of your service instance.

    • Dallas: https://api.us-south.visual-recognition.watson.cloud.ibm.com
    • Frankfurt: https://api.eu-de.visual-recognition.watson.cloud.ibm.com
    • Seoul: https://api.kr-seo.visual-recognition.watson.cloud.ibm.com

    Set the correct service URL by calling the setServiceUrl() method of the service instance.

    Set the correct service URL by specifying the serviceUrl parameter when you create the service instance.

    Set the correct service URL by calling the set_service_url() method of the service instance.

    Set the correct service URL by specifying the service_url property of the service instance.

    Set the correct service URL by calling the SetServiceURL() method of the service instance.

    Set the correct service URL by setting the serviceURL property of the service instance.

    Set the correct service URL by calling the SetServiceUrl() method of the service instance.

    Set the correct service URL by calling the SetServiceUrl() method of the service instance.

    Dallas API endpoint example for services managed on IBM Cloud

    curl -X {request_method} -u "apikey:{apikey}" "https://api.us-south.visual-recognition.watson.cloud.ibm.com/instances/{instance_id}"

    Your service instance might not use this URL

    Default URL

    https://api.us-south.visual-recognition.watson.cloud.ibm.com

    Example for the Frankfurt location

    IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
VisualRecognition visualRecognition = new VisualRecognition("{version}", authenticator);
visualRecognition.setServiceUrl("https://api.eu-de.visual-recognition.watson.cloud.ibm.com");

    Default URL

    https://api.us-south.visual-recognition.watson.cloud.ibm.com

    Example for the Frankfurt location

    const VisualRecognitionV3 = require('ibm-watson/visual-recognition/v3');
const { IamAuthenticator } = require('ibm-watson/auth');

const visualRecognition = new VisualRecognitionV3({
  version: '{version}',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: 'https://api.eu-de.visual-recognition.watson.cloud.ibm.com',
});

    Default URL

    https://api.us-south.visual-recognition.watson.cloud.ibm.com

    Example for the Frankfurt location

    from ibm_watson import VisualRecognitionV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

authenticator = IAMAuthenticator('{apikey}')
visual_recognition = VisualRecognitionV3(
    version='{version}',
    authenticator=authenticator
)

visual_recognition.set_service_url('https://api.eu-de.visual-recognition.watson.cloud.ibm.com')

    Default URL

    https://api.us-south.visual-recognition.watson.cloud.ibm.com

    Example for the Frankfurt location

    require "ibm_watson/authenticators"
require "ibm_watson/visual_recognition_v3"
include IBMWatson

authenticator = Authenticators::IamAuthenticator.new(
  apikey: "{apikey}"
)
visual_recognition = VisualRecognitionV3.new(
  version: "{version}",
  authenticator: authenticator
)
visual_recognition.service_url = "https://api.eu-de.visual-recognition.watson.cloud.ibm.com"

    Default URL

    https://api.us-south.visual-recognition.watson.cloud.ibm.com

    Example for the Frankfurt location

    visualRecognition, visualRecognitionErr := visualrecognitionv3.NewVisualRecognitionV3(options)

if visualRecognitionErr != nil {
  panic(visualRecognitionErr)
}

visualRecognition.SetServiceURL("https://api.eu-de.visual-recognition.watson.cloud.ibm.com")

    Default URL

    https://api.us-south.visual-recognition.watson.cloud.ibm.com

    Example for the Frankfurt location

    let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let visualRecognition = VisualRecognition(version: "{version}", authenticator: authenticator)
visualRecognition.serviceURL = "https://api.eu-de.visual-recognition.watson.cloud.ibm.com"

    Default URL

    https://api.us-south.visual-recognition.watson.cloud.ibm.com

    Example for the Frankfurt location

    IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

VisualRecognitionService visualRecognition = new VisualRecognitionService("{version}", authenticator);
visualRecognition.SetServiceUrl("https://api.eu-de.visual-recognition.watson.cloud.ibm.com");

    Default URL

    https://api.us-south.visual-recognition.watson.cloud.ibm.com

    Example for the Frankfurt location

    var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var visualRecognition = new VisualRecognitionService("{version}", authenticator);
visualRecognition.SetServiceUrl("https://api.eu-de.visual-recognition.watson.cloud.ibm.com");

    Disabling SSL verification

    All Watson services use Secure Sockets Layer (SSL) (or Transport Layer Security (TLS)) for secure connections between the client and server. The connection is verified against the local certificate store to ensure authentication, integrity, and confidentiality.

    If you use a self-signed certificate, you need to disable SSL verification to make a successful connection.

    Enabling SSL verification is highly recommended. Disabling SSL jeopardizes the security of the connection and data. Disable SSL only if necessary, and take steps to enable SSL as soon as possible.

    To disable SSL verification for a curl request, use the --insecure (-k) option with the request.

    To disable SSL verification, create an HttpConfigOptions object and set the disableSslVerification property to true. Then, pass the object to the service instance by using the configureClient method.

    To disable SSL verification, set the disableSslVerification parameter to true when you create the service instance.

    To disable SSL verification, specify True on the set_disable_ssl_verification method for the service instance.

    To disable SSL verification, set the disable_ssl_verification parameter to true in the configure_http_client() method for the service instance.

    To disable SSL verification, call the DisableSSLVerification method on the service instance.

    To disable SSL verification, call the disableSSLVerification() method on the service instance. You cannot disable SSL verification on Linux.

    To disable SSL verification, set the DisableSslVerification method to true on the service instance.

    To disable SSL verification, set the DisableSslVerification method to true on the service instance.

    Example to disable SSL verification. Replace {apikey} and {url} with your service credentials.

    curl -k -X {request_method} -u "apikey:{apikey}" "{url}/{method}"

    Example to disable SSL verification

    IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
VisualRecognition visualRecognition = new VisualRecognition("{version}", authenticator);
visualRecognition.setServiceUrl("{url}");

HttpConfigOptions configOptions = new HttpConfigOptions.Builder()
  .disableSslVerification(true)
  .build();
visualRecognition.configureClient(configOptions);

    Example to disable SSL verification

    const VisualRecognitionV3 = require('ibm-watson/visual-recognition/v3');
const { IamAuthenticator } = require('ibm-watson/auth');

const visualRecognition = new VisualRecognitionV3({
  version: '{version}',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
  disableSslVerification: true,
});

    Example to disable SSL verification

    from ibm_watson import VisualRecognitionV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

authenticator = IAMAuthenticator('{apikey}')
visual_recognition = VisualRecognitionV3(
    version='{version}',
    authenticator=authenticator
)

visual_recognition.set_service_url('{url}')

visual_recognition.set_disable_ssl_verification(True)

    Example to disable SSL verification

    require "ibm_watson/authenticators"
require "ibm_watson/visual_recognition_v3"
include IBMWatson

authenticator = Authenticators::IamAuthenticator.new(
  apikey: "{apikey}"
)
visual_recognition = VisualRecognitionV3.new(
  version: "{version}",
  authenticator: authenticator
)
visual_recognition.service_url = "{url}"

visual_recognition.configure_http_client(disable_ssl_verification: true)

    Example to disable SSL verification

    visualRecognition, visualRecognitionErr := visualrecognitionv3.NewVisualRecognitionV3(options)

if visualRecognitionErr != nil {
  panic(visualRecognitionErr)
}

visualRecognition.SetServiceURL("{url}")

visualRecognition.DisableSSLVerification()

    Example to disable SSL verification

    let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let visualRecognition = VisualRecognition(version: "{version}", authenticator: authenticator)
visualRecognition.serviceURL = "{url}"

visualRecognition.disableSSLVerification()

    Example to disable SSL verification

    IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

VisualRecognitionService visualRecognition = new VisualRecognitionService("{version}", authenticator);
visualRecognition.SetServiceUrl("{url}");

visualRecognition.DisableSslVerification(true);

    Example to disable SSL verification

    var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var visualRecognition = new VisualRecognitionService("{version}", authenticator);
visualRecognition.SetServiceUrl("{url}");

visualRecognition.DisableSslVerification = true;

    Versioning

    API requests require a version parameter that takes a date in the format version=YYYY-MM-DD. When we change the API in a backwards-incompatible way, we release a new version date.

    Send the version parameter with every API request. The service uses the API version for the date you specify, or the most recent version before that date. Don't default to the current date. Instead, specify a date that matches a version that is compatible with your app, and don't change it until your app is ready for a later version.

    Specify the version to use on API requests with the version parameter when you create the service instance. The service uses the API version for the date you specify, or the most recent version before that date. Don't default to the current date. Instead, specify a date that matches a version that is compatible with your app, and don't change it until your app is ready for a later version.

    This documentation describes the current version of Visual Recognition, 2018-03-19. In some cases, differences in earlier versions are noted in the descriptions of parameters and response models.

    Error handling

    Visual Recognition uses standard HTTP response codes to indicate whether a method completed successfully. HTTP response codes in the 2xx range indicate success. A response in the 4xx range is some sort of failure, and a response in the 5xx range usually indicates an internal system error that cannot be resolved by the user. Response codes are listed with the method.

    ErrorResponse

    Name Description
    code
    integer    		 The HTTP response code.
    error
    string    		 General description of an error.

    ErrorHTML

    Name Description
    Error
    string    		 HTML description of the error.

    ErrorInfo

    Information about what might have caused a failure, such as an image that is too large. Not returned when there is no error.

    Name Description
    code
    integer    		 HTTP response code.
    description
    string    		 Human-readable error description. For example, File size limit exceeded.
    error_id
    string    		 Codified error string. For example, limit_exceeded.

    The Java SDK generates an exception for any unsuccessful method invocation. All methods that accept an argument can also throw an IllegalArgumentException.

    Exception Description
    IllegalArgumentException An invalid argument was passed to the method.

    When the Java SDK receives an error response from the Visual Recognition service, it generates an exception from the com.ibm.watson.developer_cloud.service.exception package. All service exceptions contain the following fields.

    Field Description
    statusCode The HTTP response code that is returned.
    message A message that describes the error.

    When the Node SDK receives an error response from the Visual Recognition service, it creates an Error object with information that describes the error that occurred. This error object is passed as the first parameter to the callback function for the method. The contents of the error object are as shown in the following table.

    Error

    Field Description
    code The HTTP response code that is returned.
    message A message that describes the error.

    The Python SDK generates an exception for any unsuccessful method invocation. When the Python SDK receives an error response from the Visual Recognition service, it generates an ApiException with the following fields.

    Field Description
    code The HTTP response code that is returned.
    message A message that describes the error.
    info A dictionary of additional information about the error.

    When the Ruby SDK receives an error response from the Visual Recognition service, it generates an ApiException with the following fields.

    Field Description
    code The HTTP response code that is returned.
    message A message that describes the error.
    info A dictionary of additional information about the error.

    The Go SDK generates an error for any unsuccessful service instantiation and method invocation. You can check for the error immediately. The contents of the error object are as shown in the following table.

    Error

    Field Description
    code The HTTP response code that is returned.
    message A message that describes the error.

    The Swift SDK returns a WatsonError in the completionHandler any unsuccessful method invocation. This error type is an enum that conforms to LocalizedError and contains an errorDescription property that returns an error message. Some of the WatsonError cases contain associated values that reveal more information about the error.

    Field Description
    errorDescription A message that describes the error.

    When the .NET Standard SDK receives an error response from the Visual Recognition service, it generates a ServiceResponseException with the following fields.

    Field Description
    Message A message that describes the error.
    CodeDescription The HTTP response code that is returned.

    When the Unity SDK receives an error response from the Visual Recognition service, it generates an IBMError with the following fields.

    Field Description
    Url The URL that generated the error.
    StatusCode The HTTP response code returned.
    ErrorMessage A message that describes the error.
    Response The contents of the response from the server.
    ResponseHeaders A dictionary of headers returned by the request.

    Example error handling

    try {
  // Invoke a method
} catch (NotFoundException e) {
  // Handle Not Found (404) exception
} catch (RequestTooLargeException e) {
  // Handle Request Too Large (413) exception
} catch (ServiceResponseException e) {
  // Base class for all exceptions caused by error responses from the service
  System.out.println("Service returned status code "
    + e.getStatusCode() + ": " + e.getMessage());
}

    Example error handling

    visualRecognition.method(params)
  .catch(err => {
    console.log('error:', err);
  });

    Example error handling

    from ibm_watson import ApiException
try:
    # Invoke a method
except ApiException as ex:
    print "Method failed with status code " + str(ex.code) + ": " + ex.message

    Example error handling

    require "ibm_watson"
begin
  # Invoke a method
rescue IBMWatson::ApiException => ex
  print "Method failed with status code #{ex.code}: #{ex.error}"
end

    Example error handling

    import "github.com/watson-developer-cloud/go-sdk/visualrecognitionv3"

// Instantiate a service
visualRecognition, visualRecognitionErr := visualrecognitionv3.NewVisualRecognitionV3(options)

// Check for errors
if visualRecognitionErr != nil {
  panic(visualRecognitionErr)
}

// Call a method
result, response, responseErr := visualRecognition.MethodName(&methodOptions)

// Check for errors
if responseErr != nil {
  panic(responseErr)
}

    Example error handling

    visualRecognition.method() {
  response, error in

  if let error = error {
    switch error {
    case let .http(statusCode, message, metadata):
      switch statusCode {
      case .some(404):
        // Handle Not Found (404) exception
        print("Not found")
      case .some(413):
        // Handle Request Too Large (413) exception
        print("Payload too large")
      default:
        if let statusCode = statusCode {
          print("Error - code: \(statusCode), \(message ?? "")")
        }
      }
    default:
      print(error.localizedDescription)
    }
    return
  }

  guard let result = response?.result else {
    print(error?.localizedDescription ?? "unknown error")
    return
  }

  print(result)
}

    Example error handling

    try
{
    // Invoke a method
}
catch(ServiceResponseException e)
{
    Console.WriteLine("Error: " + e.Message);
}
catch (Exception e)
{
    Console.WriteLine("Error: " + e.Message);
}

    Example error handling

    // Invoke a method
visualRecognition.MethodName(Callback, Parameters);

// Check for errors
private void Callback(DetailedResponse<ExampleResponse> response, IBMError error)
{
    if (error == null)
    {
        Log.Debug("ExampleCallback", "Response received: {0}", response.Response);
    }
    else
    {
        Log.Debug("ExampleCallback", "Error received: {0}, {1}, {3}", error.StatusCode, error.ErrorMessage, error.Response);
    }
}

    Data handling

    Additional headers

    Some Watson services accept special parameters in headers that are passed with the request.

    You can pass request header parameters in all requests or in a single request to the service.

    To pass a request header, use the --header (-H) option with a curl request.

    To pass header parameters with every request, use the setDefaultHeaders method of the service object. See Data collection for an example use of this method.

    To pass header parameters in a single request, use the addHeader method as a modifier on the request before you execute it.

    To pass header parameters with every request, specify the headers parameter when you create the service object. See Data collection for an example use of this method.

    To pass header parameters in a single request, use the headers method as a modifier on the request before you execute it.

    To pass header parameters with every request, specify the set_default_headers method of the service object. See Data collection for an example use of this method.

    To pass header parameters in a single request, include headers as a dict in the request.

    To pass header parameters with every request, specify the add_default_headers method of the service object. See Data collection for an example use of this method.

    To pass header parameters in a single request, specify the headers method as a chainable method in the request.

    To pass header parameters with every request, specify the SetDefaultHeaders method of the service object. See Data collection for an example use of this method.

    To pass header parameters in a single request, specify the Headers as a map in the request.

    To pass header parameters with every request, add them to the defaultHeaders property of the service object. See Data collection for an example use of this method.

    To pass header parameters in a single request, pass the headers parameter to the request method.

    To pass header parameters in a single request, use the WithHeader() method as a modifier on the request before you execute it. See Data collection for an example use of this method.

    To pass header parameters in a single request, use the WithHeader() method as a modifier on the request before you execute it.

    Example header parameter in a request

    curl -X {request_method} -H "Request-Header: {header_value}" "{url}/v3/{method}"

    Example header parameter in a request

    ReturnType returnValue = visualRecognition.methodName(parameters)
  .addHeader("Custom-Header", "{header_value}")
  .execute();

    Example header parameter in a request

    const parameters = {
  {parameters}
};

visualRecognition.methodName(
  parameters,
  headers: {
    'Custom-Header': '{header_value}'
  })
   .then(result => {
    console.log(response);
  })
  .catch(err => {
    console.log('error:', err);
  });

    Example header parameter in a request

    response = visual_recognition.methodName(
    parameters,
    headers = {
        'Custom-Header': '{header_value}'
    })

    Example header parameter in a request

    response = visual_recognition.headers(
  "Custom-Header" => "{header_value}"
).methodName(parameters)

    Example header parameter in a request

    result, response, responseErr := visualRecognition.MethodName(
  &methodOptions{
    Headers: map[string]string{
      "Accept": "application/json",
    },
  },
)

    Example header parameter in a request

    let customHeader: [String: String] = ["Custom-Header": "{header_value}"]
visualRecognition.methodName(parameters, headers: customHeader) {
  response, error in
}

    Example header parameter in a request

    IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

VisualRecognitionService visualRecognition = new VisualRecognitionService("{version}", authenticator);
visualRecognition.SetServiceUrl("{url}");

visualRecognition.WithHeader("Custom-Header", "header_value");

    Example header parameter in a request

    var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var visualRecognition = new VisualRecognitionService("{version}", authenticator);
visualRecognition.SetServiceUrl("{url}");

visualRecognition.WithHeader("Custom-Header", "header_value");

    Response details

    The Visual Recognition service might return information to the application in response headers.

    To access all response headers that the service returns, include the --include (-i) option with a curl request. To see detailed response data for the request, including request headers, response headers, and extra debugging information, include the --verbose (-v) option with the request.

    Example request to access response headers

    curl -X {request_method} {authentication_method} --include "{url}/v3/{method}"

    To access information in the response headers, use one of the request methods that returns details with the response: executeWithDetails(), enqueueWithDetails(), or rxWithDetails(). These methods return a Response<T> object, where T is the expected response model. Use the getResult() method to access the response object for the method, and use the getHeaders() method to access information in response headers.

    Example request to access response headers

    Response<ReturnType> response = visualRecognition.methodName(parameters)
  .executeWithDetails();
// Access response from methodName
ReturnType returnValue = response.getResult();
// Access information in response headers
Headers responseHeaders = response.getHeaders();

    All response data is available in the Response<T> object that is returned by each method. To access information in the response object, use the following properties.

    Property Description
    result Returns the response for the service-specific method.
    headers Returns the response header information.
    status Returns the HTTP status code.

    Example request to access response headers

    visualRecognition.methodName(parameters)
  .then(response => {
    console.log(response.headers);
  })
  .catch(err => {
    console.log('error:', err);
  });

    The return value from all service methods is a DetailedResponse object. To access information in the result object or response headers, use the following methods.

    DetailedResponse

    Method Description
    get_result() Returns the response for the service-specific method.
    get_headers() Returns the response header information.
    get_status_code() Returns the HTTP status code.

    Example request to access response headers

    visual_recognition.set_detailed_response(True)
response = visual_recognition.methodName(parameters)
# Access response from methodName
print(json.dumps(response.get_result(), indent=2))
# Access information in response headers
print(response.get_headers())
# Access HTTP response status
print(response.get_status_code())

    The return value from all service methods is a DetailedResponse object. To access information in the response object, use the following properties.

    DetailedResponse

    Property Description
    result Returns the response for the service-specific method.
    headers Returns the response header information.
    status Returns the HTTP status code.

    Example request to access response headers

    response = visual_recognition.methodName(parameters)
# Access response from methodName
print response.result
# Access information in response headers
print response.headers
# Access HTTP response status
print response.status

    The return value from all service methods is a DetailedResponse object. To access information in the response object or response headers, use the following methods.

    DetailedResponse

    Method Description
    GetResult() Returns the response for the service-specific method.
    GetHeaders() Returns the response header information.
    GetStatusCode() Returns the HTTP status code.

    Example request to access response headers

    import (
  "github.com/IBM/go-sdk-core/core"
  "github.com/watson-developer-cloud/go-sdk/visualrecognitionv3"
)
result, response, responseErr := visualRecognition.MethodName(
  &methodOptions{})
// Access result
core.PrettyPrint(response.GetResult(), "Result ")

// Access response headers
core.PrettyPrint(response.GetHeaders(), "Headers ")

// Access status code
core.PrettyPrint(response.GetStatusCode(), "Status Code ")

    All response data is available in the WatsonResponse<T> object that is returned in each method's completionHandler.

    Example request to access response headers

    visualRecognition.methodName(parameters) {
  response, error in

  guard let result = response?.result else {
    print(error?.localizedDescription ?? "unknown error")
    return
  }
  print(result) // The data returned by the service
  print(response?.statusCode)
  print(response?.headers)
}

    The response contains fields for response headers, response JSON, and the status code.

    DetailedResponse

    Property Description
    Result Returns the result for the service-specific method.
    Response Returns the raw JSON response for the service-specific method.
    Headers Returns the response header information.
    StatusCode Returns the HTTP status code.

    Example request to access response headers

    var results = visualRecognition.MethodName(parameters);

var result = results.Result;            //  The result object
var responseHeaders = results.Headers;  //  The response headers
var responseJson = results.Response;    //  The raw response JSON
var statusCode = results.StatusCode;    //  The response status code

    The response contains fields for response headers, response JSON, and the status code.

    DetailedResponse

    Property Description
    Result Returns the result for the service-specific method.
    Response Returns the raw JSON response for the service-specific method.
    Headers Returns the response header information.
    StatusCode Returns the HTTP status code.

    Example request to access response headers

    private void Example()
{
    visualRecognition.MethodName(Callback, Parameters);
}

private void Callback(DetailedResponse<ResponseType> response, IBMError error)
{
    var result = response.Result;                 //  The result object
    var responseHeaders = response.Headers;       //  The response headers
    var responseJson = reresponsesults.Response;  //  The raw response JSON
    var statusCode = response.StatusCode;         //  The response status code
}

    Data labels

    You can remove customer data if you associate the customer and the data when you send the information to a service. First, you label the data with a customer ID, and then you can delete the data by the ID.

    • Use the X-Watson-Metadata header to associate a customer ID with the data. By adding a customer ID to a request, you indicate that it contains data that belongs to that customer.

      Specify a random or generic string for the customer ID. Do not include personal data, such as an email address. Pass the string customer_id={id} as the argument of the header.

    • Use the Delete labeled data method to remove data that is associated with a customer ID.

    Labeling data is used only by methods that accept customer data. For more information about Visual Recognition and labeling data, see Information security.

    For more information about how to pass headers, see Additional headers.

    Data collection

    By default, Visual Recognition service instances that are not part of Premium plans log requests and their results. Logging is done only to improve the services for future users. The logged data is not shared or made public. Logging is disabled for services that are part of Premium plans.

    To prevent IBM usage of your data for an API request, set the X-Watson-Learning-Opt-Out header parameter to true.

    You must set the header on each request that you do not want IBM to access for general service improvements.

    You can set the header by using the setDefaultHeaders method of the service object.

    You can set the header by using the headers parameter when you create the service object.

    You can set the header by using the set_default_headers method of the service object.

    You can set the header by using the add_default_headers method of the service object.

    You can set the header by using the SetDefaultHeaders method of the service object.

    You can set the header by adding it to the defaultHeaders property of the service object.

    You can set the header by using the WithHeader() method of the service object.

    Example request

    curl -u "apikey:{apikey}" -H "X-Watson-Learning-Opt-Out: true" "{url}/{method}"

    Example request

    Map<String, String> headers = new HashMap<String, String>();
headers.put("X-Watson-Learning-Opt-Out", "true");

visualRecognition.setDefaultHeaders(headers);

    Example request

    const VisualRecognitionV3 = require('ibm-watson/visual-recognition/v3');
const { IamAuthenticator } = require('ibm-watson/auth');

const visualRecognition = new VisualRecognitionV3({
  version: '{version}',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
  headers: {
    'X-Watson-Learning-Opt-Out': 'true'
  }
});

    Example request

    visual_recognition.set_default_headers({'x-watson-learning-opt-out': "true"})

    Example request

    visual_recognition.add_default_headers(headers: {"x-watson-learning-opt-out" => "true"})

    Example request

    import "net/http"

headers := http.Header{}
headers.Add("x-watson-learning-opt-out", "true")
visualRecognition.SetDefaultHeaders(headers)

    Example request

    visualRecognition.defaultHeaders["X-Watson-Learning-Opt-Out"] = "true"

    Example request

    IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

VisualRecognitionService visualRecognition = new VisualRecognitionService("{version}", authenticator);
visualRecognition.SetServiceUrl("{url}");

visualRecognition.WithHeader("X-Watson-Learning-Opt-Out", "true");

    Example request

    var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var visualRecognition = new VisualRecognitionService("{version}", authenticator);
visualRecognition.SetServiceUrl("{url}");

visualRecognition.WithHeader("X-Watson-Learning-Opt-Out", "true");

    Synchronous and asynchronous requests

    The Java SDK supports both synchronous (blocking) and asynchronous (non-blocking) execution of service methods. All service methods implement the ServiceCall interface.

    • To call a method synchronously, use the execute method of the ServiceCall interface. You can call the execute method directly from an instance of the service.
    • To call a method asynchronously, use the enqueue method of the ServiceCall interface to receive a callback when the response arrives. The ServiceCallback interface of the method's argument provides onResponse and onFailure methods that you override to handle the callback.

    The Ruby SDK supports both synchronous (blocking) and asynchronous (non-blocking) execution of service methods. All service methods implement the Concurrent::Async module. When you use the synchronous or asynchronous methods, an IVar object is returned. You access the DetailedResponse object by calling ivar_object.value.

    For more information about the Ivar object, see the IVar class docs.

    • To call a method synchronously, either call the method directly or use the .await chainable method of the Concurrent::Async module.

      Calling a method directly (without .await) returns a DetailedResponse object.

    • To call a method asynchronously, use the .async chainable method of the Concurrent::Async module.

    You can call the .await and .async methods directly from an instance of the service.

    Example synchronous request

    ReturnType returnValue = visualRecognition.method(parameters).execute();

    Example asynchronous request

    visualRecognition.method(parameters).enqueue(new ServiceCallback<ReturnType>() {
  @Override public void onResponse(ReturnType response) {
    . . .
  }
  @Override public void onFailure(Exception e) {
    . . .
  }
});

    Example synchronous request

    response = visual_recognition.method_name(parameters)

    or

    response = visual_recognition.await.method_name(parameters)

    Example asynchronous request

    response = visual_recognition.async.method_name(parameters)

    Related information

    Methods

    Classify an image

    Classify an image with the built-in or custom classifiers.

    GET /v3/classify

    Request

    Custom Headers

    • Accept-Language
      string

      The desired language of parts of the response. See the response for details.

      Allowable values: [en,ar,de,es,fr,it,ja,ko,pt-br,zh-cn,zh-tw]

      Default: en

    Query Parameters

    • version
      string

      Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

    • url
      string

      The URL of an image (.gif, .jpg, .png, .tif). The minimum recommended pixel density is 32X32 pixels, but the service tends to perform better with images that are at least 224 x 224 pixels. The maximum image size is 10 MB. Redirects are followed, so you can use a shortened URL.

    • owners
      string[]

      The categories of classifiers to apply. The classifier_ids parameter overrides owners, so make sure that classifier_ids is empty.

      • Use IBM to classify against the default general classifier. You get the same result if both classifier_ids and owners parameters are empty.
      • Use me to classify against all your custom classifiers. However, for better performance use classifier_ids to specify the specific custom classifiers to apply.
      • Use both IBM and me to analyze the image against both classifier categories.

      Allowable values: [IBM,me]

    • classifier_ids
      string[]

      Which classifiers to apply. Overrides the owners parameter. You can specify both custom and built-in classifier IDs. The built-in default classifier is used if both classifier_ids and owners parameters are empty.

      The following built-in classifier_ids require no training:

      • default: Returns classes from thousands of general tags.
      • food: Enhances specificity and accuracy for images of food items.
      • explicit: Evaluates whether the image might be pornographic.
    • threshold
      float

      The minimum score a class must have to be displayed in the response. Set the threshold to 0.0 to return all identified classes

      Constraints: 0 ≤ value ≤ 1

      Default: 0.5

    • curl -u "apikey:{apikey}" "{url}/v3/classify?url=https://watson-developer-cloud.github.io/doc-tutorial-downloads/visual-recognition/fruitbowl.jpg&version=2018-03-19"

              Response

              Response Body

              ClassifiedImages

              Results for all images.

              Status Code

              • 200

                success

              • 400

                Invalid request due to user input, for example:

                • Bad header parameter
                • Invalid output language
                • No input images
                • The size of the image file in the request is larger than the maximum supported size
              • 401

                No API key or the key is not valid.

              Example responses
              • {
  "images": [
    {
      "classifiers": [
        {
          "classifier_id": "default",
          "name": "default",
          "classes": [
            {
              "class": "diet (food)",
              "score": 0.571,
              "type_hierarchy": "/food/diet (food)"
            },
            {
              "class": "food",
              "score": 0.571
            },
            {
              "class": "fruit",
              "score": 0.825
            },
            {
              "class": "banana",
              "score": 0.518,
              "type_hierarchy": "/fruit/banana"
            },
            {
              "class": "Granny Smith",
              "score": 0.5,
              "type_hierarchy": "/fruit/pome/apple/eating apple/Granny Smith"
            },
            {
              "class": "eating apple",
              "score": 0.64
            },
            {
              "class": "apple",
              "score": 0.655
            },
            {
              "class": "pome",
              "score": 0.669
            },
            {
              "class": "Golden Delicious",
              "score": 0.5,
              "type_hierarchy": "/fruit/pome/apple/eating apple/Golden Delicious"
            },
            {
              "class": "olive color",
              "score": 0.942
            },
            {
              "class": "lemon yellow color",
              "score": 0.9
            }
          ]
        }
      ],
      "source_url": "https://watson-developer-cloud.github.io/doc-tutorial-downloads/visual-recognition/fruitbowl.jpg",
      "resolved_url": "https://watson-developer-cloud.github.io/doc-tutorial-downloads/visual-recognition/fruitbowl.jpg"
    }
  ],
  "images_processed": 1,
  "custom_classes": 0
}
              • {
  "code": "400",
  "error": "Error: Too many images in collection"
}
              • {
  "code": "401",
  "error": "Unauthorized"
}

              Classify images

              Classify images with built-in or custom classifiers.

              Classify images with built-in or custom classifiers.

              Classify images with built-in or custom classifiers.

              Classify images with built-in or custom classifiers.

              Classify images with built-in or custom classifiers.

              Classify images with built-in or custom classifiers.

              Classify images with built-in or custom classifiers.

              Classify images with built-in or custom classifiers.

              Classify images with built-in or custom classifiers.

              POST /v3/classify
              (visualRecognition *VisualRecognitionV3) Classify(classifyOptions *ClassifyOptions) (result *ClassifiedImages, response *core.DetailedResponse, err error)
              (visualRecognition *VisualRecognitionV3) ClassifyWithContext(ctx context.Context, classifyOptions *ClassifyOptions) (result *ClassifiedImages, response *core.DetailedResponse, err error)
              ServiceCall<ClassifiedImages> classify(ClassifyOptions classifyOptions)
              classify(params)
              classify(self,
        *,
        images_file: BinaryIO = None,
        images_filename: str = None,
        images_file_content_type: str = None,
        url: str = None,
        threshold: float = None,
        owners: List[str] = None,
        classifier_ids: List[str] = None,
        accept_language: str = None,
        **kwargs
    ) -> DetailedResponse
              classify(images_file: nil, images_filename: nil, images_file_content_type: nil, url: nil, threshold: nil, owners: nil, classifier_ids: nil, accept_language: nil)
              func classify(
    imagesFile: Data? = nil,
    imagesFilename: String? = nil,
    imagesFileContentType: String? = nil,
    url: String? = nil,
    threshold: Double? = nil,
    owners: [String]? = nil,
    classifierIDs: [String]? = nil,
    acceptLanguage: String? = nil,
    headers: [String: String]? = nil,
    completionHandler: @escaping (WatsonResponse<ClassifiedImages>?, WatsonError?) -> Void)
              Classify(System.IO.MemoryStream imagesFile = null, string imagesFilename = null, string imagesFileContentType = null, string url = null, float? threshold = null, List<string> owners = null, List<string> classifierIds = null, string acceptLanguage = null)
              Classify(Callback<ClassifiedImages> callback, System.IO.MemoryStream imagesFile = null, string imagesFilename = null, string imagesFileContentType = null, string url = null, float? threshold = null, List<string> owners = null, List<string> classifierIds = null, string acceptLanguage = null)

              Request

              Instantiate the ClassifyOptions struct and set the fields to provide parameter values for the Classify method.

              Use the ClassifyOptions.Builder to create a ClassifyOptions object that contains the parameter values for the classify method.

              Custom Headers

              • Accept-Language
                string

                The desired language of parts of the response. See the response for details.

                Allowable values: [en,ar,de,es,fr,it,ja,ko,pt-br,zh-cn,zh-tw]

                Default: en

              Query Parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              Form Parameters

              • images_file
                binary

                An image file (.gif, .jpg, .png, .tif) or .zip file with images. Maximum image size is 10 MB. Include no more than 20 images and limit the .zip file to 100 MB. Encode the image and .zip file names in UTF-8 if they contain non-ASCII characters. The service assumes UTF-8 encoding if it encounters non-ASCII characters.

                You can also include an image with the url parameter.

              • url
                string

                The URL of an image (.gif, .jpg, .png, .tif) to analyze. The minimum recommended pixel density is 32X32 pixels, but the service tends to perform better with images that are at least 224 x 224 pixels. The maximum image size is 10 MB.

                You can also include images with the images_file parameter.

              • threshold
                float

                The minimum score a class must have to be displayed in the response. Set the threshold to 0.0 to return all identified classes.

                Default: 0.5

              • owners
                string[]

                The categories of classifiers to apply. The classifier_ids parameter overrides owners, so make sure that classifier_ids is empty.

                • Use IBM to classify against the default general classifier. You get the same result if both classifier_ids and owners parameters are empty.
                • Use me to classify against all your custom classifiers. However, for better performance use classifier_ids to specify the specific custom classifiers to apply.
                • Use both IBM and me to analyze the image against both classifier categories.
              • classifier_ids
                string[]

                Which classifiers to apply. Overrides the owners parameter. You can specify both custom and built-in classifier IDs. The built-in default classifier is used if both classifier_ids and owners parameters are empty.

                The following built-in classifier IDs require no training:

                • default: Returns classes from thousands of general tags.
                • food: Enhances specificity and accuracy for images of food items.
                • explicit: Evaluates whether the image might be pornographic.

              parameter

              WithContext method only

              ClassifyOptions

              The Classify options.

              ClassifyOptions

              The classify options.

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • imagesFile
                NodeJS.ReadableStream | Buffer

                An image file (.gif, .jpg, .png, .tif) or .zip file with images. Maximum image size is 10 MB. Include no more than 20 images and limit the .zip file to 100 MB. Encode the image and .zip file names in UTF-8 if they contain non-ASCII characters. The service assumes UTF-8 encoding if it encounters non-ASCII characters.

                You can also include an image with the url parameter.

              • imagesFilename
                string

                The filename for imagesFile.

              • imagesFileContentType
                string

                The content type of imagesFile.

              • url
                string

                The URL of an image (.gif, .jpg, .png, .tif) to analyze. The minimum recommended pixel density is 32X32 pixels, but the service tends to perform better with images that are at least 224 x 224 pixels. The maximum image size is 10 MB.

                You can also include images with the images_file parameter.

              • threshold
                number

                The minimum score a class must have to be displayed in the response. Set the threshold to 0.0 to return all identified classes.

              • owners
                string[]

                The categories of classifiers to apply. The classifier_ids parameter overrides owners, so make sure that classifier_ids is empty.

                • Use IBM to classify against the default general classifier. You get the same result if both classifier_ids and owners parameters are empty.
                • Use me to classify against all your custom classifiers. However, for better performance use classifier_ids to specify the specific custom classifiers to apply.
                • Use both IBM and me to analyze the image against both classifier categories.
              • classifierIds
                string[]

                Which classifiers to apply. Overrides the owners parameter. You can specify both custom and built-in classifier IDs. The built-in default classifier is used if both classifier_ids and owners parameters are empty.

                The following built-in classifier IDs require no training:

                • default: Returns classes from thousands of general tags.
                • food: Enhances specificity and accuracy for images of food items.
                • explicit: Evaluates whether the image might be pornographic.
              • acceptLanguage
                string

                The desired language of parts of the response. See the response for details.

                Allowable values: [en,ar,de,es,fr,it,ja,ko,pt-br,zh-cn,zh-tw]

                Default: en

              parameters

              • self.version
                str

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • images_file
                BinaryIO

                An image file (.gif, .jpg, .png, .tif) or .zip file with images. Maximum image size is 10 MB. Include no more than 20 images and limit the .zip file to 100 MB. Encode the image and .zip file names in UTF-8 if they contain non-ASCII characters. The service assumes UTF-8 encoding if it encounters non-ASCII characters.

                You can also include an image with the url parameter.

              • images_filename
                str

                The filename for images_file.

              • images_file_content_type
                str

                The content type of images_file.

              • url
                str

                The URL of an image (.gif, .jpg, .png, .tif) to analyze. The minimum recommended pixel density is 32X32 pixels, but the service tends to perform better with images that are at least 224 x 224 pixels. The maximum image size is 10 MB.

                You can also include images with the images_file parameter.

              • threshold
                float

                The minimum score a class must have to be displayed in the response. Set the threshold to 0.0 to return all identified classes.

              • owners
                List[str]

                The categories of classifiers to apply. The classifier_ids parameter overrides owners, so make sure that classifier_ids is empty.

                • Use IBM to classify against the default general classifier. You get the same result if both classifier_ids and owners parameters are empty.
                • Use me to classify against all your custom classifiers. However, for better performance use classifier_ids to specify the specific custom classifiers to apply.
                • Use both IBM and me to analyze the image against both classifier categories.
              • classifier_ids
                List[str]

                Which classifiers to apply. Overrides the owners parameter. You can specify both custom and built-in classifier IDs. The built-in default classifier is used if both classifier_ids and owners parameters are empty.

                The following built-in classifier IDs require no training:

                • default: Returns classes from thousands of general tags.
                • food: Enhances specificity and accuracy for images of food items.
                • explicit: Evaluates whether the image might be pornographic.
              • accept_language
                str

                The desired language of parts of the response. See the response for details.

                Allowable values: [en,ar,de,es,fr,it,ja,ko,pt-br,zh-cn,zh-tw]

                Default: en

              parameters

              • version
                String

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • images_file
                File

                An image file (.gif, .jpg, .png, .tif) or .zip file with images. Maximum image size is 10 MB. Include no more than 20 images and limit the .zip file to 100 MB. Encode the image and .zip file names in UTF-8 if they contain non-ASCII characters. The service assumes UTF-8 encoding if it encounters non-ASCII characters.

                You can also include an image with the url parameter.

              • images_filename
                String

                The filename for images_file.

              • images_file_content_type
                String

                The content type of images_file.

              • url
                String

                The URL of an image (.gif, .jpg, .png, .tif) to analyze. The minimum recommended pixel density is 32X32 pixels, but the service tends to perform better with images that are at least 224 x 224 pixels. The maximum image size is 10 MB.

                You can also include images with the images_file parameter.

              • threshold
                Float

                The minimum score a class must have to be displayed in the response. Set the threshold to 0.0 to return all identified classes.

              • owners
                Array[String]

                The categories of classifiers to apply. The classifier_ids parameter overrides owners, so make sure that classifier_ids is empty.

                • Use IBM to classify against the default general classifier. You get the same result if both classifier_ids and owners parameters are empty.
                • Use me to classify against all your custom classifiers. However, for better performance use classifier_ids to specify the specific custom classifiers to apply.
                • Use both IBM and me to analyze the image against both classifier categories.
              • classifier_ids
                Array[String]

                Which classifiers to apply. Overrides the owners parameter. You can specify both custom and built-in classifier IDs. The built-in default classifier is used if both classifier_ids and owners parameters are empty.

                The following built-in classifier IDs require no training:

                • default: Returns classes from thousands of general tags.
                • food: Enhances specificity and accuracy for images of food items.
                • explicit: Evaluates whether the image might be pornographic.
              • accept_language
                String

                The desired language of parts of the response. See the response for details.

                Allowable values: [en,ar,de,es,fr,it,ja,ko,pt-br,zh-cn,zh-tw]

                Default: en

              parameters

              • version
                String

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • imagesFile
                Data

                An image file (.gif, .jpg, .png, .tif) or .zip file with images. Maximum image size is 10 MB. Include no more than 20 images and limit the .zip file to 100 MB. Encode the image and .zip file names in UTF-8 if they contain non-ASCII characters. The service assumes UTF-8 encoding if it encounters non-ASCII characters.

                You can also include an image with the url parameter.

              • imagesFilename
                String

                The filename for imagesFile.

              • imagesFileContentType
                String

                The content type of imagesFile.

              • url
                String

                The URL of an image (.gif, .jpg, .png, .tif) to analyze. The minimum recommended pixel density is 32X32 pixels, but the service tends to perform better with images that are at least 224 x 224 pixels. The maximum image size is 10 MB.

                You can also include images with the images_file parameter.

              • threshold
                Double

                The minimum score a class must have to be displayed in the response. Set the threshold to 0.0 to return all identified classes.

              • owners
                [String]

                The categories of classifiers to apply. The classifier_ids parameter overrides owners, so make sure that classifier_ids is empty.

                • Use IBM to classify against the default general classifier. You get the same result if both classifier_ids and owners parameters are empty.
                • Use me to classify against all your custom classifiers. However, for better performance use classifier_ids to specify the specific custom classifiers to apply.
                • Use both IBM and me to analyze the image against both classifier categories.
              • classifierIDs
                [String]

                Which classifiers to apply. Overrides the owners parameter. You can specify both custom and built-in classifier IDs. The built-in default classifier is used if both classifier_ids and owners parameters are empty.

                The following built-in classifier IDs require no training:

                • default: Returns classes from thousands of general tags.
                • food: Enhances specificity and accuracy for images of food items.
                • explicit: Evaluates whether the image might be pornographic.
              • acceptLanguage
                String

                The desired language of parts of the response. See the response for details.

                Allowable values: [en,ar,de,es,fr,it,ja,ko,pt-br,zh-cn,zh-tw]

                Default: en

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • imagesFile
                System.IO.MemoryStream

                An image file (.gif, .jpg, .png, .tif) or .zip file with images. Maximum image size is 10 MB. Include no more than 20 images and limit the .zip file to 100 MB. Encode the image and .zip file names in UTF-8 if they contain non-ASCII characters. The service assumes UTF-8 encoding if it encounters non-ASCII characters.

                You can also include an image with the url parameter.

              • imagesFilename
                string

                The filename for imagesFile.

              • imagesFileContentType
                string

                The content type of imagesFile.

              • url
                string

                The URL of an image (.gif, .jpg, .png, .tif) to analyze. The minimum recommended pixel density is 32X32 pixels, but the service tends to perform better with images that are at least 224 x 224 pixels. The maximum image size is 10 MB.

                You can also include images with the images_file parameter.

              • threshold
                float?

                The minimum score a class must have to be displayed in the response. Set the threshold to 0.0 to return all identified classes.

              • owners
                List<string>

                The categories of classifiers to apply. The classifier_ids parameter overrides owners, so make sure that classifier_ids is empty.

                • Use IBM to classify against the default general classifier. You get the same result if both classifier_ids and owners parameters are empty.
                • Use me to classify against all your custom classifiers. However, for better performance use classifier_ids to specify the specific custom classifiers to apply.
                • Use both IBM and me to analyze the image against both classifier categories.
              • classifierIds
                List<string>

                Which classifiers to apply. Overrides the owners parameter. You can specify both custom and built-in classifier IDs. The built-in default classifier is used if both classifier_ids and owners parameters are empty.

                The following built-in classifier IDs require no training:

                • default: Returns classes from thousands of general tags.
                • food: Enhances specificity and accuracy for images of food items.
                • explicit: Evaluates whether the image might be pornographic.
              • acceptLanguage
                string

                The desired language of parts of the response. See the response for details.

                Allowable values: [en,ar,de,es,fr,it,ja,ko,pt-br,zh-cn,zh-tw]

                Default: en

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • imagesFile
                System.IO.MemoryStream

                An image file (.gif, .jpg, .png, .tif) or .zip file with images. Maximum image size is 10 MB. Include no more than 20 images and limit the .zip file to 100 MB. Encode the image and .zip file names in UTF-8 if they contain non-ASCII characters. The service assumes UTF-8 encoding if it encounters non-ASCII characters.

                You can also include an image with the url parameter.

              • imagesFilename
                string

                The filename for imagesFile.

              • imagesFileContentType
                string

                The content type of imagesFile.

              • url
                string

                The URL of an image (.gif, .jpg, .png, .tif) to analyze. The minimum recommended pixel density is 32X32 pixels, but the service tends to perform better with images that are at least 224 x 224 pixels. The maximum image size is 10 MB.

                You can also include images with the images_file parameter.

              • threshold
                float?

                The minimum score a class must have to be displayed in the response. Set the threshold to 0.0 to return all identified classes.

              • owners
                List<string>

                The categories of classifiers to apply. The classifier_ids parameter overrides owners, so make sure that classifier_ids is empty.

                • Use IBM to classify against the default general classifier. You get the same result if both classifier_ids and owners parameters are empty.
                • Use me to classify against all your custom classifiers. However, for better performance use classifier_ids to specify the specific custom classifiers to apply.
                • Use both IBM and me to analyze the image against both classifier categories.
              • classifierIds
                List<string>

                Which classifiers to apply. Overrides the owners parameter. You can specify both custom and built-in classifier IDs. The built-in default classifier is used if both classifier_ids and owners parameters are empty.

                The following built-in classifier IDs require no training:

                • default: Returns classes from thousands of general tags.
                • food: Enhances specificity and accuracy for images of food items.
                • explicit: Evaluates whether the image might be pornographic.
              • acceptLanguage
                string

                The desired language of parts of the response. See the response for details.

                Allowable values: [en,ar,de,es,fr,it,ja,ko,pt-br,zh-cn,zh-tw]

                Default: en

              • curl -X POST -u "apikey:{apikey}" -F "images_file=@fruitbowl.jpg" -F "threshold=0.6" -F "owners=me" "{url}/v3/classify?version=2018-03-19"

                Download example image fruitbowl.jpg

              • curl -X POST -u "apikey:{apikey}" -F "images_file=@fruitbowl.jpg" -F "classifier_ids=food" "{url}/v3/classify?version=2018-03-19"

                Download example image fruitbowl.jpg

              • IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

VisualRecognitionService visualRecognition = new VisualRecognitionService("2018-03-19", authenticator);
visualRecognition.SetServiceUrl("{url}");

DetailedResponse<ClassifiedImages> result;
using (FileStream fs = File.OpenRead("./fruitbowl.jpg"))
{
    using (MemoryStream ms = new MemoryStream())
    {
        fs.CopyTo(ms);
        result = service.Classify(
            imagesFile: ms,
            imagesFilename: "fruitbowl.jpg",  
            threshold: 0.6f,
            owners: new List<string>()
            {
                "me"
            }
            );
    }
}

Console.WriteLine(result.Response);
              • IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

VisualRecognitionService visualRecognition = new VisualRecognitionService("2018-03-19", authenticator);
visualRecognition.SetServiceUrl("{url}");

DetailedResponse<ClassifiedImages> result;
using (FileStream fs = File.OpenRead("./fruitbowl.jpg"))
{
    using (MemoryStream ms = new MemoryStream())
    {
        fs.CopyTo(ms);
        result = service.Classify(
            imagesFile: ms,
            imagesFilename: "fruitbowl.jpg",  
            classifierIds: new List<string>()
            {
                "food"
            }
            );
    }
}

Console.WriteLine(result.Response);
              • package main

import (
  "encoding/json"
  "fmt"
  "github.com/IBM/go-sdk-core/core"
  "github.com/watson-developer-cloud/go-sdk/visualrecognitionv3"
  "os"
)

func main() {
  authenticator := &core.IamAuthenticator{
    ApiKey: "{apikey}",
  }

  options := &visualrecognitionv3.VisualRecognitionV3Options{
    Version: "2018-03-19",
    Authenticator: authenticator,
  }

  visualRecognition, visualRecognitionErr := visualrecognitionv3.NewVisualRecognitionV3(options)

  if visualRecognitionErr != nil {
    panic(visualRecognitionErr)
  }

  visualRecognition.SetServiceURL("{url}")

  imageFile, imageFileErr := os.Open("./fruitbowl.jpg")
  if imageFileErr != nil {
    panic(imageFileErr)
  }
  defer imageFile.Close()

  result, response, responseErr := visualRecognition.Classify(
    &visualrecognitionv3.ClassifyOptions{
      ImagesFile: imageFile,
      Threshold: core.Float32Ptr(0.6),
      Owners: []string{"me"},
    },
  )
  if responseErr != nil {
    panic(responseErr)
  }
  b, _ := json.MarshalIndent(result, "", "  ")
  fmt.Println(string(b))
}

                Download example image fruitbowl.jpg

              • package main

import (
  "encoding/json"
  "fmt"
  "github.com/IBM/go-sdk-core/core"
  "github.com/watson-developer-cloud/go-sdk/visualrecognitionv3"
  "os"
)

func main() {
  authenticator := &core.IamAuthenticator{
    ApiKey: "{apikey}",
  }

  options := &visualrecognitionv3.VisualRecognitionV3Options{
    Version: "2018-03-19",
    Authenticator: authenticator,
  }

  visualRecognition, visualRecognitionErr := visualrecognitionv3.NewVisualRecognitionV3(options)

  if visualRecognitionErr != nil {
    panic(visualRecognitionErr)
  }

  imageFile, imageFileErr := os.Open("./fruitbowl.jpg")
  if imageFileErr != nil {
    panic(imageFileErr)
  }
  defer imageFile.Close()

  result, response, responseErr := visualRecognition.Classify(
    &visualrecognitionv3.ClassifyOptions{
      ImagesFile: imageFile,
      ClassifierIds: []string{"food"},
    },
  )
  if responseErr != nil {
    panic(responseErr)
  }
  result := visualRecognition.GetClassifyResult(response)
  b, _ := json.MarshalIndent(result, "", "  ")
  fmt.Println(string(b))
}
              • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
VisualRecognition visualRecognition = new VisualRecognition("2018-03-19", authenticator);
visualRecognition.setServiceUrl("{url}");

InputStream imagesStream = new FileInputStream("./fruitbowl.jpg");
ClassifyOptions classifyOptions = new ClassifyOptions.Builder()
  .imagesFile(imagesStream)
  .classifierIds(Arrays.asList("food"))
  .build();
ClassifiedImages result = visualRecognition.classify(classifyOptions).execute().getResult();
System.out.println(result);x

                Download example image fruitbowl.jpg

              • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
VisualRecognition visualRecognition = new VisualRecognition("2018-03-19", authenticator);
visualRecognition.setServiceUrl("{url}");

InputStream imagesStream = new FileInputStream("./fruitbowl.jpg");
ClassifyOptions classifyOptions = new ClassifyOptions.Builder()
  .imagesFile(imagesStream)
  .classifierIds(Arrays.asList("food"))
  .build();
ClassifiedImages result = visualRecognition.classify(classifyOptions).execute().getResult();
System.out.println(result);
              • const fs = require('fs');
const VisualRecognitionV3 = require('ibm-watson/visual-recognition/v3');
const { IamAuthenticator } = require('ibm-watson/auth');

const visualRecognition = new VisualRecognitionV3({
  version: '2018-03-19',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
});

const classifyParams = {
  imagesFile: fs.createReadStream('./fruitbowl.jpg'),
  owners: ['me'],
  threshold: 0.6,
};

visualRecognition.classify(classifyParams)
  .then(response => {
    const classifiedImages = response.result;
    console.log(JSON.stringify(classifiedImages, null, 2));
  })
  .catch(err => {
    console.log('error:', err);
  });

                Download example image fruitbowl.jpg

              • const fs = require('fs');
const VisualRecognitionV3 = require('ibm-watson/visual-recognition/v3');
const { IamAuthenticator } = require('ibm-watson/auth');

const visualRecognition = new VisualRecognitionV3({
  version: '2018-03-19',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
});

const classifyParams = {
  images_file: fs.createReadStream('./fruitbowl.jpg'),
  classifier_ids: ['food'],
};

visualRecognition.classify(classifyParams)
  .then(response => {
    const classifiedImages = response.result;
    console.log(JSON.stringify(classifiedImages, null, 2));
  })
  .catch(err => {
    console.log('error:', err);
  });
              • import json
from ibm_watson import VisualRecognitionV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

authenticator = IAMAuthenticator('{apikey}')
visual_recognition = VisualRecognitionV3(
    version='2018-03-19',
    authenticator=authenticator
)

visual_recognition.set_service_url('{url}')

with open('./fruitbowl.jpg', 'rb') as images_file:
    classes = visual_recognition.classify(
        images_file=images_file,
        threshold='0.6',
        owners=["me"]).get_result()
    print(json.dumps(classes, indent=2))

                Download example image fruitbowl.jpg

              • import json
from ibm_watson import VisualRecognitionV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

authenticator = IAMAuthenticator('{apikey}')
visual_recognition = VisualRecognitionV3(
    version='2018-03-19',
    authenticator=authenticator
)

visual_recognition.set_service_url('{url}')

with open('./fruitbowl.jpg', 'rb') as images_file:
    classes = visual_recognition.classify(
        images_file=images_file,
        classifier_ids=["food"]).get_result()
    print(json.dumps(classes, indent=2))
              • require "json"
require "ibm_watson/authenticators"
require "ibm_watson/visual_recognition_v3"
include IBMWatson

authenticator = Authenticators::IamAuthenticator.new(
  apikey: "{apikey}"
)
visual_recognition = VisualRecognitionV3.new(
  version: "2018-03-19",
  authenticator: authenticator
)
visual_recognition.service_url = "{url}"

File.open("./fruitbowl.jpg") do |images_file|
  classes = visual_recognition.classify(
    images_file: images_file,
    threshold: "0.6",
    owners: ["me"]
  )
  puts JSON.pretty_generate(classes.result)
end

                Download example image fruitbowl.jpg

              • require "json"
require "ibm_watson/authenticators"
require "ibm_watson/visual_recognition_v3"
include IBMWatson

authenticator = Authenticators::IamAuthenticator.new(
  apikey: "{apikey}"
)
visual_recognition = VisualRecognitionV3.new(
  version: "2018-03-19",
  authenticator: authenticator
)
visual_recognition.service_url = "{url}"

File.open("./fruitbowl.jpg") do |images_file|
  classes = visual_recognition.classify(
    images_file: images_file,
    classifier_ids: ["food"]
  )
  puts JSON.pretty_generate(classes.result)
end
              • let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let visualRecognition = VisualRecognition(version: "2018-03-19", authenticator: authenticator)
visualRecognition.serviceURL = "{url}"

let url = Bundle.main.url(forResource: "fruitbowl", withExtension: "jpg")
let fruitbowl = try? Data(contentsOf: url!)

visualRecognition.classify(imagesFile: fruitbowl, threshold: 0.6, owners: ["me"]) {
  response, error in

  guard let result = response?.result else {
    print(error?.localizedDescription ?? "unknown error")
    return
  }

  print(result)
}

                Download example image fruitbowl.jpg

              • let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let visualRecognition = VisualRecognition(version: "2018-03-19", authenticator: authenticator)
visualRecognition.serviceURL = "{url}"

let url = Bundle.main.url(forResource: "fruitbowl", withExtension: "jpg")
let fruitbowl = try? Data(contentsOf: url!)

visualRecognition.classify(imagesFile: fruitbowl, classifierIDs: ["food"]) {
  response, error in

  guard let result = response?.result else {
    print(error?.localizedDescription ?? "unknown error")
    return
  }

  print(result)
}
              • var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var visualRecognition = new VisualRecognitionService("2018-03-19", authenticator);
visualRecognition.SetServiceUrl("{url}");

ClassifiedImages classifyResponse = null;
using (FileStream fs = File.OpenRead("./fruitbowl.jpg"))
{
    using (MemoryStream ms = new MemoryStream())
    {
        fs.CopyTo(ms);
        VisualRecognitionService.Classify(
        callback: (DetailedResponse<ClassifiedImages> response, IBMError error) =>
        {
            Log.Debug("VisualRecognitionServiceV3", "Classify result: {0}", response.Response);
            classifyResponse = response.Result;
        },
            imagesFile: ms,
            imagesFilename: "fruitbowl.jpg",
            threshold: 0.6f,
            owners: new List<string>()
            {
                "me"
            }
    );

        while (classifyResponse == null)
            yield return null;
    }
}
              • var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var visualRecognition = new VisualRecognitionService("2018-03-19", authenticator);
visualRecognition.SetServiceUrl("{url}");

ClassifiedImages classifyResponse = null;
using (FileStream fs = File.OpenRead("./fruitbowl.jpg"))
{
    using (MemoryStream ms = new MemoryStream())
    {
        fs.CopyTo(ms);
        service.Classify(
        callback: (DetailedResponse<ClassifiedImages> response, IBMError error) =>
        {
            Log.Debug("VisualRecognitionServiceV3", "Classify result: {0}", response.Response);
            classifyResponse = response.Result;
        },
            imagesFile: ms,
            imagesFilename: "fruitbowl.jpg",
            owners: new List<string>()
            {
                "food"
            }
    );

        while (classifyResponse == null)
            yield return null;
    }
}

              Response

              Response Body

              ClassifiedImages

              Results for all images.

              ClassifiedImages

              Results for all images.

              ClassifiedImages

              Results for all images.

              ClassifiedImages

              Results for all images.

              ClassifiedImages

              Results for all images.

              ClassifiedImages

              Results for all images.

              ClassifiedImages

              Results for all images.

              ClassifiedImages

              Results for all images.

              ClassifiedImages

              Results for all images.

              Status Code

              • 200

                success

              • 400

                Invalid request due to user input, for example:

                • Bad JSON input
                • Bad query parameter or header
                • Invalid output language
                • No input images
                • The size of the image file in the request is larger than the maximum supported size
                • Corrupt .zip file
              • 401

                No API key or the key is not valid.

              • 413

                The .zip file is too large.

              Example responses
              • {
  "images": [
    {
      "classifiers": [
        {
          "classifier_id": "roundPlusBanana_1758279329",
          "name": "roundPlusBanana",
          "classes": [
            {
              "class": "fruit",
              "score": 0.788
            },
            {
              "class": "olive color",
              "score": 0.973
            },
            {
              "class": "lemon yellow color",
              "score": 0.789
            }
          ]
        }
      ],
      "image": "fruitbowl.jpg"
    }
  ],
  "images_processed": 1,
  "custom_classes": 6
}
              • {
  "images": [
    {
      "classifiers": [
        {
          "classifier_id": "roundPlusBanana_1758279329",
          "name": "roundPlusBanana",
          "classes": [
            {
              "class": "fruit",
              "score": 0.788
            },
            {
              "class": "olive color",
              "score": 0.973
            },
            {
              "class": "lemon yellow color",
              "score": 0.789
            }
          ]
        }
      ],
      "image": "fruitbowl.jpg"
    }
  ],
  "images_processed": 1,
  "custom_classes": 6
}
              • {
  "code": "400",
  "error": "Error: Too many images in collection"
}
              • {
  "code": "400",
  "error": "Error: Too many images in collection"
}
              • {
  "code": "401",
  "error": "Unauthorized"
}
              • {
  "code": "401",
  "error": "Unauthorized"
}

              Create a classifier

              Train a new multi-faceted classifier on the uploaded image data. Create your custom classifier with positive or negative example training images. Include at least two sets of examples, either two positive example files or one positive and one negative file. You can upload a maximum of 256 MB per call.

              Tips when creating:

              • If you set the X-Watson-Learning-Opt-Out header parameter to true when you create a classifier, the example training images are not stored. Save your training images locally. For more information, see Data collection.

              • Encode all names in UTF-8 if they contain non-ASCII characters (.zip and image file names, and classifier and class names). The service assumes UTF-8 encoding if it encounters non-ASCII characters.

              Train a new multi-faceted classifier on the uploaded image data. Create your custom classifier with positive or negative example training images. Include at least two sets of examples, either two positive example files or one positive and one negative file. You can upload a maximum of 256 MB per call.

              Tips when creating:

              • If you set the X-Watson-Learning-Opt-Out header parameter to true when you create a classifier, the example training images are not stored. Save your training images locally. For more information, see Data collection.

              • Encode all names in UTF-8 if they contain non-ASCII characters (.zip and image file names, and classifier and class names). The service assumes UTF-8 encoding if it encounters non-ASCII characters.

              Train a new multi-faceted classifier on the uploaded image data. Create your custom classifier with positive or negative example training images. Include at least two sets of examples, either two positive example files or one positive and one negative file. You can upload a maximum of 256 MB per call.

              Tips when creating:

              • If you set the X-Watson-Learning-Opt-Out header parameter to true when you create a classifier, the example training images are not stored. Save your training images locally. For more information, see Data collection.

              • Encode all names in UTF-8 if they contain non-ASCII characters (.zip and image file names, and classifier and class names). The service assumes UTF-8 encoding if it encounters non-ASCII characters.

              Train a new multi-faceted classifier on the uploaded image data. Create your custom classifier with positive or negative example training images. Include at least two sets of examples, either two positive example files or one positive and one negative file. You can upload a maximum of 256 MB per call.

              Tips when creating:

              • If you set the X-Watson-Learning-Opt-Out header parameter to true when you create a classifier, the example training images are not stored. Save your training images locally. For more information, see Data collection.

              • Encode all names in UTF-8 if they contain non-ASCII characters (.zip and image file names, and classifier and class names). The service assumes UTF-8 encoding if it encounters non-ASCII characters.

              Train a new multi-faceted classifier on the uploaded image data. Create your custom classifier with positive or negative example training images. Include at least two sets of examples, either two positive example files or one positive and one negative file. You can upload a maximum of 256 MB per call.

              Tips when creating:

              • If you set the X-Watson-Learning-Opt-Out header parameter to true when you create a classifier, the example training images are not stored. Save your training images locally. For more information, see Data collection.

              • Encode all names in UTF-8 if they contain non-ASCII characters (.zip and image file names, and classifier and class names). The service assumes UTF-8 encoding if it encounters non-ASCII characters.

              Train a new multi-faceted classifier on the uploaded image data. Create your custom classifier with positive or negative example training images. Include at least two sets of examples, either two positive example files or one positive and one negative file. You can upload a maximum of 256 MB per call.

              Tips when creating:

              • If you set the X-Watson-Learning-Opt-Out header parameter to true when you create a classifier, the example training images are not stored. Save your training images locally. For more information, see Data collection.

              • Encode all names in UTF-8 if they contain non-ASCII characters (.zip and image file names, and classifier and class names). The service assumes UTF-8 encoding if it encounters non-ASCII characters.

              Train a new multi-faceted classifier on the uploaded image data. Create your custom classifier with positive or negative example training images. Include at least two sets of examples, either two positive example files or one positive and one negative file. You can upload a maximum of 256 MB per call.

              Tips when creating:

              • If you set the X-Watson-Learning-Opt-Out header parameter to true when you create a classifier, the example training images are not stored. Save your training images locally. For more information, see Data collection.

              • Encode all names in UTF-8 if they contain non-ASCII characters (.zip and image file names, and classifier and class names). The service assumes UTF-8 encoding if it encounters non-ASCII characters.

              Train a new multi-faceted classifier on the uploaded image data. Create your custom classifier with positive or negative example training images. Include at least two sets of examples, either two positive example files or one positive and one negative file. You can upload a maximum of 256 MB per call.

              Tips when creating:

              • If you set the X-Watson-Learning-Opt-Out header parameter to true when you create a classifier, the example training images are not stored. Save your training images locally. For more information, see Data collection.

              • Encode all names in UTF-8 if they contain non-ASCII characters (.zip and image file names, and classifier and class names). The service assumes UTF-8 encoding if it encounters non-ASCII characters.

              Train a new multi-faceted classifier on the uploaded image data. Create your custom classifier with positive or negative example training images. Include at least two sets of examples, either two positive example files or one positive and one negative file. You can upload a maximum of 256 MB per call.

              Tips when creating:

              • If you set the X-Watson-Learning-Opt-Out header parameter to true when you create a classifier, the example training images are not stored. Save your training images locally. For more information, see Data collection.

              • Encode all names in UTF-8 if they contain non-ASCII characters (.zip and image file names, and classifier and class names). The service assumes UTF-8 encoding if it encounters non-ASCII characters.

              POST /v3/classifiers
              (visualRecognition *VisualRecognitionV3) CreateClassifier(createClassifierOptions *CreateClassifierOptions) (result *Classifier, response *core.DetailedResponse, err error)
              (visualRecognition *VisualRecognitionV3) CreateClassifierWithContext(ctx context.Context, createClassifierOptions *CreateClassifierOptions) (result *Classifier, response *core.DetailedResponse, err error)
              ServiceCall<Classifier> createClassifier(CreateClassifierOptions createClassifierOptions)
              createClassifier(params)
              create_classifier(self,
        name: str,
        positive_examples: Dict[str, BinaryIO],
        *,
        negative_examples: BinaryIO = None,
        negative_examples_filename: str = None,
        **kwargs
    ) -> DetailedResponse
              create_classifier(name:, positive_examples:, negative_examples: nil, negative_examples_filename: nil)
              func createClassifier(
    name: String,
    positiveExamples: [String: Data],
    negativeExamples: Data? = nil,
    negativeExamplesFilename: String? = nil,
    headers: [String: String]? = nil,
    completionHandler: @escaping (WatsonResponse<Classifier>?, WatsonError?) -> Void)
              CreateClassifier(string name, Dictionary<string, System.IO.MemoryStream> positiveExamples, System.IO.MemoryStream negativeExamples = null, string negativeExamplesFilename = null)
              CreateClassifier(Callback<Classifier> callback, string name, Dictionary<string, System.IO.MemoryStream> positiveExamples, System.IO.MemoryStream negativeExamples = null, string negativeExamplesFilename = null)

              Request

              Instantiate the CreateClassifierOptions struct and set the fields to provide parameter values for the CreateClassifier method.

              Use the CreateClassifierOptions.Builder to create a CreateClassifierOptions object that contains the parameter values for the createClassifier method.

              Query Parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              Form Parameters

              • name
                string

                The name of the new classifier. Encode special characters in UTF-8.

              • {classname}_positive_examples
                binary

                A .zip file of images that depict the visual subject of a class in the new classifier. You can include more than one positive example file in a call.

                Specify the parameter name by appending _positive_examples to the class name. For example, goldenretriever_positive_examples creates the class goldenretriever. The string cannot contain the following characters: $ * - { } \ | / ' " ` [ ].

                Include at least 10 images in .jpg or .png format. The minimum recommended image resolution is 32X32 pixels. The maximum number of images is 10,000 images or 100 MB per .zip file.

                Encode special characters in the file name in UTF-8.

                Constraints: Value must match regular expression ^(?![-"$'*/[\\]`{|}])[\p{L}\p{N}#%&()+,:;<=>?@^~]*$

              • negative_examples
                binary

                A .zip file of images that do not depict the visual subject of any of the classes of the new classifier. Must contain a minimum of 10 images.

                Encode special characters in the file name in UTF-8.

              parameter

              WithContext method only

              CreateClassifierOptions

              The CreateClassifier options.

              CreateClassifierOptions

              The createClassifier options.

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • name
                string

                The name of the new classifier. Encode special characters in UTF-8.

              • positiveExamples
                Record<string, NodeJS.ReadableStream | Buffer>

                A dictionary that contains the value for each classname. The value is a .zip file of images that depict the visual subject of a class in the new classifier. You can include more than one positive example file in a call.

                Specify the parameter name by appending _positive_examples to the class name. For example, goldenretriever_positive_examples creates the class goldenretriever. The string cannot contain the following characters: $ * - { } \ | / ' " ` [ ].

                Include at least 10 images in .jpg or .png format. The minimum recommended image resolution is 32X32 pixels. The maximum number of images is 10,000 images or 100 MB per .zip file.

                Encode special characters in the file name in UTF-8.

                Constraints: Value must match regular expression /^(?![-\"$'*\/[\\\\]`{|}])[\\p{L}\\p{N}#%&()+,:;<=>?@^~]*$/

              • negativeExamples
                NodeJS.ReadableStream | Buffer

                A .zip file of images that do not depict the visual subject of any of the classes of the new classifier. Must contain a minimum of 10 images.

                Encode special characters in the file name in UTF-8.

              • negativeExamplesFilename
                string

                The filename for negativeExamples.

              parameters

              • self.version
                str

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • name
                str

                The name of the new classifier. Encode special characters in UTF-8.

              • positive_examples
                dict

                A dictionary that contains the value for each classname. The value is a .zip file of images that depict the visual subject of a class in the new classifier. You can include more than one positive example file in a call.

                Specify the parameter name by appending _positive_examples to the class name. For example, goldenretriever_positive_examples creates the class goldenretriever. The string cannot contain the following characters: $ * - { } \ | / ' " ` [ ].

                Include at least 10 images in .jpg or .png format. The minimum recommended image resolution is 32X32 pixels. The maximum number of images is 10,000 images or 100 MB per .zip file.

                Encode special characters in the file name in UTF-8.

                Constraints: Value must match regular expression /^(?![-\"$'*\/[\\\\]`{|}])[\\p{L}\\p{N}#%&()+,:;<=>?@^~]*$/

              • negative_examples
                BinaryIO

                A .zip file of images that do not depict the visual subject of any of the classes of the new classifier. Must contain a minimum of 10 images.

                Encode special characters in the file name in UTF-8.

              • negative_examples_filename
                str

                The filename for negative_examples.

              parameters

              • version
                String

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • name
                String

                The name of the new classifier. Encode special characters in UTF-8.

              • positive_examples
                File

                A .zip file of images that depict the visual subject of a class in the new classifier. You can include more than one positive example file in a call.

                Specify the parameter name by appending _positive_examples to the class name. For example, goldenretriever_positive_examples creates the class goldenretriever. The string cannot contain the following characters: $ * - { } \ | / ' " ` [ ].

                Include at least 10 images in .jpg or .png format. The minimum recommended image resolution is 32X32 pixels. The maximum number of images is 10,000 images or 100 MB per .zip file.

                Encode special characters in the file name in UTF-8.

                Constraints: Value must match regular expression /^(?![-\"$'*\/[\\\\]`{|}])[\\p{L}\\p{N}#%&()+,:;<=>?@^~]*$/

              • negative_examples
                File

                A .zip file of images that do not depict the visual subject of any of the classes of the new classifier. Must contain a minimum of 10 images.

                Encode special characters in the file name in UTF-8.

              • negative_examples_filename
                String

                The filename for negative_examples.

              parameters

              • version
                String

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • name
                String

                The name of the new classifier. Encode special characters in UTF-8.

              • positiveExamples
                [String: Data]

                A dictionary that contains the value for each classname. The value is a .zip file of images that depict the visual subject of a class in the new classifier. You can include more than one positive example file in a call.

                Specify the parameter name by appending _positive_examples to the class name. For example, goldenretriever_positive_examples creates the class goldenretriever. The string cannot contain the following characters: $ * - { } \ | / ' " ` [ ].

                Include at least 10 images in .jpg or .png format. The minimum recommended image resolution is 32X32 pixels. The maximum number of images is 10,000 images or 100 MB per .zip file.

                Encode special characters in the file name in UTF-8.

                Constraints: Value must match regular expression /^(?![-\"$'*\/[\\\\]`{|}])[\\p{L}\\p{N}#%&()+,:;<=>?@^~]*$/

              • negativeExamples
                Data

                A .zip file of images that do not depict the visual subject of any of the classes of the new classifier. Must contain a minimum of 10 images.

                Encode special characters in the file name in UTF-8.

              • negativeExamplesFilename
                String

                The filename for negativeExamples.

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • name
                string

                The name of the new classifier. Encode special characters in UTF-8.

              • positiveExamples
                Dictionary<string, System.IO.MemoryStream>

                A dictionary that contains the value for each classname. The value is a .zip file of images that depict the visual subject of a class in the new classifier. You can include more than one positive example file in a call.

                Specify the parameter name by appending _positive_examples to the class name. For example, goldenretriever_positive_examples creates the class goldenretriever. The string cannot contain the following characters: $ * - { } \ | / ' " ` [ ].

                Include at least 10 images in .jpg or .png format. The minimum recommended image resolution is 32X32 pixels. The maximum number of images is 10,000 images or 100 MB per .zip file.

                Encode special characters in the file name in UTF-8.

                Constraints: Value must match regular expression /^(?![-\"$'*\/[\\\\]`{|}])[\\p{L}\\p{N}#%&()+,:;<=>?@^~]*$/

              • negativeExamples
                System.IO.MemoryStream

                A .zip file of images that do not depict the visual subject of any of the classes of the new classifier. Must contain a minimum of 10 images.

                Encode special characters in the file name in UTF-8.

              • negativeExamplesFilename
                string

                The filename for negativeExamples.

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • name
                string

                The name of the new classifier. Encode special characters in UTF-8.

              • positiveExamples
                Dictionary<string, System.IO.MemoryStream>

                A dictionary that contains the value for each classname. The value is a .zip file of images that depict the visual subject of a class in the new classifier. You can include more than one positive example file in a call.

                Specify the parameter name by appending _positive_examples to the class name. For example, goldenretriever_positive_examples creates the class goldenretriever. The string cannot contain the following characters: $ * - { } \ | / ' " ` [ ].

                Include at least 10 images in .jpg or .png format. The minimum recommended image resolution is 32X32 pixels. The maximum number of images is 10,000 images or 100 MB per .zip file.

                Encode special characters in the file name in UTF-8.

                Constraints: Value must match regular expression /^(?![-\"$'*\/[\\\\]`{|}])[\\p{L}\\p{N}#%&()+,:;<=>?@^~]*$/

              • negativeExamples
                System.IO.MemoryStream

                A .zip file of images that do not depict the visual subject of any of the classes of the new classifier. Must contain a minimum of 10 images.

                Encode special characters in the file name in UTF-8.

              • negativeExamplesFilename
                string

                The filename for negativeExamples.

              • curl -X POST -u "apikey:{apikey}" -F "beagle_positive_examples=@beagle.zip" -F "goldenretriever_positive_examples=@golden-retriever.zip" -F "husky_positive_examples=@husky.zip" -F "negative_examples=@cats.zip" -F "name=dogs" "{url}/v3/classifiers?version=2018-03-19"

                Download positive examples files beagle.zip, golden-retriever.zip, husky.zip

                Download negative examples file cats.zip

              • IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

VisualRecognitionService visualRecognition = new VisualRecognitionService("2018-03-19", authenticator);
visualRecognition.SetServiceUrl("{url}");

DetailedResponse<Classifier> result = null;
using (FileStream beagle = File.OpenRead("./beagle.zip"), goldenRetriever = File.OpenRead("./golden-retriever.zip"), husky = File.OpenRead("./husky.zip"), cats = File.OpenRead("./cats.zip"))
{
    using (MemoryStream beagleMemoryStream = new MemoryStream(), goldenRetrieverMemoryStream = new MemoryStream(), huskyMemoryStream = new MemoryStream(), catsMemoryStream = new MemoryStream())
    {
        beagle.CopyTo(beagleMemoryStream);
        goldenRetriever.CopyTo(goldenRetrieverMemoryStream);
        husky.CopyTo(huskyMemoryStream);
        cats.CopyTo(catsMemoryStream);

        Dictionary<string, MemoryStream> positiveExamples = new Dictionary<string, MemoryStream>();
        positiveExamples.Add("beagle_positive_examples", beagleMemoryStream);
        positiveExamples.Add("goldenretriever_positive_examples", goldenRetrieverMemoryStream);
        positiveExamples.Add("husky_positive_examples", huskyMemoryStream);
        result = service.CreateClassifier(
            name: "dogs",
            positiveExamples: positiveExamples,
            negativeExamples: catsMemoryStream
            );
    }
}

Console.WriteLine(result.Response);
              • package main

import (
  "encoding/json"
  "fmt"
   "io"
  "os"

  "github.com/IBM/go-sdk-core/core"
  "github.com/watson-developer-cloud/go-sdk/visualrecognitionv3"
)

func main() {
  authenticator := &core.IamAuthenticator{
    ApiKey: "{apikey}",
  }

  options := &visualrecognitionv3.VisualRecognitionV3Options{
    Version: "2018-03-19",
    Authenticator: authenticator,
  }

  visualRecognition, visualRecognitionErr := visualrecognitionv3.NewVisualRecognitionV3(options)

  if visualRecognitionErr != nil {
    panic(visualRecognitionErr)
  }

  visualRecognition.SetServiceURL("{url}")

  beagle, beagleErr := os.Open("./beagle.zip")
  if beagleErr != nil {
    panic(beagleErr)
  }
  defer beagle.Close()
  goldenRetriever, goldenRetrieverErr := os.Open("./golden-retriever.zip")
  if goldenRetrieverErr != nil {
    panic(goldenRetrieverErr)
  }
  defer goldenRetriever.Close()
  husky, huskyErr := os.Open("./husky.zip")
  if huskyErr != nil {
    panic(huskyErr)
  }
  defer husky.Close()

  cats, catsErr := os.Open( "./cats.zip")
  if catsErr != nil {
    panic(catsErr)
  }
  defer cats.Close()

  positiveExamples := make(map[string]io.ReadCloser)
  positiveExamples["beagle"] = beagle
  positiveExamples["goldenretriever"] = goldenRetriever
  positiveExamples["husky"] = husky

  result, response, responseErr := visualRecognition.CreateClassifier(
    &visualrecognitionv3.CreateClassifierOptions{
      Name: core.StringPtr("dogs"),
      PositiveExamples: positiveExamples,
      NegativeExamples: cats,
    },
  )
  if responseErr != nil {
    panic(responseErr)
  }
  b, _ := json.MarshalIndent(result, "", "  ")
  fmt.Println(string(b))
}
              • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
VisualRecognition visualRecognition = new VisualRecognition("2018-03-19", authenticator);
visualRecognition.setServiceUrl("{url}");

CreateClassifierOptions createClassifierOptions = new CreateClassifierOptions.Builder()
  .name("dogs")
  .addPositiveExamples("beagle", new FileInputStream("./beagle.zip"))
  .addPositiveExamples("goldenretriever",new FileInputStream("./golden-retriever.zip"))
  .addPositiveExamples("husky", new FileInputStream("./husky.zip"))
  .negativeExamples(new FileInputStream("./cats.zip"))
  .negativeExamplesFilename("cats")
  .build();

Classifier dogs = visualRecognition.createClassifier(createClassifierOptions).execute().getResult();
System.out.println(dogs);
              • const fs = require('fs');
const VisualRecognitionV3 = require('ibm-watson/visual-recognition/v3');
const { IamAuthenticator } = require('ibm-watson/auth');

const visualRecognition = new VisualRecognitionV3({
  version: '2018-03-19',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
});

const createClassifierParams = {
  name: 'dogs',
  negativeExamples: fs.createReadStream('./cats.zip'),
  positiveExamples: {
    beagle: fs.createReadStream('./beagle.zip'),
    husky: fs.createReadStream('./husky.zip'),
    goldenretriever: fs.createReadStream('./golden-retriever.zip'),
  }
};

visualRecognition.createClassifier(createClassifierParams)
  .then(response => {
    const classifier = response.result;
    console.log(JSON.stringify(classifier, null, 2));
  })
  .catch(err => {
    console.log('error:', err);
  });
              • import json
from ibm_watson import VisualRecognitionV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

authenticator = IAMAuthenticator('{apikey}')
visual_recognition = VisualRecognitionV3(
    version='2018-03-19',
    authenticator=authenticator
)

visual_recognition.set_service_url('{url}')

with open('./beagle.zip', 'rb') as beagle, open(
        './golden-retriever.zip', 'rb') as goldenretriever, open(
            './husky.zip', 'rb') as husky, open(
                './cats.zip', 'rb') as cats:
    model = visual_recognition.create_classifier(
        'dogs',
        positive_examples={'beagle': beagle, 'goldenretriever': goldenretriever, 'husky': husky},
        negative_examples=cats).get_result()
print(json.dumps(model, indent=2))
              • require "json"
require "ibm_watson/authenticators"
require "ibm_watson/visual_recognition_v3"
include IBMWatson

authenticator = Authenticators::IamAuthenticator.new(
  apikey: "{apikey}"
)
visual_recognition = VisualRecognitionV3.new(
  version: "2018-03-19",
  authenticator: authenticator
)
visual_recognition.service_url = "{url}"

beagle = File.open("./beagle.zip")
goldenretriever = File.open("./golden-retriever.zip")
husky = File.open("./husky.zip")
cats = File.open("./cats.zip")
model = visual_recognition.create_classifier(
  name: "dogs",
  positive_examples: { beagle: beagle, goldenretriever: goldenretriever, husky: husky },
  negative_examples: { cats: cats }
)
beagle.close
goldenretriever.close
husky.close
cats.close
puts JSON.pretty_generate(model.result)
              • let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let visualRecognition = VisualRecognition(version: "2018-03-19", authenticator: authenticator)
visualRecognition.serviceURL = "{url}"

let beagleURL = Bundle.main.url(forResource: "beagle", withExtension: "zip")
let beagle = try! Data(contentsOf: beagleURL!)
let goldenRetrieverURL = Bundle.main.url(forResource: "golden-retriever", withExtension: "zip")
let goldenRetriever = try! Data(contentsOf: goldenRetrieverURL!)
let huskyURL = Bundle.main.url(forResource: "husky", withExtension: "zip")
let husky = try! Data(contentsOf: huskyURL!)
let catsURL = Bundle.main.url(forResource: "cats", withExtension: "zip")
let cats = try! Data(contentsOf: catsURL!)

visualRecognition.createClassifier(
  name: "dogs",
  positiveExamples: [
    "beagle": beagle,
    "goldenretriever": goldenRetriever,
    "husky": husky
  ],
  negativeExamples: cats)
{
  response, error in

  guard let classifier = response?.result else {
    print(error?.localizedDescription ?? "unknown error")
    return
  }

  print(classifier)
}
              • var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var visualRecognition = new VisualRecognitionService("2018-03-19", authenticator);
visualRecognition.SetServiceUrl("{url}");

Classifier createClassifierResponse = null;

using (FileStream beagle = File.OpenRead("./beagle.zip"), goldenRetriever = File.OpenRead("./golden-retriever.zip"), husky = File.OpenRead("./husky.zip"), cats = File.OpenRead("./cats.zip"))
{
    using (MemoryStream beagleMemoryStream = new MemoryStream(), goldenRetrieverMemoryStream = new MemoryStream(), huskyMemoryStream = new MemoryStream(), catsMemoryStream = new MemoryStream())
    {
        beagle.CopyTo(beagleMemoryStream);
        goldenRetriever.CopyTo(goldenRetrieverMemoryStream);
        husky.CopyTo(huskyMemoryStream);
        cats.CopyTo(catsMemoryStream);

        Dictionary<string, MemoryStream> positiveExamples = new Dictionary<string, MemoryStream>();
        positiveExamples.Add("beagle_positive_examples", beagleMemoryStream);
        positiveExamples.Add("goldenretriever_positive_examples", goldenRetrieverMemoryStream);
        positiveExamples.Add("husky_positive_examples", huskyMemoryStream);
        VisualRecognitionService.CreateClassifier(
            callback: (DetailedResponse<Classifier> response, IBMError error) =>
            {
                Log.Debug("VisualRecognitionServiceV3", "CreateClassifier result: {0}", response.Response);
                createClassifierResponse = response.Result;
                classifierId = createClassifierResponse.ClassifierId;
            },
            name: "dogs",
            positiveExamples: positiveExamples,
            negativeExamples: catsMemoryStream
        );
    }
}

while (createClassifierResponse == null)
    yield return null;

              Response

              Response Body

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Status Code

              • 200

                success

              • 400

                Invalid request due to user input, for example:

                • Bad query parameter or header
                • No input images
                • The size of the image file in the request is larger than the maximum supported size
                • Corrupt .zip file
                • Cannot find the classifier
              • 401

                No API key or the key is not valid.

              • 413

                The .zip file is too large.

              Example responses
              • {
  "classifier_id": "dogs_1477088859",
  "name": "dogs",
  "status": "training",
  "owner": "b2a3c43c-f1ef-4186-a3d3-71073e4142c5",
  "created": "2018-03-17T19:01:30.536Z",
  "updated": "2018-03-17T19:01:30.536Z",
  "classes": [
    {
      "class": "husky"
    },
    {
      "class": "goldenretriever"
    },
    {
      "class": "beagle"
    }
  ],
  "core_ml_enabled": true
}
              • {
  "classifier_id": "dogs_1477088859",
  "name": "dogs",
  "status": "training",
  "owner": "b2a3c43c-f1ef-4186-a3d3-71073e4142c5",
  "created": "2018-03-17T19:01:30.536Z",
  "updated": "2018-03-17T19:01:30.536Z",
  "classes": [
    {
      "class": "husky"
    },
    {
      "class": "goldenretriever"
    },
    {
      "class": "beagle"
    }
  ],
  "core_ml_enabled": true
}
              • {
  "code": "400",
  "error": "Error: Too many images in collection"
}
              • {
  "code": "400",
  "error": "Error: Too many images in collection"
}
              • {
  "code": "401",
  "error": "Unauthorized"
}
              • {
  "code": "401",
  "error": "Unauthorized"
}

              Retrieve a list of classifiers

              GET /v3/classifiers
              (visualRecognition *VisualRecognitionV3) ListClassifiers(listClassifiersOptions *ListClassifiersOptions) (result *Classifiers, response *core.DetailedResponse, err error)
              (visualRecognition *VisualRecognitionV3) ListClassifiersWithContext(ctx context.Context, listClassifiersOptions *ListClassifiersOptions) (result *Classifiers, response *core.DetailedResponse, err error)
              ServiceCall<Classifiers> listClassifiers(ListClassifiersOptions listClassifiersOptions)
              listClassifiers(params)
              list_classifiers(self,
        *,
        verbose: bool = None,
        **kwargs
    ) -> DetailedResponse
              list_classifiers(verbose: nil)
              func listClassifiers(
    verbose: Bool? = nil,
    headers: [String: String]? = nil,
    completionHandler: @escaping (WatsonResponse<Classifiers>?, WatsonError?) -> Void)
              ListClassifiers(bool? verbose = null)
              ListClassifiers(Callback<Classifiers> callback, bool? verbose = null)

              Request

              Instantiate the ListClassifiersOptions struct and set the fields to provide parameter values for the ListClassifiers method.

              Use the ListClassifiersOptions.Builder to create a ListClassifiersOptions object that contains the parameter values for the listClassifiers method.

              Query Parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • verbose
                boolean

                Specify true to return details about the classifiers. Omit this parameter to return a brief list of classifiers.

              parameter

              WithContext method only

              ListClassifiersOptions

              The ListClassifiers options.

              ListClassifiersOptions

              The listClassifiers options.

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • verbose
                boolean

                Specify true to return details about the classifiers. Omit this parameter to return a brief list of classifiers.

              parameters

              • self.version
                str

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • verbose
                bool

                Specify true to return details about the classifiers. Omit this parameter to return a brief list of classifiers.

              parameters

              • version
                String

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • verbose
                Boolean

                Specify true to return details about the classifiers. Omit this parameter to return a brief list of classifiers.

              parameters

              • version
                String

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • verbose
                Bool

                Specify true to return details about the classifiers. Omit this parameter to return a brief list of classifiers.

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • verbose
                bool?

                Specify true to return details about the classifiers. Omit this parameter to return a brief list of classifiers.

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • verbose
                bool?

                Specify true to return details about the classifiers. Omit this parameter to return a brief list of classifiers.

              • curl -u "apikey:{apikey}" "{url}/v3/classifiers?verbose=true&version=2018-03-19"
              • IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

VisualRecognitionService visualRecognition = new VisualRecognitionService("2018-03-19", authenticator);
visualRecognition.SetServiceUrl("{url}");

var result = service.ListClassifiers(
    verbose: true
    );

Console.WriteLine(result.Response);
              • package main

import (
  "encoding/json"
  "fmt"
  "github.com/IBM/go-sdk-core/core"
  "github.com/watson-developer-cloud/go-sdk/visualrecognitionv3"
)

func main() {
  authenticator := &core.IamAuthenticator{
    ApiKey: "{apikey}",
  }

  options := &visualrecognitionv3.VisualRecognitionV3Options{
    Version: "2018-03-19",
    Authenticator: authenticator,
  }

  visualRecognition, visualRecognitionErr := visualrecognitionv3.NewVisualRecognitionV3(options)

  if visualRecognitionErr != nil {
    panic(visualRecognitionErr)
  }

  visualRecognition.SetServiceURL("{url}")

  result, response, responseErr := visualRecognition.ListClassifiers(
    &visualrecognitionv3.ListClassifiersOptions{
      Verbose: core.BoolPtr(true),
    },
  )
  if responseErr != nil {
    panic(responseErr)
  }
  b, _ := json.MarshalIndent(result, "", "  ")
  fmt.Println(string(b))
}
              • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
VisualRecognition visualRecognition = new VisualRecognition("2018-03-19", authenticator);
visualRecognition.setServiceUrl("{url}");

ListClassifiersOptions listClassifiersOptions = new ListClassifiersOptions.Builder()
  .verbose(true)
  .build();
Classifiers classifiers = visualRecognition.listClassifiers(listClassifiersOptions).execute().getResult();
System.out.println(classifiers);
              • const VisualRecognitionV3 = require('ibm-watson/visual-recognition/v3');
const { IamAuthenticator } = require('ibm-watson/auth');

const visualRecognition = new VisualRecognitionV3({
  version: '2018-03-19',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
});

const listClassifiersParams = {
  verbose: true,
};

visualRecognition.listClassifiers(listClassifiersParams)
  .then(response => {
    const classifiers = response.result;
    console.log(JSON.stringify(classifiers, null, 2));
  })
  .catch(err => {
    console.log('error:', err);
  });
              • import json
from ibm_watson import VisualRecognitionV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

authenticator = IAMAuthenticator('{apikey}')
visual_recognition = VisualRecognitionV3(
    version='2018-03-19',
    authenticator=authenticator
)

visual_recognition.set_service_url('{url}')

classifiers = visual_recognition.list_classifiers(verbose=True).get_result()
print(json.dumps(classifiers, indent=2))
              • require "json"
require "ibm_watson/authenticators"
require "ibm_watson/visual_recognition_v3"
include IBMWatson

authenticator = Authenticators::IamAuthenticator.new(
  apikey: "{apikey}"
)
visual_recognition = VisualRecognitionV3.new(
  version: "2018-03-19",
  authenticator: authenticator
)
visual_recognition.service_url = "{url}"

classifiers = visual_recognition.list_classifiers(
  verbose: true
)
puts JSON.pretty_generate(classifiers.result)
              • let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let visualRecognition = VisualRecognition(version: "2018-03-19", authenticator: authenticator)
visualRecognition.serviceURL = "{url}"

visualRecognition.listClassifiers(verbose: true) {
  response, error in

  guard let classifiers = response?.result else {
    print(error?.localizedDescription ?? "unknown error")
    return
  }

  print(classifiers)
}
              • var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var visualRecognition = new VisualRecognitionService("2018-03-19", authenticator);
visualRecognition.SetServiceUrl("{url}");

Classifiers listClassifiersResponse = null;
VisualRecognitionService.ListClassifiers(
    callback: (DetailedResponse<Classifiers> response, IBMError error) =>
    {
        Log.Debug("VisualRecognitionServiceV3", "ListClassifiers result: {0}", response.Response);
        listClassifiersResponse = response.Result;
    },
    verbose: true
);

while (listClassifiersResponse == null)
    yield return null;

              Response

              Response Body

              Classifiers

              A container for the list of classifiers.

              Classifiers

              A container for the list of classifiers.

              Classifiers

              A container for the list of classifiers.

              Classifiers

              A container for the list of classifiers.

              Classifiers

              A container for the list of classifiers.

              Classifiers

              A container for the list of classifiers.

              Classifiers

              A container for the list of classifiers.

              Classifiers

              A container for the list of classifiers.

              Classifiers

              A container for the list of classifiers.

              Status Code

              • 200

                success

              • 400

                Invalid request due to user input, such as a bad parameter.

              • 401

                No API key or the key is not valid.

              Example responses
              • {
  "classifiers": [
    {
      "classifier_id": "dogs_1477088859",
      "name": "dogs",
      "status": "ready",
      "owner": "99d0114d-9959-4071-b06f-654701909be4",
      "created": "2018-03-17T19:01:30.536Z",
      "updated": "2018-03-17T19:42:19.906Z",
      "classes": [
        {
          "class": "husky"
        },
        {
          "class": "goldenretriever"
        },
        {
          "class": "beagle"
        }
      ],
      "core_ml_enabled": true
    },
    {
      "classifier_id": "CarsvsTrucks_1479118188",
      "name": "Cars vs Trucks",
      "status": "ready",
      "owner": "99d0114d-9959-4071-b06f-654701909be4",
      "created": "2016-07-19T15:24:08.743Z",
      "updated": "2016-07-19T15:24:08.743Z",
      "classes": [
        {
          "class": "cars"
        }
      ],
      "core_ml_enabled": false
    }
  ]
}
              • {
  "classifiers": [
    {
      "classifier_id": "dogs_1477088859",
      "name": "dogs",
      "status": "ready",
      "owner": "99d0114d-9959-4071-b06f-654701909be4",
      "created": "2018-03-17T19:01:30.536Z",
      "updated": "2018-03-17T19:42:19.906Z",
      "classes": [
        {
          "class": "husky"
        },
        {
          "class": "goldenretriever"
        },
        {
          "class": "beagle"
        }
      ],
      "core_ml_enabled": true
    },
    {
      "classifier_id": "CarsvsTrucks_1479118188",
      "name": "Cars vs Trucks",
      "status": "ready",
      "owner": "99d0114d-9959-4071-b06f-654701909be4",
      "created": "2016-07-19T15:24:08.743Z",
      "updated": "2016-07-19T15:24:08.743Z",
      "classes": [
        {
          "class": "cars"
        }
      ],
      "core_ml_enabled": false
    }
  ]
}
              • {
  "code": "400",
  "error": "Error: Too many images in collection"
}
              • {
  "code": "400",
  "error": "Error: Too many images in collection"
}
              • {
  "code": "401",
  "error": "Unauthorized"
}
              • {
  "code": "401",
  "error": "Unauthorized"
}

              Retrieve classifier details

              Retrieve information about a custom classifier.

              Retrieve information about a custom classifier.

              Retrieve information about a custom classifier.

              Retrieve information about a custom classifier.

              Retrieve information about a custom classifier.

              Retrieve information about a custom classifier.

              Retrieve information about a custom classifier.

              Retrieve information about a custom classifier.

              Retrieve information about a custom classifier.

              GET /v3/classifiers/{classifier_id}
              (visualRecognition *VisualRecognitionV3) GetClassifier(getClassifierOptions *GetClassifierOptions) (result *Classifier, response *core.DetailedResponse, err error)
              (visualRecognition *VisualRecognitionV3) GetClassifierWithContext(ctx context.Context, getClassifierOptions *GetClassifierOptions) (result *Classifier, response *core.DetailedResponse, err error)
              ServiceCall<Classifier> getClassifier(GetClassifierOptions getClassifierOptions)
              getClassifier(params)
              get_classifier(self,
        classifier_id: str,
        **kwargs
    ) -> DetailedResponse
              get_classifier(classifier_id:)
              func getClassifier(
    classifierID: String,
    headers: [String: String]? = nil,
    completionHandler: @escaping (WatsonResponse<Classifier>?, WatsonError?) -> Void)
              GetClassifier(string classifierId)
              GetClassifier(Callback<Classifier> callback, string classifierId)

              Request

              Instantiate the GetClassifierOptions struct and set the fields to provide parameter values for the GetClassifier method.

              Use the GetClassifierOptions.Builder to create a GetClassifierOptions object that contains the parameter values for the getClassifier method.

              Path Parameters

              • classifier_id
                string

                The ID of the classifier.

              Query Parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              parameter

              WithContext method only

              GetClassifierOptions

              The GetClassifier options.

              GetClassifierOptions

              The getClassifier options.

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • classifierId
                string

                The ID of the classifier.

              parameters

              • self.version
                str

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • classifier_id
                str

                The ID of the classifier.

              parameters

              • version
                String

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • classifier_id
                String

                The ID of the classifier.

              parameters

              • version
                String

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • classifierID
                String

                The ID of the classifier.

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • classifierId
                string

                The ID of the classifier.

              parameters

              • version
                string

                Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is 2018-03-19.

              • classifierId
                string

                The ID of the classifier.

              • curl -u "apikey:{apikey}" "{url}/v3/classifiers/dogs_1477088859?version=2018-03-19"
              • IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

VisualRecognitionService visualRecognition = new VisualRecognitionService("2018-03-19", authenticator);
visualRecognition.SetServiceUrl("{url}");

var result = service.GetClassifier(
    classifierId: "dogs_1477088859"
    );

Console.WriteLine(result.Response);
              • package main

import (
  "encoding/json"
  "fmt"
  "github.com/IBM/go-sdk-core/core"
  "github.com/watson-developer-cloud/go-sdk/visualrecognitionv3"
)

func main() {
  authenticator := &core.IamAuthenticator{
    ApiKey: "{apikey}",
  }

  options := &visualrecognitionv3.VisualRecognitionV3Options{
    Version: "2018-03-19",
    Authenticator: authenticator,
  }

  visualRecognition, visualRecognitionErr := visualrecognitionv3.NewVisualRecognitionV3(options)

  if visualRecognitionErr != nil {
    panic(visualRecognitionErr)
  }

  visualRecognition.SetServiceURL("{url}")

  result, response, responseErr := visualRecognition.GetClassifier(
    &visualrecognitionv3.GetClassifierOptions{
      ClassifierID: core.StringPtr("dogs_1477088859"),
    },
  )
  if responseErr != nil {
    panic(responseErr)
  }
  b, _ := json.MarshalIndent(result, "", "  ")
  fmt.Println(string(b))
}
              • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
VisualRecognition visualRecognition = new VisualRecognition("2018-03-19", authenticator);
visualRecognition.setServiceUrl("{url}");

GetClassifierOptions getClassifierOptions = new GetClassifierOptions.Builder("dogs_1477088859").build();
Classifier classifier = visualRecognition.getClassifier(getClassifierOptions).execute().getResult();
System.out.println(classifier);
              • const VisualRecognitionV3 = require('ibm-watson/visual-recognition/v3');
const { IamAuthenticator } = require('ibm-watson/auth');

const visualRecognition = new VisualRecognitionV3({
  version: '2018-03-19',
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
});

const getClassifierParams = {
  classifierId: 'dogs_1477088859',
};

visualRecognition.getClassifier(getClassifierParams)
  .then(response => {
    const classifier = response.result;
    console.log(JSON.stringify(classifier, null, 2));
  })
  .catch(err => {
    console.log('error:', err);
  });
              • import json
from ibm_watson import VisualRecognitionV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

authenticator = IAMAuthenticator('{apikey}')
visual_recognition = VisualRecognitionV3(
    version='2018-03-19',
    authenticator=authenticator
)

visual_recognition.set_service_url('{url}')

classifier = visual_recognition.get_classifier(
    classifier_id='dogs_1477088859').get_result()
print(json.dumps(classifier, indent=2))
              • require "json"
require "ibm_watson/authenticators"
require "ibm_watson/visual_recognition_v3"
include IBMWatson

authenticator = Authenticators::IamAuthenticator.new(
  apikey: "{apikey}"
)
visual_recognition = VisualRecognitionV3.new(
  version: "2018-03-19",
  authenticator: authenticator
)
visual_recognition.service_url = "{url}"

classifier = visual_recognition.get_classifier(
  classifier_id: "dogs_1477088859"
)
puts JSON.pretty_generate(classifier.result)
              • let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let visualRecognition = VisualRecognition(version: "2018-03-19", authenticator: authenticator)
visualRecognition.serviceURL = "{url}"

visualRecognition.getClassifier(classifierID: "{classifier_id}") {
  response, error in

  guard let classifier = response?.result else {
    print(error?.localizedDescription ?? "unknown error")
    return
  }

  print(classifier)
}
              • var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var visualRecognition = new VisualRecognitionService("2018-03-19", authenticator);
visualRecognition.SetServiceUrl("{url}");

Classifier getClassifierResponse = null;

VisualRecognitionService.GetClassifier(
    callback: (DetailedResponse<Classifier> response, IBMError error) =>
    {
        getClassifierResponse = response.Result;
        Log.Debug("VisualRecognitionServiceV3", "CheckClassifierStatus: {0}", getClassifierResponse.Status);
    },
    classifierId: "dogs_1477088859"
);

while (getClassifierResponse == null)
    yield return null;

              Response

              Response Body

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.

              Classifier

              Information about a classifier.