Prediction Parameters

You can set additional parameters to gain flexibility in the predict operation.

Select Concepts

By putting this additional parameter on your predict calls, you can receive predict value(s) for only the concepts that you want to. You can specify particular concepts by either their id and/or their name. The concept names and ids are case sensitive, and so, these must be exact matches.

To retrieve an entire list of concepts from a given model use the GET /v2/models/{model_id}/output_info endpoint. Check out the Advanced Models section for how to use with any of the API clients!

If you submit a request with not an exact match of the concept id or name, you will receive an invalid model argument error. However, if one or more matches while one or more do not, the API will respond with a Mixed Success.

gRPC Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.StatusCode;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
    PostModelOutputsRequest.newBuilder()
        .setModelId("aaa03c23b3724a16a56b629203edc62c")  // This is model ID of the clarifai/main General model.
        .addInputs(
            Input.newBuilder().setData(
                Data.newBuilder().setImage(
                    Image.newBuilder().setUrl("https://samples.clarifai.com/metro-north.jpg")
                )
            )
        )
        .setModel(
            Model.newBuilder().setOutputInfo(
                OutputInfo.newBuilder().setOutputConfig(
                    OutputConfig.newBuilder()
                        // When selecting concepts, value is ignored, so no need to specify it.
                        .addSelectConcepts(Concept.newBuilder().setName("train"))
                        .addSelectConcepts(Concept.newBuilder().setId("ai_6kTjGfF6")
                        )
                )
            )
        )
        .build()
);

if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
  throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
}

// Since we have one input, one output will exist here.
Output output = postModelOutputsResponse.getOutputs(0);

System.out.println("Predicted concepts:");
for (Concept concept : output.getData().getConceptsList()) {
    System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());
}

gRPC NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.PostModelOutputs(
    {
        model_id: "aaa03c23b3724a16a56b629203edc62c",  // This is model ID of the clarifai/main General model.
        inputs: [
            {data: {image: {url: "https://samples.clarifai.com/metro-north.jpg"}}}
        ],
        // When selecting concepts, value is ignored, so no need to specify it.
        model: {output_info: {output_config: {select_concepts: [{name: "train"}, {id: "ai_6kTjGfF6"}]}}}
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post model outputs failed, status: " + response.status.description);
        }

        // Since we have one input, one output will exist here.
        const output = response.outputs[0];

        console.log("Predicted concepts:");
        for (const concept of output.data.concepts) {
            console.log(concept.name + " " + concept.value);
        }
    }
);

gRPC Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

post_model_outputs_response = stub.PostModelOutputs(
    service_pb2.PostModelOutputsRequest(
        model_id="aaa03c23b3724a16a56b629203edc62c",  # This is model ID of the clarifai/main General model.
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/metro-north.jpg"
                    )
                )
            )
        ],
        model=resources_pb2.Model(
            output_info=resources_pb2.OutputInfo(
                output_config=resources_pb2.OutputConfig(
                    select_concepts=[
                        # When selecting concepts, value is ignored, so no need to specify it.
                        resources_pb2.Concept(name="train"),
                        resources_pb2.Concept(id="ai_6kTjGfF6")
                    ]
                )
            )
        )
    ),
    metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)

# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]

print("Predicted concepts:")
for concept in output.data.concepts:
    print("%s %.2f" % (concept.name, concept.value))

js

app.models.predict(Clarifai.GENERAL_MODEL, 'https://samples.clarifai.com/metro-north.jpg', {
  selectConcepts: [
    {name: 'train'},
    {id: 'ai_6kTjGfF6'}
  ]
})

python

from clarifai.rest import ClarifaiApp, Concept
app = ClarifaiApp(api_key='YOUR_API_KEY')

model = app.models.get('general-v1.3')

select_concept_list = [Concept(concept_name='train'), Concept(concept_id='ai_6kTjGfF6')]
model.predict_by_url(url='https://samples.clarifai.com/metro-north.jpg', select_concepts=select_concept_list)

java

client.predict(client.getDefaultModels().generalModel().id())
    .withInputs(ClarifaiInput.forImage("https://samples.clarifai.com/metro-north.jpg"))
    .selectConcepts(Concept.forID("dog"), Concept.forID("cat"))
    .executeSync();

csharp

using System.Collections.Generic;
using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Inputs;
using Clarifai.DTOs.Predictions;

namespace YourNamespace
{
    public class YourClassName
    {
        public static async Task Main()
        {
            var client = new ClarifaiClient("YOUR_API_KEY");

            await client.Predict<Concept>(
                    client.PublicModels.GeneralModel.ModelID,
                    new ClarifaiURLImage("https://samples.clarifai.com/metro-north.jpg"),
                    selectConcepts: new List<Concept>
                    {
                        new Concept(id: "", name: "dog"),
                        new Concept(id: "", name: "cat")
                    })
                .ExecuteAsync();
        }
    }
}

objective-c

// Coming Soon

php

use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLImage;
use Clarifai\DTOs\Models\ModelType;
use Clarifai\DTOs\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;

$client = new ClarifaiClient('YOUR_API_KEY');

$response = $client->predict(ModelType::concept(),
        $client->publicModels()->generalModel()->modelID(),
        new ClarifaiURLImage('https://samples.clarifai.com/metro-north.jpg'))
    ->withSelectConcepts([(new Concept(''))->withName('dog'), (new Concept(''))->withName('cat')])
    ->executeSync();

if ($response->isSuccessful()) {
    echo "Response is successful.\n";

    $output = $response->get();

    echo "Predicted concepts:\n";

    foreach ($output->data() as $concept) {
        echo $concept->name() . ': ' . $concept->value() . "\n";
    }
} else {
    echo "Response is not successful. Reason: \n";
    echo $response->status()->description() . "\n";
    echo $response->status()->errorDetails() . "\n";
    echo "Status code: " . $response->status()->statusCode();
}

cURL

curl -X POST \
  -H 'authorization: Key YOUR_API_KEY' \
  -H 'content -type: application/json' \
  -d '{
  "inputs": [
    {
      "data": {
        "image": {
          "url": "https://samples.clarifai.com/metro-north.jpg"
        }
      }
    }
  ],
  "model": {
    "output_info": {
      "output_config": {
        "select_concepts": [
          {"name": "train"},
          {"id": "ai_6kTjGfF6"}
        ]
      }
    }
  }
}'\
https://api.clarifai.com/v2/models/aaa03c23b3724a16a56b629203edc62c/outputs

# Above is model ID of the publicly available General model.

Response JSON

{
  "status": {
    "code": 10000,
    "description": "Ok"
  },
  "outputs": [
    {
      "id": "c8abf5cbe52746efa9df8a2319d49d0a",
      "status": {
        "code": 10000,
        "description": "Ok"
      },
      "created_at": "2017-06-27T13:31:57.493797045Z",
      "model": {
        "id": "aaa03c23b3724a16a56b629203edc62c",
        "name": "general-v1.3",
        "created_at": "2016-03-09T17:11:39.608845Z",
        "app_id": "main",
        "output_info": {
          "message": "Show output_info with: GET /models/{model_id}/output_info",
          "type": "concept",
          "type_ext": "concept"
        },
        "model_version": {
          "id": "aa9ca48295b37401f8af92ad1af0d91d",
          "created_at": "2016-07-13T01:19:12.147644Z",
          "status": {
            "code": 21100,
            "description": "Model trained successfully"
          }
        }
      },
      "input": {
        "id": "c613b3254da34382b2fca65365da7c49",
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/metro-north.jpg"
          }
        }
      },
      "data": {
        "concepts": [
          {
            "id": "ai_HLmqFqBf",
            "name": "train",
            "value": 0.9989112,
            "app_id": "main"
          },
          {
            "id": "ai_6kTjGfF6",
            "name": "station",
            "value": 0.992573,
            "app_id": "main"
          }
        ]
      }
    }
  ]
}

Maximum Concepts

Setting the max concepts parameter will customize how many concepts and their corresponding probability scores the predict endpoint will return. If not specified, the predict endpoint will return the top 20 concepts. You can currently set the max concepts parameter to any number in the range: [1-200]. If your use case requires more concepts, please contact Support.

gRPC Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
    PostModelOutputsRequest.newBuilder()
        .setModelId("aaa03c23b3724a16a56b629203edc62c")  // This is model ID of the clarifai/main General model.
        .addInputs(
            Input.newBuilder().setData(
                Data.newBuilder().setImage(
                    Image.newBuilder().setUrl("https://samples.clarifai.com/metro-north.jpg")
                )
            )
        )
        .setModel(
            Model.newBuilder().setOutputInfo(
                OutputInfo.newBuilder().setOutputConfig(
                    OutputConfig.newBuilder().setMaxConcepts(3)
                )
            )
        )
        .build()
);

if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
  throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
}

// Since we have one input, one output will exist here.
Output output = postModelOutputsResponse.getOutputs(0);

System.out.println("Predicted concepts:");
for (Concept concept : output.getData().getConceptsList()) {
    System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());

gRPC NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.PostModelOutputs(
    {
        model_id: "aaa03c23b3724a16a56b629203edc62c",  // This is model ID of the clarifai/main General model
        inputs: [
            {data: {image: {url: "https://samples.clarifai.com/metro-north.jpg"}}}
        ],
        model: {output_info: {output_config: {max_concepts: 3}}}
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post model outputs failed, status: " + response.status.description);
        }

        // Since we have one input, one output will exist here.
        const output = response.outputs[0];

        console.log("Predicted concepts:");
        for (const concept of output.data.concepts) {
            console.log(concept.name + " " + concept.value);
        }
    }
);

gRPC Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

post_model_outputs_response = stub.PostModelOutputs(
    service_pb2.PostModelOutputsRequest(
        model_id="aaa03c23b3724a16a56b629203edc62c",  # This is model ID of the clarifai/main General model.
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/metro-north.jpg"
                    )
                )
            )
        ],
        model=resources_pb2.Model(
            output_info=resources_pb2.OutputInfo(
                output_config=resources_pb2.OutputConfig(
                    max_concepts=3
                )
            )
        )
    ),
    metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)

# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]

print("Predicted concepts:")
for concept in output.data.concepts:
    print("%s %.2f" % (concept.name, concept.value))

js

app.models.predict(Clarifai.GENERAL_MODEL, 'https://samples.clarifai.com/metro-north.jpg', { maxConcepts: 3 })
  .then(response => {
    // There was a successful response
  })
  .catch(error => {
    // There was an error
  });

python

from clarifai.rest import ClarifaiApp
app = ClarifaiApp(api_key='YOUR_API_KEY')

model = app.models.get('general-v1.3')

model.predict_by_url(url='https://samples.clarifai.com/metro-north.jpg', max_concepts=3)

java

client.predict(client.getDefaultModels().generalModel().id())
    .withInputs(ClarifaiInput.forImage("https://samples.clarifai.com/metro-north.jpg"))
    .withMaxConcepts(3)
    .executeSync();

csharp

using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Inputs;
using Clarifai.DTOs.Predictions;

namespace YourNamespace
{
    public class YourClassName
    {
        public static async Task Main()
        {
            var client = new ClarifaiClient("YOUR_API_KEY");

            await client.Predict<Concept>(
                    client.PublicModels.GeneralModel.ModelID,
                    new ClarifaiURLImage("https://samples.clarifai.com/metro-north.jpg"),
                    maxConcepts: 3)
                .ExecuteAsync();
        }
    }
}

objective-c

//Coming soon

php

use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLImage;
use Clarifai\DTOs\Models\ModelType;
use Clarifai\DTOs\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;

$client = new ClarifaiClient('YOUR_API_KEY');

$response = $client->predict(ModelType::concept(),
        $client->publicModels()->generalModel()->modelID(),
        new ClarifaiURLImage('https://samples.clarifai.com/metro-north.jpg'))
    ->withMaxConcepts(3)
    ->executeSync();

if ($response->isSuccessful()) {
    echo "Response is successful.\n";

    /** @var ClarifaiOutput $output */
    $output = $response->get();

    echo "Predicted concepts:\n";
    /** @var Concept $concept */
    foreach ($output->data() as $concept) {
        echo $concept->name() . ': ' . $concept->value() . "\n";
    }
} else {
    echo "Response is not successful. Reason: \n";
    echo $response->status()->description() . "\n";
    echo $response->status()->errorDetails() . "\n";
    echo "Status code: " . $response->status()->statusCode();
}

cURL

curl -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "inputs": [
      {
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/metro-north.jpg"
          }
        }
      }
    ],
    "model":{
      "output_info":{
        "output_config":{
          "max_concepts": 3
        }
      }
    }
  }'\
  https://api.clarifai.com/v2/models/aaa03c23b3724a16a56b629203edc62c/outputs

Response JSON

{
  "status": {
    "code": 10000,
    "description": "Ok"
  },
  "outputs": [
    {
      "id": "c8c400234b0d47df9084857df0d69efb",
      "status": {
        "code": 10000,
        "description": "Ok"
      },
      "created_at": "2017-06-15T16:09:48.984389535Z",
      "model": {
        "id": "aaa03c23b3724a16a56b629203edc62c",
        "name": "general-v1.3",
        "created_at": "2016-02-26T23:38:40.086101Z",
        "app_id": "main",
        "output_info": {
          "message": "Show output_info with: GET /models/{model_id}/output_info",
          "type": "concept",
          "type_ext": "concept"
        },
        "model_version": {
          "id": "aa9ca48295b37401f8af92ad1af0d91d",
          "created_at": "2016-07-13T00:58:55.915745Z",
          "status": {
            "code": 21100,
            "description": "Model trained successfully"
          }
        }
      },
      "input": {
        "id": "fd99d9e345f3495a8bd2802151d09efa",
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/metro-north.jpg"
          }
        }
      },
      "data": {
        "concepts": [
          {
            "id": "ai_HLmqFqBf",
            "name": "train",
            "value": 0.9989112,
            "app_id": "main"
          },
          {
            "id": "ai_fvlBqXZR",
            "name": "railway",
            "value": 0.9975532,
            "app_id": "main"
          },
          {
            "id": "ai_Xxjc3MhT",
            "name": "transportation system",
            "value": 0.9959158,
            "app_id": "main"
          }
        ]
      }
    }
  ]
}

Minimum Prediction Value

This parameter lets you set a minimum probability threshold for the outputs you want to view for the Predict operation. For example if you want to see all concepts with a probability score of .90 or higher, this parameter will allow you to accomplish that. Also note that if you don't specify the number of max concepts, you will only see the top 20. If your result can contain more values you will have to increase the number of maximum concepts as well.

gRPC Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
    PostModelOutputsRequest.newBuilder()
        .setModelId("aaa03c23b3724a16a56b629203edc62c")  // This is model ID of the clarifai/main General model.
        .addInputs(
            Input.newBuilder().setData(
                Data.newBuilder().setImage(
                    Image.newBuilder().setUrl("https://samples.clarifai.com/metro-north.jpg")
                )
            )
        )
        .setModel(
            Model.newBuilder().setOutputInfo(
                OutputInfo.newBuilder().setOutputConfig(
                    OutputConfig.newBuilder().setMinValue(0.95f)
                )
            )
        )
        .build()
);

if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
  throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
}

// Since we have one input, one output will exist here.
Output output = postModelOutputsResponse.getOutputs(0);

System.out.println("Predicted concepts:");
for (Concept concept : output.getData().getConceptsList()) {
    System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());
}

gRPC NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.PostModelOutputs(
    {
        model_id: "aaa03c23b3724a16a56b629203edc62c",  // This is model ID of the clarifai/main General model
        inputs: [
            {data: {image: {url: "https://samples.clarifai.com/metro-north.jpg"}}}
        ],
        model: {output_info: {output_config: {min_value: 0.95}}}
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post model outputs failed, status: " + response.status.description);
        }

        // Since we have one input, one output will exist here.
        const output = response.outputs[0];

        console.log("Predicted concepts:");
        for (const concept of output.data.concepts) {
            console.log(concept.name + " " + concept.value);
        }
    }
);

gRPC Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

post_model_outputs_response = stub.PostModelOutputs(
    service_pb2.PostModelOutputsRequest(
        model_id="aaa03c23b3724a16a56b629203edc62c",  # This is model ID of the clarifai/main General model.
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/metro-north.jpg"
                    )
                )
            )
        ],
        model=resources_pb2.Model(
            output_info=resources_pb2.OutputInfo(
                output_config=resources_pb2.OutputConfig(
                    min_value=0.95
                )
            )
        )
    ),
    metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)

# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]

print("Predicted concepts:")
for concept in output.data.concepts:
    print("%s %.2f" % (concept.name, concept.value))

js

app.models.predict(Clarifai.GENERAL_MODEL, 'https://samples.clarifai.com/metro-north.jpg', { minValue: 0.97 })
  .then(response => {
    // There was a successful response
  })
  .catch(error => {
    // There was an error
  });

python

from clarifai.rest import ClarifaiApp
app = ClarifaiApp(api_key='YOUR_CLARIFAI_KEY')

model = app.models.get('general-v1.3')

model.predict_by_url(url='https://samples.clarifai.com/metro-north.jpg', min_value=0.97)

java

client.predict(client.getDefaultModels().generalModel().id())
    .withInputs(ClarifaiInput.forImage("https://samples.clarifai.com/metro-north.jpg"))
    .withMinValue(0.9)
    .executeSync();

csharp

using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Inputs;
using Clarifai.DTOs.Predictions;

namespace YourNamespace
{
    public class YourClassName
    {
        public static async Task Main()
        {
            var client = new ClarifaiClient("YOUR_API_KEY");

            await client.Predict<Concept>(
                    client.PublicModels.GeneralModel.ModelID,
                    new ClarifaiURLImage("https://samples.clarifai.com/metro-north.jpg"),
                    minValue: 0.9M)
                .ExecuteAsync();
        }
    }
}

objective-c

//Coming soon

php

use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLImage;
use Clarifai\DTOs\Models\ModelType;
use Clarifai\DTOs\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;

$client = new ClarifaiClient('YOUR_API_KEY');

$response = $client->predict(ModelType::concept(),
        $client->publicModels()->generalModel()->modelID(),
        new ClarifaiURLImage('https://samples.clarifai.com/metro-north.jpg'))
    ->withMinValue(0.9)
    ->executeSync();

if ($response->isSuccessful()) {
    echo "Response is successful.\n";

    /** @var ClarifaiOutput $output */
    $output = $response->get();

    echo "Predicted concepts:\n";
    /** @var Concept $concept */
    foreach ($output->data() as $concept) {
        echo $concept->name() . ': ' . $concept->value() . "\n";
    }
} else {
    echo "Response is not successful. Reason: \n";
    echo $response->status()->description() . "\n";
    echo $response->status()->errorDetails() . "\n";
    echo "Status code: " . $response->status()->statusCode();
}

cURL

curl -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "inputs": [
      {
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/metro-north.jpg"
          }
        }
      }
    ],
    "model":{
      "output_info":{
        "output_config":{
          "min_value": 0.95
        }
      }
    }
  }'\
  https://api.clarifai.com/v2/models/aaa03c23b3724a16a56b629203edc62c/outputs

Response JSON

{
  "status": {
    "code": 10000,
    "description": "Ok"
  },
  "outputs": [
    {
      "id": "b2027bccf4964d03b062ce653cff85b6",
      "status": {
        "code": 10000,
        "description": "Ok"
      },
      "created_at": "2017-06-15T20:22:05.841603659Z",
      "model": {
        "id": "aaa03c23b3724a16a56b629203edc62c",
        "name": "general-v1.3",
        "created_at": "2016-02-26T23:38:40.086101Z",
        "app_id": "main",
        "output_info": {
          "message": "Show output_info with: GET /models/{model_id}/output_info",
          "type": "concept",
          "type_ext": "concept"
        },
        "model_version": {
          "id": "aa9ca48295b37401f8af92ad1af0d91d",
          "created_at": "2016-07-13T00:58:55.915745Z",
          "status": {
            "code": 21100,
            "description": "Model trained successfully"
          }
        }
      },
      "input": {
        "id": "f7640568d37f47fbba9d6fdc892ec64d",
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/metro-north.jpg"
          }
        }
      },
      "data": {
        "concepts": [
          {
            "id": "ai_HLmqFqBf",
            "name": "train",
            "value": 0.9989112,
            "app_id": "main"
          },
          {
            "id": "ai_fvlBqXZR",
            "name": "railway",
            "value": 0.9975532,
            "app_id": "main"
          },
          {
            "id": "ai_Xxjc3MhT",
            "name": "transportation system",
            "value": 0.9959158,
            "app_id": "main"
          },
          {
            "id": "ai_6kTjGfF6",
            "name": "station",
            "value": 0.992573,
            "app_id": "main"
          },
          {
            "id": "ai_RRXLczch",
            "name": "locomotive",
            "value": 0.992556,
            "app_id": "main"
          },
          {
            "id": "ai_VRmbGVWh",
            "name": "travel",
            "value": 0.98789215,
            "app_id": "main"
          },
          {
            "id": "ai_SHNDcmJ3",
            "name": "subway system",
            "value": 0.9816359,
            "app_id": "main"
          },
          {
            "id": "ai_jlb9q33b",
            "name": "commuter",
            "value": 0.9712483,
            "app_id": "main"
          }
        ]
      }
    }
  ]
}

By Model Version ID

Every time you train a custom model, it creates a new model version. By specifying version id in your predict call, you can continue to predict on a previous version, for consistent prediction results. Clarifai also updates our pre-built models on a regular basis.

If you are looking for consistent results from your predict calls, use version id. If the model version id is not specified, predict will default to the most current model.

gRPC Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
    PostModelOutputsRequest.newBuilder()
        .setModelId("aaa03c23b3724a16a56b629203edc62c")  // This is model ID of the clarifai/main General model.
        .setVersionId("aa7f35c01e0642fda5cf400f543e7c40")  // This is optional. Defaults to the latest model version.
        .addInputs(
            Input.newBuilder().setData(
                Data.newBuilder().setImage(
                    Image.newBuilder().setUrl("https://samples.clarifai.com/metro-north.jpg")
                )
            )
        )
        .build()
);

if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
  throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
}

// Since we have one input, one output will exist here.
Output output = postModelOutputsResponse.getOutputs(0);

System.out.println("Predicted concepts:");
for (Concept concept : output.getData().getConceptsList()) {
    System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());
}

gRPC NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.PostModelOutputs(
    {
        model_id: "aaa03c23b3724a16a56b629203edc62c",  // This is model ID of the clarifai/main General model
        version_id: "aa7f35c01e0642fda5cf400f543e7c40",  // This is optional. Defaults to the latest model version.
        inputs: [
            {data: {image: {url: "https://samples.clarifai.com/metro-north.jpg"}}}
        ],
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post model outputs failed, status: " + response.status.description);
        }

        // Since we have one input, one output will exist here.
        const output = response.outputs[0];

        console.log("Predicted concepts:");
        for (const concept of output.data.concepts) {
            console.log(concept.name + " " + concept.value);
        }
    }
);

gRPC Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

post_model_outputs_response = stub.PostModelOutputs(
    service_pb2.PostModelOutputsRequest(
        model_id="aaa03c23b3724a16a56b629203edc62c",  # This is model ID of the clarifai/main General model.
        version_id="aa7f35c01e0642fda5cf400f543e7c40",  # This is optional. Defaults to the latest model version.
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/metro-north.jpg"
                    )
                )
            )
        ]
    ),
    metadata=metadata
)

if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)

# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]

print("Predicted concepts:")
for concept in output.data.concepts:
    print("\t%s %.2f" % (concept.name, concept.value))

js

let app = new Clarifai.App({apiKey: 'YOUR_API_KEY'});

app.models.predict({id:'MODEL_ID', version:'MODEL_VERSION_ID'}, "https://samples.clarifai.com/metro-north.jpg").then(
  function(response) {
    // do something with response
  },
  function(err) {
    // there was an error
  }
);

python

from clarifai.rest import ClarifaiApp
app = ClarifaiApp(api_key='YOUR_API_KEY')

model = app.models.get('YOUR_MODEL_ID')
model.model_version = 'YOUR_MODEL_VERSION_ID'

response = model.predict_by_url('https://samples.clarifai.com/metro-north.jpg')

java

ModelVersion modelVersion = client.getModelVersionByID("MODEL_ID", "MODEL_VERSION_ID")
        .executeSync()
        .get();

    client.predict("MODEL_ID")
        .withVersion(modelVersion)
        .withInputs(ClarifaiInput.forImage("https://samples.clarifai.com/metro-north.jpg"))
        .executeSync();

csharp

using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Inputs;
using Clarifai.DTOs.Predictions;

namespace YourNamespace
{
    public class YourClassName
    {
        public static async Task Main()
        {
            var client = new ClarifaiClient("YOUR_API_KEY");

                 var response = await Client.Predict<Concept>(
                    "YOUR_MODEL_ID",
                    new ClarifaiURLImage("https://samples.clarifai.com/metro-north.jpg"),
                    modelVersionID: "MODEL_VERSION_ID")
                  .ExecuteAsync();
        }
    }
}

objective-c

//Coming soon

php

use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLImage;
use Clarifai\DTOs\Models\ModelType;
use Clarifai\DTOs\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;

$client = new ClarifaiClient('YOUR_API_KEY');

$response = $client->predict(ModelType::concept(), '{model_id}',
        new ClarifaiURLImage('https://samples.clarifai.com/puppy.jpeg'))
    ->withModelVersionID('MODEL_VERSION_ID')
    ->executeSync();

if ($response->isSuccessful()) {
    echo "Response is successful.\n";

    /** @var ClarifaiOutput $output */
    $output = $response->get();

    echo "Predicted concepts:\n";
    /** @var Concept $concept */
    foreach ($output->data() as $concept) {
        echo $concept->name() . ': ' . $concept->value() . "\n";
    }
} else {
    echo "Response is not successful. Reason: \n";
    echo $response->status()->description() . "\n";
    echo $response->status()->errorDetails() . "\n";
    echo "Status code: " . $response->status()->statusCode();
}

cURL

curl -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "inputs": [
      {
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/metro-north.jpg"
          }
        }
      }
    ]
  }'\
  https://api.clarifai.com/v2/models/aaa03c23b3724a16a56b629203edc62c/versions/aa7f35c01e0642fda5cf400f543e7c40/outputs

# Above is model ID of the publicly available General model.
# Version ID is optional. It defaults to the latest model version.