Custom Models

Clarifai makes it easy to customize and repurpose existing models.

You do not need many images to get started. We recommend starting with 10 and adding more as needed. Before you train your first model you will have needed to create an application and select a base workflow.

Add images with concepts

To get started training your own model, you must first add images that already contain the concepts you want your model to see.

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

MultiInputResponse postInputsResponse = stub.postInputs(
    PostInputsRequest.newBuilder()
        .addInputs(
            Input.newBuilder()
                .setData(
                    Data.newBuilder()
                        .setImage(
                            Image.newBuilder()
                                .setUrl("https://samples.clarifai.com/puppy.jpeg")
                                .setAllowDuplicateUrl(true)
                        )
                        .addConcepts(Concept.newBuilder().setId("charlie").setValue(1))
                        .addConcepts(Concept.newBuilder().setId("our_wedding").setValue(0))
                )
        )
        .addInputs(
            Input.newBuilder()
                .setData(
                    Data.newBuilder()
                        .setImage(
                            Image.newBuilder()
                                .setUrl("https://samples.clarifai.com/wedding.jpg")
                                .setAllowDuplicateUrl(true)
                        )
                        .addConcepts(Concept.newBuilder().setId("our_wedding").setValue(1))
                        .addConcepts(Concept.newBuilder().setId("charlie").setValue(0))
                        .addConcepts(Concept.newBuilder().setId("cat").setValue(0))
                )
        )
        .build()
);

if (postInputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    for (Input input : postInputsResponse.getInputsList()) {
        System.out.println("Input " + input.getId() + " status: ");
        System.out.println(input.getStatus() + "\n");
    }

    throw new RuntimeException("Post inputs failed, status: " + postInputsResponse.getStatus());
}

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.PostInputs(
    {
        inputs: [
            {
                data: {
                    image: {url: "https://samples.clarifai.com/puppy.jpeg", allow_duplicate_url: true},
                    concepts: [{id: "charlie", value: 1}, {id: "our_wedding", value: 0}]
                }
            },
            {
                data: {
                    image: {url: "https://samples.clarifai.com/wedding.jpg", allow_duplicate_url: true},
                    concepts: [{id: "our_wedding", value: 1}, {id: "charlie", value: 0}, {id: "cat", value: 0}]
                }
            },
        ]
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            for (const input of response.inputs) {
                console.log("Input " + input.id + " status: ");
                console.log(JSON.stringify(input.status, null, 2) + "\n");
            }

            throw new Error("Post inputs failed, status: " + response.status.description);
        }
    }
);

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_inputs_response = stub.PostInputs(
    service_pb2.PostInputsRequest(
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/puppy.jpeg",
                        allow_duplicate_url=True
                    ),
                    concepts=[
                        resources_pb2.Concept(id="charlie", value=1),
                        resources_pb2.Concept(id="our_wedding", value=0),
                    ]
                )
            ),
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/wedding.jpg",
                        allow_duplicate_url=True
                    ),
                    concepts=[
                        resources_pb2.Concept(id="our_wedding", value=1),
                        resources_pb2.Concept(id="charlie", value=0),
                        resources_pb2.Concept(id="cat", value=0),
                    ]
                )
            ),
        ]
    ),
    metadata=metadata
)

if post_inputs_response.status.code != status_code_pb2.SUCCESS:
    for input_object in post_inputs_response.inputs:
        print("Input " + input_object.id + " status:")
        print(input_object.status)

    raise Exception("Post inputs failed, status: " + post_inputs_response.status.description)

js

app.inputs.create({
  url: "https://samples.clarifai.com/puppy.jpeg",
  concepts: [
    {
      id: "charlie",
      value: true
    }
  ]
});

python

from clarifai.rest import ClarifaiApp
from clarifai.rest import Image as ClImage

app = ClarifaiApp(api_key='YOUR_API_KEY')

# add multiple images with concepts
img1 = ClImage(url="https://samples.clarifai.com/puppy.jpeg", concepts=['charlie'], not_concepts=['our_wedding'])
img2 = ClImage(url="https://samples.clarifai.com/wedding.jpg", concepts=['our_wedding'], not_concepts=['cat','charlie'])

app.inputs.bulk_create_images([img1, img2])

java

client.addInputs()
    .plus(
        ClarifaiInput.forImage("https://samples.clarifai.com/puppy.jpeg")
            .withConcepts(Concept.forID("charlie"))
    )
    .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.AddInputs(
                    new ClarifaiURLImage(
                        "https://samples.clarifai.com/puppy.jpeg",
                        positiveConcepts: new List<Concept> {new Concept(id: "charlie")}))
                .ExecuteAsync();
        }
    }
}

objective-c

ClarifaiImage *image = [[ClarifaiImage alloc] initWithURL:@"https://samples.clarifai.com/puppy.jpeg" andConcepts:@"cute puppy"];
[_app addInputs:@[image] completion:^(NSArray<ClarifaiInput *> *inputs, NSError *error) {
    NSLog(@"inputs: %@", inputs);
}];

php

use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLImage;
use Clarifai\DTOs\Predictions\Concept;

$client = new ClarifaiClient('YOUR_API_KEY');

$response = $client->addInputs(
        (new ClarifaiURLImage('https://samples.clarifai.com/puppy.jpeg'))
        ->withAllowDuplicateUrl(true)
        ->withPositiveConcepts([new Concept('charlie')]))
    ->executeSync();

if ($response-> isSuccessful()) {
    echo "Response is successful.\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/puppy.jpeg",
            "allow_duplicate_url": true
          },
          "concepts":[
            {
              "id": "charlie",
              "value": 1
            },
            {
              "id": "our_wedding",
              "value": 0
            }
          ]
        }
      },
      {
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/wedding.jpg",
            "allow_duplicate_url": true
          },
          "concepts":[
            {
              "id": "our_wedding",
              "value": 1
            },
            {
              "id": "charlie",
              "value": 0
            },
            {
              "id": "cat",
              "value": 0
            }
          ]
        }
      }
    ]
  }'\
  https://api.clarifai.com/v2/inputs

Response JSON

{
  "status": {
    "code": 10000,
    "description": "Ok"
  },
  "inputs": [
    {
      "id": "e82fd13b11354d808cc48dc8f94ec3a9",
      "created_at": "2016-11-22T17:16:00Z",
      "data": {
        "image": {
          "url": "https://samples.clarifai.com/puppy.jpeg"
        },
        "concepts": [
          {
            "id": "charlie",
            "name": "charlie",
            "app_id": "f09abb8a57c041cbb94759ebb0cf1b0d",
            "value": 1
          }
        ]
      },
      "status": {
        "code": 30000,
        "description": "Download complete"
      }
    }
  ]
}

Create a model

Once your images with concepts are added, you are now ready to create the model. You'll need a name for the model and you'll also need to provide it with the concepts you added above.

Take note of the model id that is returned in the response. You'll need that for the next two steps.

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

SingleModelResponse postModelsResponse = stub.postModels(
    PostModelsRequest.newBuilder().addModels(
        Model.newBuilder()
            .setId("pets")
            .setOutputInfo(
                OutputInfo.newBuilder()
                    .setData(
                        Data.newBuilder().addConcepts(Concept.newBuilder().setId("charlie"))
                    )
                    .setOutputConfig(
                        OutputConfig.newBuilder()
                            .setConceptsMutuallyExclusive(false)
                            .setClosedEnvironment(false)
                    )
            )
    ).build()
);

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

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.PostModels(
    {
        models: [
            {
                id: "pets",
                output_info: {
                    data: {concepts: [{id: "charlie"}]},
                    output_config: {concepts_mutually_exclusive: false, closed_environment: false}
                }
            }
        ]
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

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

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_models_response = stub.PostModels(
    service_pb2.PostModelsRequest(
        models=[
            resources_pb2.Model(
                id="pets",
                output_info=resources_pb2.OutputInfo(
                    data=resources_pb2.Data(
                        concepts=[resources_pb2.Concept(id="charlie", value=1)]
                    ),
                    output_config=resources_pb2.OutputConfig(
                        concepts_mutually_exclusive=False,
                        closed_environment=False
                    )
                )
            )
        ]
    ),
    metadata=metadata
)

if post_models_response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Post models failed, status: " + post_models_response.status.description)

js

app.models.create(
  "pets",
  [
    { "id": "charlie" }
  ]
).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.create('pets', concepts=['charlie'])

java

client.createModel("pets")
    .withOutputInfo(ConceptOutputInfo.forConcepts(
        Concept.forID("charlie")
    ))
    .executeSync();

csharp

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

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

            await client.CreateModel(
                    "pets",
                    concepts: new List<Concept> {new Concept("charlie")})
                .ExecuteAsync();
        }
    }
}

objective-c

[app createModel:@[concept] name:modelName conceptsMutuallyExclusive:NO closedEnvironment:NO
      completion:^(ClarifaiModel *model, NSError *error) {
        NSLog(@"model: %@", model);
}];

php

use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Predictions\Concept;

$client = new ClarifaiClient('YOUR_API_KEY');

$response = $client->createModel('pets')
    ->withConcepts([new Concept('charlie')])
    ->executeSync();

if ($response-> isSuccessful()) {
    echo "Response is successful.\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 '
  {
    "model": {
      "id": "pets",
      "output_info": {
        "data": {
          "concepts": [
            {
              "id": "charlie"
            }
          ]
        },
        "output_config": {
          "concepts_mutually_exclusive": false,
          "closed_environment":false
        }
      }
    }
  }'\
  https://api.clarifai.com/v2/models

Response JSON

{
  "status": {
    "code": 10000,
    "description": "Ok"
  },
  "model": {
    "name": "pets",
    "id": "a10f0cf48cf3426cbb8c4805e246c214",
    "created_at": "2016-11-22T17:17:36Z",
    "app_id": "f09abb8a57c041cbb94759ebb0cf1b0d",
    "output_info": {
      "message": "Show output_info with: GET /models/{model_id}/output_info",
      "type": "concept",
      "output_config": {
        "concepts_mutually_exclusive": false,
        "closed_environment": false
      }
    },
    "model_version": {
      "id": "e7bcd534b61b4874a3ab69fba974c012",
      "created_at": "2016-11-22T17:17:36Z",
      "status": {
        "code": 21102,
        "description": "Model not yet trained"
      }
    }
  }
}

Train the model

Now that you've added images with concepts, then created a model with those concepts, the next step is to train the model. When you train a model, you are telling the system to look at all the images with concepts you've provided and learn from them. This train operation is asynchronous. It may take a few seconds for your model to be fully trained and ready.

Keep note of the model_version id in the response. We'll need that for the next section when we predict with the 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

SingleModelResponse postModelVersionsResponse = stub.postModelVersions(
    PostModelVersionsRequest.newBuilder()
        .setModelId("pets")
        .build()
);

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

String modelVersionId = postModelVersionsResponse.getModel().getModelVersion().getId();
System.out.println("New model version ID: " + modelVersionId);

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.PostModelVersions(
    {model_id: "pets"},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

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

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_versions = stub.PostModelVersions(
    service_pb2.PostModelVersionsRequest(
        model_id="pets"
    ),
    metadata=metadata
)

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

js

app.models.train("{model_id}").then(
  function(response) {
    // do something with response
  },
  function(err) {
    // there was an error
  }
);

// or if you have an instance of a model

model.train().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('{model_id}')
model.train()

java

client.trainModel("{model_id}").executeSync();

csharp

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

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

            await client.TrainModel<Concept>("{model_id}")
                .ExecuteAsync();
        }
    }
}

objective-c

ClarifaiImage *image = [[ClarifaiImage alloc] initWithURL:@"https://samples.clarifai.com/puppy.jpeg"]
[app getModel:@"{id}" completion:^(ClarifaiModel *model, NSError *error) {
    [model train:^(ClarifaiModel *model, NSError *error) {
        NSLog(@"model: %@", model);
    }];
}];

php

use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Models\ModelType;

$client = new ClarifaiClient('YOUR_API_KEY');

$response = $client->trainModel(ModelType::concept(), 'MODEL_ID')
    ->executeSync();

if ($response-> isSuccessful()) {
    echo "Response is successful.\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" \
  https://api.clarifai.com/v2/models/pets/versions

Response JSON

{
  "status": {
    "code": 10000,
    "description": "Ok"
  },
  "model": {
    "name": "pets",
    "id": "a10f0cf48cf3426cbb8c4805e246c214",
    "created_at": "2016-11-22T17:17:36Z",
    "app_id": "f09abb8a57c041cbb94759ebb0cf1b0d",
    "output_info": {
      "message": "Show output_info with: GET /models/{model_id}/output_info",
      "type": "concept",
      "output_config": {
        "concepts_mutually_exclusive": false,
        "closed_environment": false
      }
    },
    "model_version": {
      "id": "d1b38fd2251148d08675c5542ef00c7b",
      "created_at": "2016-11-22T17:21:13Z",
      "status": {
        "code": 21103,
        "description": "Custom model is currently in queue for training, waiting on inputs to process."
      }
    }
  }
}

Predict with the model

Now that we have a trained model we can start making predictions with it. In our predict call we specify three items. The model id, model version id (optional, defaults to the latest trained version) and the input we want a prediction for.

Note: you can repeat the above steps as often as you like. By adding more images with concepts and training, you can get the model to predict exactly how you want it to.

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("pets")
        .setVersionId("{YOUR_MODEL_VERSION_ID}")  // 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: "pets",
        version_id: "{YOUR_MODEL_VERSION_ID}",  // 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="pets",
        version_id="{YOUR_MODEL_VERSION_ID}",  # 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("%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('MODEL_ID')
model.model_version = 'MODEL_VERSION_ID'  # This is optional. Defaults to the latest model version.

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

ClarifaiImage *image = [[ClarifaiImage alloc] initWithURL:@"https://samples.clarifai.com/puppy.jpeg"]
[app getModel:@"{id}" completion:^(ClarifaiModel *model, NSError *error) {
    [model predictOnImages:@[image]
                completion:^(NSArray<ClarifaiSearchResult *> *outputs, NSError *error) {
                    NSLog(@"outputs: %@", outputs);
                }];
}];

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'))
    ->executeSync();

if ($response-> isSuccessful()) {
    /** @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/pets/versions/{YOUR_MODEL_VERSION_ID}/outputs

Response JSON

{
  "status": {
    "code": 10000,
    "description": "Ok"
  },
  "outputs": [
    {
      "id": "e8b6eb27de764f3fa8d4f7752a3a2dfc",
      "status": {
        "code": 10000,
        "description": "Ok"
      },
      "created_at": "2016-11-22T17:22:23Z",
      "model": {
        "name": "pets",
        "id": "a10f0cf48cf3426cbb8c4805e246c214",
        "created_at": "2016-11-22T17:17:36Z",
        "app_id": "f09abb8a57c041cbb94759ebb0cf1b0d",
        "output_info": {
          "message": "Show output_info with: GET /models/{model_id}/output_info",
          "type": "concept",
          "output_config": {
            "concepts_mutually_exclusive": false,
            "closed_environment": false
          }
        },
        "model_version": {
          "id": "d1b38fd2251148d08675c5542ef00c7b",
          "created_at": "2016-11-22T17:21:13Z",
          "status": {
            "code": 21100,
            "description": "Model trained successfully"
          }
        }
      },
      "input": {
        "id": "e8b6eb27de764f3fa8d4f7752a3a2dfc",
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/puppy.jpeg"
          }
        }
      },
      "data": {
        "concepts": [
          {
            "id": "charlie",
            "name": "charlie",
            "app_id": "f09abb8a57c041cbb94759ebb0cf1b0d",
            "value": 0.98308545
          }
        ]
      }
    }
  ]
}