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Label and organize your data faster using AI-automated data labeling, data augmentation, denoising and deduping tools. 

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Quickly prototype ideas, design models, manage versions and run repeatable evaluations. Use transfer learning for quick training or optimize hyperparameters for optimal performance.

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Deploy your AI models anywhere you need them. Use our cloud API, web-based UI or deploy on-premise or at the edge. We maintain the infrastructure, so you don’t have to.

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response = stub.PostModelOutputs(
    service_pb2.PostModelOutputsRequest(
        model_id="{THE_MODEL_ID}",
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/metro-north.jpg"
                    )
                )
            )
        ]
    ),
    metadata=metadata
)
print("Predicted concepts:")
for concept in response.outputs[0].data.concepts:
    print(concept.name + " " + str(concept.value))
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/e0be3b9d6a454f0493ac3a30784001ff/outputs

 

MultiOutputResponse response = stub.postModelOutputs(
PostModelOutputsRequest.newBuilder()
.setModelId("aaa03c23b3724a16a56b629203edc62c")
.addInputs(
Input.newBuilder().setData(
Data.newBuilder().setImage(
Image.newBuilder().setUrl("YOUR_IMAGE_URL")
)
)
)
.build()
);
const request = new service.PostModelOutputsRequest();
request.setModelId("aaa03c23b3724a16a56b629203edc62c");
request.addInputs(
    new resources.Input()
        .setData(
            new resources.Data()
                .setImage(
                    new resources.Image()
                        .setUrl("https://samples.clarifai.com/dog2.jpeg")
                )
        )
)
[$response, $status] = $client->PostModelOutputs(
new PostModelOutputsRequest([
'model_id' => 'aaa03c23b3724a16a56b629203edc62c',
'inputs' => [
new Input([
'data' => new Data([
'image' => new Image([
'url' => 'https://samples.clarifai.com/dog2.jpeg'
])
])
])
]
]),
$metadata
)->wait();
var GeneralModelId = "aaa03c23b3724a16a56b629203edc62c"
response, err := client.PostModelOutputs(
ctx,
&api.PostModelOutputsRequest{
ModelId: GeneralModelId,
Inputs: []*api.Input{
{
Data: &api.Data{
Image: &api.Image{
Url: "https://samples.clarifai.com/dog2.jpeg",
},
},
},
},
},
)
const GENERAL_MODEL_ID: &str = "aaa03c23b3724a16a56b629203edc62c";

let request = service::PostModelOutputsRequest {
model_id: GENERAL_MODEL_ID.to_string(),
inputs: RepeatedField::from(vec![resources::Input {
data: SingularPtrField::some(resources::Data {
image: SingularPtrField::some(resources::Image {
url: "https://samples.clarifai.com/dog2.jpeg".to_string(),
..Default::default()
}),
..Default::default()
}),
..Default::default()
}]),
..Default::default()
};
string GENERAL_MODEL_ID = "aaa03c23b3724a16a56b629203edc62c";

PostModelOutputsRequest request;
request.set_model_id(GENERAL_MODEL_ID);

Input* input = request.add_inputs();
Data* data = input->mutable_data();
Image* image = data->mutable_image();
image->set_url("https://samples.clarifai.com/dog2.jpeg");

MultiOutputResponse response;
grpc::Status status = stub->PostModelOutputs(context.get(), request, &response);

if (!status.ok()) {
cout << "Failure: " << status.error_code() << " " << status.error_message() << endl;
exit(1);
}

if (response.status().code() != status::StatusCode::SUCCESS) {
cout << "Error response: " << response.status().code() << " " << response.status().description() << " " << response.status().details() << endl;
exit(1);
}

Data response_data = response.outputs(0).data();
cout << "Predicted concepts:" << endl;
for (int i = 0; i < response_data.concepts_size(); i++) {
const Concept& c = response_data.concepts(i);
cout << "\t" << c.name() << ": " << c.value() << endl;
}
var response = client.PostModelOutputs(
new PostModelOutputsRequest()
{
ModelId = "aaa03c23b3724a16a56b629203edc62c",
Inputs =
{
new List<Input>()
{
new Input()
{
Data = new Data()
{
Image = new Image()
{
Url = "https://samples.clarifai.com/dog2.jpeg"
}
}
}
}
}
},
metadata
);
let response = try client.postModelOutputs(
Clarifai_Api_PostModelOutputsRequest.with {
$0.modelID = "aaa03c23b3724a16a56b629203edc62c";
$0.inputs = [
Clarifai_Api_Input.with {
$0.data = Clarifai_Api_Data.with {
$0.image = Clarifai_Api_Image.with {
$0.url = "https://samples.clarifai.com/dog2.jpeg"
}
}
}
]
}
).response.wait()
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