Custom model walkthrough

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.

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

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.jpg", concepts=['boscoe'], not_concepts=['our_wedding'])
img2 = ClImage(url="https://samples.clarifai.com/wedding.jpg", concepts=['our_wedding'], not_concepts=['cat','boscoe'])

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

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.jpg",
                        positiveConcepts: new List<Concept> {new Concept(id: "boscoe")}))
                .ExecuteAsync();
        }
    }
}

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

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('boscoe')]))
    ->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 -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "inputs": [
      {
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/puppy.jpg"
          },
          "concepts":[
            {
              "id": "boscoe",
              "value": true
            }
          ]
        }
      }
    ]
  }'\
  https://api.clarifai.com/v2/inputs

{
  "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": "boscoe",
            "name": "boscoe",
            "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.

app.models.create(
  "pets",
  [
    { "id": "boscoe" }
  ]
).then(
  function(response) {
    // do something with response
  },
  function(err) {
    // there was an error
  }
);

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("boscoe")})
                .ExecuteAsync();
        }
    }
}

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

$client = new ClarifaiClient('YOUR_API_KEY');

$response = $client->createModel('pets')
    ->withConcepts([new Concept('boscoe')])
    ->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 -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "model": {
      "id": "pets",
      "output_info": {
        "data": {
          "concepts": [
            {
              "id": "boscoe"
            }
          ]
        },
        "output_config": {
          "concepts_mutually_exclusive": false,
          "closed_environment":false
        }
      }
    }
  }'\
  https://api.clarifai.com/v2/models

{
  "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.

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
  }
);

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();
        }
    }
}

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

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();
}

{
  "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 need to specify three items. The model id, model_version id 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.

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
  }
);

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')

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();

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();
        }
    }
}

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

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 -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/<model_id>/versions/<model_version_id>/outputs

{
  "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": "boscoe",
            "name": "boscoe",
            "app_id": "f09abb8a57c041cbb94759ebb0cf1b0d",
            "value": 0.98308545
          }
        ]
      }
    }
  ]
}

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