Images

Via URL

To get predictions for an input, you need to supply an image and the model you'd like to get predictions from. You can supply an image either with a publicly accessible URL or by directly sending bytes. You can send up to 128 images in one API call. You specify the model you'd like to use with the {model-id} parameter.

Below is an example of how you would send image URLs and receive back predictions from the general model.

You can learn all about the different Clarifai Models available later in the guide.

gRPC Java
gRPC NodeJS
gRPC Python
js
python
java
csharp
objective-c
php
cURL
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("{THE_MODEL_ID}")
.setVersionId("{THE_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: "{THE_MODEL_ID}",
version_id: "{THE_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="{THE_MODEL_ID}",
version_id="{THE_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
app.models.initModel({id: Clarifai.GENERAL_MODEL, version: "aa7f35c01e0642fda5cf400f543e7c40"})
.then(generalModel => {
return generalModel.predict("@@sampleTrain");
})
.then(response => {
var concepts = response['outputs'][0]['data']['concepts']
})
python
from clarifai.rest import ClarifaiApp
app = ClarifaiApp(api_key='YOUR_API_KEY')
model = app.public_models.general_model
response = model.predict_by_url('@@sampleTrain')
java
ConceptModel model = client.getDefaultModels().generalModel();
ModelVersion modelVersion = model.getVersionByID("the-version").executeSync().get();
ClarifaiResponse<List<ClarifaiOutput<Prediction>>> response = client.predict(model.id())
.withInputs(ClarifaiInput.forImage("@@sampleTrain"))
.withVersion("aa7f35c01e0642fda5cf400f543e7c40")
.executeSync();
csharp
using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Inputs;
namespace YourNamespace
{
public class YourClassName
{
public static async Task Main()
{
var Client = new ClarifaiClient("YOUR_API_KEY");
var response = await Client.Predict<Concept>(
Client.PublicModels.GeneralModel.ModelID,
new List<IClarifaiInput>
{
new ClarifaiURLImage("@@sampleTrain"),
new ClarifaiURLImage("the-url-2")
},
"aa7f35c01e0642fda5cf400f543e7c40")
.ExecuteAsync();
}
}
}
objective-c
ClarifaiImage *image = [[ClarifaiImage alloc] initWithURL:@"@@sampleTrain"];
[_app getModelByName:@"general-v1.3" 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\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;
$client = new ClarifaiClient('YOUR_API_KEY');
$model = $client->publicModels()->generalModel();
$input = new ClarifaiURLImage("@@sampleTrain");
$response = $model->predict($input)
->withModelVersionID("aa7f35c01e0642fda5cf400f543e7c40")
->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/{THE_MODEL_ID}/versions/{THE_MODEL_VERSION_ID}/outputs
Response JSON
Response JSON
{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "ea68cac87c304b28a8046557062f34a0",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2016-11-22T16:50:25Z",
"model": {
"name": "general-v1.3",
"id": "aaa03c23b3724a16a56b629203edc62c",
"created_at": "2016-03-09T17:11:39Z",
"app_id": null,
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "ea68cac87c304b28a8046557062f34a0",
"data": {
"image": {
"url": "https://samples.clarifai.com/metro-north.jpg"
}
}
},
"data": {
"concepts": [
{
"id": "ai_HLmqFqBf",
"name": "train",
"app_id": null,
"value": 0.9989112
},
{
"id": "ai_fvlBqXZR",
"name": "railway",
"app_id": null,
"value": 0.9975532
},
{
"id": "ai_Xxjc3MhT",
"name": "transportation system",
"app_id": null,
"value": 0.9959158
},
{
"id": "ai_6kTjGfF6",
"name": "station",
"app_id": null,
"value": 0.992573
},
{
"id": "ai_RRXLczch",
"name": "locomotive",
"app_id": null,
"value": 0.992556
},
{
"id": "ai_VRmbGVWh",
"name": "travel",
"app_id": null,
"value": 0.98789215
},
{
"id": "ai_SHNDcmJ3",
"name": "subway system",
"app_id": null,
"value": 0.9816359
},
{
"id": "ai_jlb9q33b",
"name": "commuter",
"app_id": null,
"value": 0.9712483
},
{
"id": "ai_46lGZ4Gm",
"name": "railroad track",
"app_id": null,
"value": 0.9690325
},
{
"id": "ai_tr0MBp64",
"name": "traffic",
"app_id": null,
"value": 0.9687052
},
{
"id": "ai_l4WckcJN",
"name": "blur",
"app_id": null,
"value": 0.9667078
},
{
"id": "ai_2gkfMDsM",
"name": "platform",
"app_id": null,
"value": 0.9624243
},
{
"id": "ai_CpFBRWzD",
"name": "urban",
"app_id": null,
"value": 0.960752
},
{
"id": "ai_786Zr311",
"name": "no person",
"app_id": null,
"value": 0.95864904
},
{
"id": "ai_6lhccv44",
"name": "business",
"app_id": null,
"value": 0.95720303
},
{
"id": "ai_971KsJkn",
"name": "track",
"app_id": null,
"value": 0.9494642
},
{
"id": "ai_WBQfVV0p",
"name": "city",
"app_id": null,
"value": 0.94089437
},
{
"id": "ai_dSCKh8xv",
"name": "fast",
"app_id": null,
"value": 0.9399334
},
{
"id": "ai_TZ3C79C6",
"name": "road",
"app_id": null,
"value": 0.93121606
},
{
"id": "ai_VSVscs9k",
"name": "terminal",
"app_id": null,
"value": 0.9230834
}
]
}
}
]
}

Via bytes

Below is an example of how you would send the bytes of an image and receive back predictions from the general model.

gRPC Java
gRPC NodeJS
gRPC Python
js
python
java
csharp
obj-c
php
cURL
gRPC Java
import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
import com.google.protobuf.ByteString;
import java.io.File;
import java.nio.file.Files;
// 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("{THE_MODEL_ID}")
.setVersionId("{THE_MODEL_VERSION_ID") // This is optional. Defaults to the latest model version.
.addInputs(
Input.newBuilder().setData(
Data.newBuilder().setImage(
Image.newBuilder()
.setBase64(ByteString.copyFrom(Files.readAllBytes(
new File("{YOUR_IMAGE_FILE_LOCATION}").toPath()
)))
)
)
)
.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
const fs = require("fs");
const imageBytes = fs.readFileSync("{YOUR_IMAGE_FILE_LOCATION}");
stub.PostModelOutputs(
{
model_id: "{THE_MODEL_ID}",
version_id: "{THE_MODEL_VERSION_ID}", // This is optional. Defaults to the latest model version.
inputs: [
{data: {image: {base64: imageBytes}}}
]
},
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
with open("{YOUR_IMAGE_FILE_LOCATION}", "rb") as f:
file_bytes = f.read()
post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
model_id="{THE_MODEL_ID}",
version_id="{THE_MODEL_VERSION_ID}", # This is optional. Defaults to the latest model version.
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(
base64=file_bytes
)
)
)
]
),
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, {base64: "G7p3m95uAl..."}).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.public_models.general_model
response = model.predict_by_filename('/home/user/image.jpeg')
# You could also use model.predict_by_bytes or model.predict_by_base64
java
client.getDefaultModels().generalModel().predict()
.withInputs(ClarifaiInput.forImage(new File("/home/user/image.jpeg")))
.executeSync();
csharp
using System.IO;
using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Inputs;
namespace YourNamespace
{
public class YourClassName
{
public static async Task Main()
{
var client = new ClarifaiClient("YOUR_API_KEY");
await client.PublicModels.GeneralModel.Predict(
new ClarifaiFileImage(File.ReadAllBytes("/home/user/image.jpeg")))
.ExecuteAsync();
}
}
}
obj-c
UIImage *image = [UIImage imageNamed:@"dress.jpg"];
ClarifaiImage *clarifaiImage = [[ClarifaiImage alloc] initWithImage:image];
[_app getModelByName:@"general-v1.3" completion:^(ClarifaiModel *model, NSError *error) {
[model predictOnImages:@[clarifaiImage]
completion:^(NSArray<ClarifaiSearchResult *> *outputs, NSError *error) {
NSLog(@"outputs: %@", outputs);
}];
}];
php
use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiFileImage;
use Clarifai\DTOs\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;
$client = new ClarifaiClient('YOUR_API_KEY');
$response = $client->publicModels()->generalModel()->predict(
new ClarifaiFileImage(file_get_contents('/home/user/image.jpeg')))
->executeSync();
if ($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: " . $response->status()->statusCode();
}
cURL
// Smaller files (195 KB or less)
curl -X POST \
-H "Authorization: Key YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '
{
"inputs": [
{
"data": {
"image": {
"base64": "'"$(base64 /home/user/image.jpeg)"'"
}
}
}
]
}'\
https://api.clarifai.com/v2/models/{THE_MODEL_ID}/outputs
// Larger Files (Greater than 195 KB)
curl -X POST \
-H "Authorization: Key YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d @- https://api.clarifai.com/v2/models/{model-id}/outputs << FILEIN
{
"inputs": [
{
"data": {
"image": {
"base64": "$(base64 /home/user/image.png)"
}
}
}
]
}
FILEIN
Response JSON
Response JSON
{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "e1cf385843b94c6791bbd9f2654db5c0",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2016-11-22T16:59:23Z",
"model": {
"name": "general-v1.3",
"id": "aaa03c23b3724a16a56b629203edc62c",
"created_at": "2016-03-09T17:11:39Z",
"app_id": null,
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "e1cf385843b94c6791bbd9f2654db5c0",
"data": {
"image": {
"url": "https://s3.amazonaws.com/clarifai-api/img/prod/b749af061d564b829fb816215f6dc832/e11c81745d6d42a78ef712236023df1c.jpeg"
}
}
},
"data": {
"concepts": [
{
"id": "ai_l4WckcJN",
"name": "blur",
"app_id": null,
"value": 0.9973569
},
{
"id": "ai_786Zr311",
"name": "no person",
"app_id": null,
"value": 0.98865616
},
{
"id": "ai_JBPqff8z",
"name": "art",
"app_id": null,
"value": 0.986006
},
{
"id": "ai_5rD7vW4j",
"name": "wallpaper",
"app_id": null,
"value": 0.9722556
},
{
"id": "ai_sTjX6dqC",
"name": "abstract",
"app_id": null,
"value": 0.96476805
},
{
"id": "ai_Dm5GLXnB",
"name": "illustration",
"app_id": null,
"value": 0.922542
},
{
"id": "ai_5xjvC0Tj",
"name": "background",
"app_id": null,
"value": 0.8775655
},
{
"id": "ai_tBcWlsCp",
"name": "nature",
"app_id": null,
"value": 0.87474406
},
{
"id": "ai_rJGvwlP0",
"name": "insubstantial",
"app_id": null,
"value": 0.8196385
},
{
"id": "ai_2Bh4VMrb",
"name": "artistic",
"app_id": null,
"value": 0.8142488
},
{
"id": "ai_mKzmkKDG",
"name": "Christmas",
"app_id": null,
"value": 0.7996079
},
{
"id": "ai_RQccV41p",
"name": "woman",
"app_id": null,
"value": 0.7955615
},
{
"id": "ai_20SCBBZ0",
"name": "vector",
"app_id": null,
"value": 0.7775099
},
{
"id": "ai_4sJLn6nX",
"name": "dark",
"app_id": null,
"value": 0.7715479
},
{
"id": "ai_5Kp5FMJw",
"name": "still life",
"app_id": null,
"value": 0.7657637
},
{
"id": "ai_LM64MDHs",
"name": "shining",
"app_id": null,
"value": 0.7542407
},
{
"id": "ai_swtdphX8",
"name": "love",
"app_id": null,
"value": 0.74926054
},
{
"id": "ai_h45ZTxZl",
"name": "square",
"app_id": null,
"value": 0.7449074
},
{
"id": "ai_cMfj16kJ",
"name": "design",
"app_id": null,
"value": 0.73926914
},
{
"id": "ai_LxrzLJmf",
"name": "bright",
"app_id": null,
"value": 0.73790145
}
]
}
}
]
}