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Gather valuable business insights from images, video, and text using computer vision and natural language processing in one integrated AI platform.
Analyze images and returns numerical vectors that represent each detected face in the image in a 1024-dimensional space computed by our General model.
Detect toxic, obscene, racist or threatening language, or train your own custom moderation models.
Assign tags or any custom concept to analyze text based on its contents. Build accurate models for topic and sentiment analysis and smart reply.
Analyze images and return numerical vectors that represent each detected face in the image in a 1024-dimensional space.
Recognize textures and patterns in a two-dimensional image e.g., feathers, woodgrain, petrified wood, glacial ice and overarching descriptive concepts (veined, metallic).
Identify different levels of nudity in your visual data. Ideal for moderating and filtering offensive content from your platform.
Identify unwanted content such as gore, drugs, explicit nudity or suggestive nudity.
Explore our pre-built, ready-to-use image recognition models to suit your specific needs.
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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))
stub.PostModelOutputs(
{
model_id: "{THE_MODEL_ID}",
inputs: [
{data: {image: {url: "https://samples.clarifai.com/metro-north.jpg"}}}
]
},
metadata,
(err, response) => {
console.log("Predicted concepts:");
for (const concept of response.outputs[0].data.concepts) {
console.log(concept.name + " " + concept.value);
}
}
);
MultiOutputResponse response = stub.postModelOutputs(
PostModelOutputsRequest.newBuilder()
.setModelId("{THE_MODEL_ID}")
.addInputs(
Input.newBuilder().setData(
Data.newBuilder().setImage(
Image.newBuilder().setUrl("https://samples.clarifai.com/metro-north.jpg")
)
)
)
.build()
);
System.out.println("Predicted concepts:");
for (Concept concept : response.getOutputs(0).getData().getConceptsList()) {
System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());
}
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}/outputs
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