This how-to-guide shows you how to easily label food images with Clarifai's Food model. The Food model is popular among users working with social media, hospitality, health and wellness related images and videos.

For example, the Food Model might return the following predictions for the image above:

Getting Started

First things first. You will need to set up a Clarifai account and create an application. This how-to article shows you how to label food items with the Clarifai API and through Portal. If you would like to label food items via API, you will also need to generate an API key.

Classify food items in Portal 

You can do almost anything that Clarifai can do with Clarifai Portal, and we work hard to make Portal the world's easiest interface for using AI. Classifying food items with Portal is as simple as uploading your data, and choosing the right base workflow. 

Create your application and choose "Food" as your base workflow

Simply log in to Clarifai Portal and create a new application. To use the food model, select "Food" as your base workflow. 

View your image in Explorer view

Upload an image to your application and view predictions in the righthand sidebar under the tab that says "App Workflow".

Classify food items in a local image 

Use the following Python snippet as an example of how to run a prediction on an image hosted on your local computer. For more details and information on working with predictions in our other client languages, please refer to our API documentation.

from clarifai_grpc.grpc.api import service_pb2, resources_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2

# This is how you authenticate.
metadata = (('authorization', 'Key {{YOUR_CLARIFAI_API_KEY}}'),)

with open("{YOUR_IMAGE_FILE_LOCATION}", "rb") as f:
    file_bytes = f.read()

request = service_pb2.PostModelOutputsRequest(
    model_id='9504135848be0dd2c39bdab0002f78e9',
    inputs=[
        resources_pb2.Input(
            data=resources_pb2.Data(
                image=resources_pb2.Image(
                    base64=file_bytes
                )
            )
        )      
    ])
response = stub.PostModelOutputs(request, metadata=metadata)

if response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Request failed, status code: " + str(response.status.code))

for concept in response.outputs[0].data.concepts:
    print('%12s: %.2f' % (concept.name, concept.value))

Classify food items on a remote image 

Here is an example of how to run a prediction on an image that is hosted on a URL. This snippet is in Python, but we offer support for many other client languages. Please refer to our API documentation for additional information. 

from clarifai_grpc.grpc.api import service_pb2, resources_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2

# This is how you authenticate.
metadata = (('authorization', 'Key {{YOUR_CLARIFAI_API_KEY}}'),)

request = service_pb2.PostModelOutputsRequest(
    model_id='9504135848be0dd2c39bdab0002f78e9',
    inputs=[
      resources_pb2.Input(data=resources_pb2.Data(image=resources_pb2.Image(url='{{YOUR_IMAGE_URL}}')))
    ])
response = stub.PostModelOutputs(request, metadata=metadata)

if response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Request failed, status code: " + str(response.status.code))

for concept in response.outputs[0].data.concepts:
    print('%12s: %.2f' % (concept.name, concept.value))