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Multiple Choice (Choose One) Detectors

If you want to classify images into multiple categories, you can create a multi-class detector.

from groundlight import ExperimentalApi
gl_exp = ExperimentalApi()

class_names = ["Golden Retriever", "Labrador Retriever", "German Shepherd", "Other"]
detector = gl_exp.create_multiclass_detector(
    name="dog-breed-detector",
    query="What kind of dog is this?",
    class_names=class_names,
)
tip

We recommend adding an "Other" class to your multi-class detector to handle cases where the image does not belong to any of the pre-defined classes.

note

Multi-Class Detectors are available on Business and Enterprise plans.

Submit an Image Query to a Multi-Class Detector​

Now that you have created a multi-class detector, you can submit an image query to it.

from groundlight import ExperimentalApi
gl_exp = ExperimentalApi()

detector = gl_exp.get_detector_by_name("dog-breed-detector")

# Classify the breed of a dog in an image
image_query = gl_exp.submit_image_query(detector, "path/to/image.jpg")

print(f"Result: {image_query.result.label}")
print(f"Confidence: {image_query.result.confidence}")

Multi-class detectors return a label attribute in the result object, which contains the predicted class label. The label attribute will be one of the class names provided when creating the detector. The confidence attribute represents the confidence level in the predicted class, which is a value between 1/len(class_names) and 1.

Add a label to a Multi-Class Detector​

To provide ground truth labels for multi-class detectors, you can specify the label of the correct class.

from groundlight import ExperimentalApi
gl_exp = ExperimentalApi()

# Add a multi-class label to the image query from the previous example
gl_exp.add_label(image_query, label="German Shepherd")