The Recognizer endpoint returns a JSON object containing a list of recognized objects in the provided image. Each object includes a confidence score and a label indicating the type of vehicle or object detected.

The key fields in the response data are "results," which is an array of detected objects. Each object has "score" (confidence level) and "label" (name of the detected object).

The response data is structured as a JSON object. It contains a "results" array, where each entry includes a "score" and a "label," allowing users to easily interpret the detected objects and their confidence levels.

The Recognizer endpoint provides information on various vehicle types and other objects detected in an image, along with their confidence scores, enabling users to categorize and analyze visual content effectively.

Users can customize their data requests by providing different image URLs to the Recognizer endpoint. Each URL will yield results specific to the content of the image, allowing for tailored analysis.

Typical use cases include categorizing vehicle images for car retailers, organizing large image databases, and developing applications that require real-time object recognition for vehicles and other objects.

Data accuracy is maintained through a well-trained machine-learning algorithm that continuously learns from a diverse dataset of vehicle images, ensuring reliable recognition of various vehicle types.

If the Recognizer endpoint returns partial or empty results, users should check the image quality and content. Low-quality images or images without recognizable objects may lead to lower confidence scores or no detections.

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