The Detection endpoint returns breed classifications for uploaded animal images, along with a confidence score indicating the accuracy of the identification.

The key fields in the response data include "breed" (the identified breed), "confidence" (a score from 0 to 1 indicating identification reliability), and potentially "species" (the type of animal).

The Detection endpoint requires a single parameter: a URL pointing to the image of the animal to be analyzed.

The response data is structured in JSON format, typically containing fields for breed, confidence score, and possibly additional metadata about the image or species.

Typical use cases include pet identification for owners, breed classification for veterinary purposes, and wildlife research for identifying species from images.

Data accuracy is maintained through advanced machine learning algorithms trained on a diverse dataset of animal images, ensuring reliable breed identification across various species.

Users can utilize the returned data by interpreting the breed and confidence score to make informed decisions, such as selecting appropriate care or training methods based on the identified breed.

Users can expect consistent data patterns where higher confidence scores correlate with more common breeds, while lower scores may indicate less certainty or rare breeds.

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