The Detection endpoint returns data identifying the species of the animal based on the uploaded paw print image. The response includes a list of potential species matches along with confidence scores for each identification.

The key fields in the response data include "species" (the identified animal species), "confidence_score" (the accuracy of the identification), and "image_url" (the URL of the uploaded paw print image).

The Detection endpoint requires a single parameter: "image_url," which should be a valid URL pointing to the paw print image you want to analyze.

The response data is organized in a JSON format, containing an array of identified species, each with its corresponding confidence score. This structure allows for easy parsing and analysis.

The API utilizes a vast dataset of paw print images collected from various wildlife studies and databases. This diverse source helps improve the accuracy of species identification.

Data accuracy is maintained through continuous training of machine learning models on updated datasets, along with validation against expert-reviewed paw print samples to ensure reliable species identification.

Typical use cases include wildlife research, conservation efforts, educational purposes, and assisting in tracking animal movements in ecological studies by identifying species from paw prints.

If the API returns partial or empty results, users should check the image quality and ensure the paw print is clear. Additionally, consider trying different images or consulting the API documentation for troubleshooting tips.

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