{"success":true,"image_url":"https://today.usc.edu/wp-content/uploads/2019/11/Taiwan-Blue-Magpie-web-1280x720.jpg","output":[{"label":"Taiwan Blue Magpie","score":0.95}]}
curl --location --request POST 'https://pr213-testing.zylalabs.com/api/6360/feather+identification+api/9121/feather+classification?url=https://today.usc.edu/wp-content/uploads/2019/11/Taiwan-Blue-Magpie-web-1280x720.jpg' --header 'Authorization: Bearer YOUR_API_KEY'
After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Feather Identification API simply include your bearer token in the Authorization header.
| Header | Description |
|---|---|
Authorization
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Required
Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.
|
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The Feather Identification API is a cutting-edge tool designed to accurately identify bird species from images of feathers using advanced image recognition and machine learning technologies. This API provides precise classifications, making it an invaluable resource for ornithologists, researchers, conservationists, and bird enthusiasts. By analyzing feather patterns, colors, and structures, it offers reliable species identification, supporting biodiversity studies and ecological research.
The Feather Classification endpoint returns data that includes the identified bird species, a confidence score for the identification, and the original image URL. This allows users to verify the identification against the provided image.
The key fields in the response data include "success" (indicating the request status), "image_url" (the URL of the submitted image), and "output" (an array containing species labels and their corresponding confidence scores).
The primary parameter for the Feather Classification endpoint is "url," which should contain the URL of the feather image to be analyzed. This parameter is essential for the API to process the image.
The response data is organized in a JSON format. It includes a top-level success indicator, the image URL, and an output array with objects that contain species labels and confidence scores, making it easy to parse and utilize.
The data used for identification is derived from extensive image datasets and machine learning models trained on various feather characteristics. This ensures a broad and accurate representation of bird species.
Typical use cases include ornithological research, birdwatching applications, educational tools for identifying bird species, and conservation efforts that require accurate species identification from feather samples.
Users can utilize the returned data by interpreting the "output" array to identify bird species and their confidence scores. Higher scores indicate more reliable identifications, guiding users in their research or conservation efforts.
Data accuracy is maintained through continuous training of machine learning models with updated datasets, validation against expert classifications, and regular quality checks to ensure reliable species identification.
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