The Species Detection API is designed to accurately identify animal species from images. It provides a fast and reliable way to recognize a wide variety of animals by simply uploading a picture. The API supports identification of both common domestic animals and rare wildlife found in remote habitats.
Leveraging cutting-edge machine learning models trained on large-scale datasets, the API analyzes visual features such as fur patterns, skin texture, coloration, body shape, and other distinctive characteristics to determine species.
The Species Detection API is easy to implement and fully compatible with multiple programming languages and platforms. Its intuitive interface allows developers to seamlessly integrate it into web or mobile applications without hassle.
To use this endpoint you must indicate a url in the parameter.
Animal Identifier - Endpoint Features
| Object | Description |
|---|---|
url |
[Required] The image URL that you want to classify. |
{"success":true,"image_url":"https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG","output":[{"label":"Orangutan","score":0.99}]}
curl --location --request POST 'https://zylalabs.com/api/7122/species+detection+api/11229/animal+identifier?url=https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG' --header 'Authorization: Bearer YOUR_API_KEY'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
The Animal Identifier endpoint returns a JSON object containing a success status, the image URL, and an array of classification results. Each result includes a cat species label and a confidence score indicating the accuracy of the classification.
The key fields in the response data include "success" (boolean), "image_url" (string), and "output" (array). The "output" array contains objects with "label" (species name) and "score" (confidence level).
The response data is structured as a JSON object. It starts with a success indicator, followed by the image URL, and concludes with an array of classification results, each detailing the identified species and its confidence score.
The Animal Identifier endpoint provides information on species, including their names and confidence scores based on image analysis.
The Animal Identifier endpoint requires a single parameter: the image URL. Users must provide a valid URL pointing to the bird image they wish to classify.
Users can utilize the returned data by interpreting the "label" for species identification and the "score" to assess the reliability of the classification. Higher scores indicate greater confidence in the identification.
Data accuracy is maintained through advanced machine learning algorithms that analyze visual features of cats. Continuous training with diverse datasets helps improve the model's performance and reliability.
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