Developers need to find a way to develop software that will assist common users to get accurate dog breed classification results, and the best tool is an API. The efficiency and accuracy of results beats all other ways!
Dogs are classified into breeds, and these breeds are classified into types or groups according to size, shape, function and temperament. For instance, it makes sense that dogs bred to be companions are small and fluffy, while sledge dogs will be strongly built and have a thick double coat.
The most accurate dog classification results can be achieved by using Dog Breed Information API. Since the classification of dogs is becoming very difficult and moreover, these classifications are taken on the deep learning concept and training, a fully defined data set helps in training both models which predicts the different accuracy levels at both ends.
APIs are a great way to get accurate results when it comes to dog breed identification, a process that can be inaccurate when relying on the human eye, especially when it`s about mongrel dogs.
By simply uploading the URL of an image of the dog you want to identify the API will return information about it, including its breed and its likelihood of being part of a specific breed. This information will serve a purpose in planning a training program, in defining a diet, in choosing a breed for a specific service (company, guide, guardian, etc.).
Needless to say that an API is easy to integrate into any existing systems or applications. Dog Breed Information API is integrated with a suite of APIs from the same vendor that augment its accuracy, efficiency, ease of use and celerity of response. These APIs include: Detect Dogs In Images API, Dog Breed Classification API, Dog Breed Data API, Dog Breed Detector API, Dog Breed Information API, and many others.
To make use of it, go to Dog Breed Classification API and simply click on the button “Subscribe” to start using the API. With the provided personal API key one can start to use, connect, and manage APIs!
In seconds the application returns its response, that will look like this example:
{
"results": [
{
"score": 0.9873785376548767,
"label": "German shepherd, German shepherd dog, German police dog, alsatian"
},
{
"score": 0.0025157087948173285,
"label": "kelpie"
},
{
"score": 0.0009707494755275548,
"label": "malinois"
},
{
"score": 0.0008986197062768042,
"label": "dingo, warrigal, warragal, Canis dingo"
},
{
"score": 0.0005087173776701093,
"label": "bloodhound, sleuthhound"
}
]
}
Also published on Medium.