For developers to provide tools to get precise dog breed classification the most efficient approach is by using an API. This approach also works on the main concept of transfer learning which deals with data augmentation technique with its properties to increase the size of data set, after which accuracy levels are matched or it is compared with both the models so that a comparison can be made for both the models and the classification is also done with a profound approach.
The most common and popular pets in the world are dogs. There are more than 400 dog breeds in the world, and they are a favorite among many people. However, despite their popularity, it is not always easy to identify the breed of a dog. This is due to the fact that there are many different types of dogs and that some look very similar to other dogs. In addition, each dog has its own characteristics, and it is difficult to recognize the genetic load, especially in mongrel dogs.
Accuracy With The Use Of An API
In this case, Dog Breed Data API is a relevant tool for the purpose. It guarantees confidence: this is a score between 0 to 1. When closer to 1 the better the recognition of the AI for the object in the picture.
Image recognition and classification have successfully applied in various domains, such as face recognition and scene understanding for autonomous driving. At present, human face identification is successfully used for authentication and security purposes in many applications. Therefore, the extend studies from human to animal recognition has been successful. In particular, dogs are one of the most common animals. Since there are more than 180 dog breeds, dog breed recognition can be an essential task in order to provide proper training and health treatment. Previously, dog breed recognition used to be done by human experts. However, some dog breeds might be challenging to evaluate due to the lack of experts and the difficulty of breeds’ patterns themselves. It also takes time for each evaluation.
Besides, there are several studies on using dog images to identify their breeds. Some papers have proposed using coarse to fine classification by grouping similar face contours as a coarse classification and then applying a principle component analysis (PCA) classifier within the output group as fine dog breed classification. Another opinion has extended the coarse to fine classification by adding local parts to reduce misclassification within the same group. AI and Deep Learning make developers and users certain of the accuracy of the API.
How Does A Dog Breed Identifier API Work?
An API for dog breeds makes it much easier because it allows to recognize and classify all their features automatically and just by uploading images.
First of all, subscribe at www.zylalabs.com to start using Dog Breed Classification API. Then write the provided API key and enter the verification token. Upload the image URL and wait a few seconds.
The API returns information about the image and its categories. The response will look like this:
{
"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.