appwisp
  • App explorer
  • SDKs insights
  • API
  • Contact
  • About
  • API
  • Github
© 2025 appwisp.com

Cat Breed Auto Identify Photo

ru.electronikas.catsexpert

View detailed information for Cat Breed Auto Identify Photo — ratings, download counts, screenshots, pricing and developer details. See integrated SDKs and related technical data.

Total installs
63.9K(63,986)
Rating
2.6(10 reviews)
Released
July 5, 2019
Last updated
April 6, 2025
Category
Entertainment
Developer
Nikas
Developer details

Name
Nikas
E-mail
[email protected]
Website
unknown
Country
unknown
Address
unknown
Android SDKs

  • Android SDK
  • Google Firebase
  • Yandex AppMetrica
Cat Breed Auto Identify Photo Header - AppWisp.com

Screenshots

Cat Breed Auto Identify Photo Screenshot 1 - AppWisp.com
Cat Breed Auto Identify Photo Screenshot 2 - AppWisp.com
Cat Breed Auto Identify Photo Screenshot 3 - AppWisp.com
Cat Breed Auto Identify Photo Screenshot 4 - AppWisp.com

Description

What is the application?
It specifies the breed of cat by pictures using your device's camera or image gallery.

How does it works?
The photo is fed to the input of the neural network (at the moment the EfficientNetV2 architecture is used) and at its output a hypothesis is formed about what breed of cat is shown in this photo. The new version of the classifier has become less playful and only reacts to photos of real cats. Drawn cats, cartoons, toys, dogs, other animals, photos of people - the neural network most often ignores.

What is recognition accuracy?
The system is trained to recognize 62 cat breeds from 13,000 photographs. In this version of the application, the accuracy of recognition of cat breeds was 63% on 2 thousand photos from the test sample (not used in training the classifier) and 86% on all available photos. The training database of cat photos is being supplemented and improved, so the number of breeds and the quality of their recognition will increase in new releases.

Goals For Future.
It will be added to supplement the training set of cat photos your examples and thus continuously expand the number of cat breeds and recognition accuracy. The purpose of the project to create an expert system able to recognize the photos all known breeds of cats.