• Punna Vardhan
• Pamulaparthy Bharath Kumar
• Salimeda Rohith
• Dr. L. Kiran Kumar Reddy

This article primarily emphasizes on a cutting-edge project leveraging machine learning and computer vision to provide users with a seamless and accurate means of identifying dog breeds likewise objects and attributes from images. The application employs a robust image recognition model trained on a diverse dataset, ensuring comprehensive coverage of breeds. Users can easily upload or capture images through an intuitive interface, receiving instant identification results alongside detailed information about the recognized breed and features. The application is designed for mobile compatibility, enabling on-the-go breed also object feature identification. Continuous improvement is achieved via user feedback and regular model updates. With a comprehensive database of dog breeds, integration with external APIs for additional information, and a focus on privacy and security, this project aims to deliver a user-friendly, accurate, and informative tool for dog enthusiasts and owners.

Canine breed recognition, Subject recognition, TensorFlow Lite, Visual perception, MobileNet V2 architecture, Ongoing refinement, External interfaces, Extensive repository, Mobile-friendly.

"Dog Breed Identifier Application Using Tensorflow Lite, Flutter ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.c279-c284, April-2024, Available :http://www.jetir.org/papers/JETIR2404235.pdf

"Dog Breed Identifier Application Using Tensorflow Lite, Flutter ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppc279-c284, April-2024, Available at : http://www.jetir.org/papers/JETIR2404235.pdf