The animal recognition project is to recognise the animals using a convolution neural network. Although various machine learning models can classify images of different animals, it remains a challenge to distinguish animal species. This is because there are certain species with a high color similarity. It is a complicated process that requires expertise even for human beings. The CNN models are efficient modern recognition methods.
Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity.
The purpose of this project is to develop a vision-based mobile application to classify endangered animals using an advanced CNN model based on transfer learning. We acquired the images in two ways: collecting them directly from the Zoo and crawling them using the Google search.
Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we convert one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models.
Keywords: Recognition, CNN Model, Animal Recognition, Classification.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
SOFTWARE SPECIFICATIONS
HARDWARE SPECIFICATIONS