Cat Breed Classification Techniques

Nirafika Binti Mohta presents her final year project on a cat breed classification system using CNN. The project aims to identify cat breeds, design a CNN architecture, and develop a web-based system. The scope includes classifying five cat breeds and is accessible via websites. The project reviews literature on CNN applications and follows a methodology with phases like requirement, design, and testing. The medium CNN model achieved the best accuracy, and the system can classify breeds like Siamese and Bengal with high accuracy.

What is the main technique used in the project for classifying cat breeds?

• Convolutional Neural Network

• K-Nearest Neighbors

• Decision Tree

• Support Vector Machine

How many cat breeds can the system classify as per the project's current scope?

• Twelve

• Three

• Five

• Ten

What is one of the benefits of classifying cat breeds mentioned in the project?

• Reducing cat's weight

• Improving cat's diet

• Understanding cat's behavior and personality

• Increasing cat's lifespan

Which CNN model achieved the highest accuracy in the snake species classification research?

• Shallow model

• Medium model

• Deep model

• None of the above

What is the first phase in the project's methodology?

• Evaluation

• Requirement

• Development

• Design

How many images of cats were used in the dataset after preprocessing?

• 1000

• 2371

• 1200

• 5000

Which tool was used for preprocessing the images in the project?

• Scikit-learn

• OpenCV

• Pandas

• MATLAB

What platform was used to run the web application for the cat breed classification system?

• AWS

• Google Cloud

• Heroku

• Azure

Which model was chosen for the final cat breed classification system?

• None of the above

• Medium model

• Shallow model

• Deep model

What was the best accuracy achieved by the system for classifying Bengal cats?

• 90%

• 85%

• 95%

• 100%