This is my deep learning project called Cat Breeds Detector Application using Pre-Trained Model CNN (Xception and DenseNet-201). I got the dataset of cats from kaggle, you can click here if you wanted to. Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. While pretrained-model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. You either use the pretrained model as is or use transfer learning to customize this model to a given task.

This application build for research purpose. This research focuses on comparing and analyze the performance of two pre-trained model Xception and DenseNet-201 on classifying cat breeds using Confusion Matrix.

• Python
• Flask
• Javascript
• Google-Colab
• TensorFlow
• Keras: The Python Deep Learning API