New Optimization: Design of a Neural Network Cat and Dog Recognition Prediction Model
Xinwei Zhang, Longqing Zhang, Ziang Song · 2023
With the advent of the big data era, there has been a surge in research focused on the application of convolutional neural networks (CNNs) and image processing. Similar to how humans effortlessly identify cats and dogs in our everyday lives, modern machines are also capable of performing this task. One effective approach to achieving this is through leveraging Kaggle's automatic cat and dog identification technology. This study aims to tackle the challenge of recognizing cats and dogs in images with complex appearances by developing a Sequential object-based CNN model. To accomplish this, the cutting-edge deep learning framework PyTorch is utilized, along with high-performance GPUs for computational power. The network is trained and tested separately on images of dogs and cats. Experimental results demonstrate that the CNN model achieves remarkably high recognition accuracy and exhibits exceptional performance in distinguishing between different breeds of dogs and cats.