Exploring Convolutional Neural Networks for Image Classification and Object Detection

Convolutional Neural Networks or CNNs are one of the newest powerful tools in various tasks of computer vision such as image classification or object detection providing the highest accuracy. This paper also aims to evaluate the efficiency of the CNNs in these areas using a real-world dataset from Kaggle. We discuss general issues and ways to address it, such as data augmentation, dropout and choose the best/settled value of hyperparameters for improvement of the model. This paper aimed at analyzing the effectiveness of CNN in learning discriminative features from the images and confirm that CNNs are among the most accurate models for image classification. Moreover, this study also provides suggestions for subsequent research work, including improving the CNN architectures, employing transfer learning, and incorporating interpretation methods to continue enhancing the performance of CNNs in computer vision.