| Popis: |
Image classification is a fundamental computer vision task that is essential to many applications, including autonomous driving, object detection, and medical diagnostics. This paper presents a comprehensive study on image classification techniques, focusing on deep learning models. We review and analyze prominent architectures, including AlexNet, VGGNet, ResNet, and evaluate their performance on benchmark datasets such as CIFAR-10. Experimental results demonstrate the effectiveness of these models in achieving high accuracy and robustness in image classification tasks. Furthermore, we delve into the training process, hyperparameter tuning, and regularization techniques to optimize the performance of these models. |
