Advancements in Deep Learning Architectures for Image Recognition and Semantic Segmentation

This paper focuses on using Convolutional Neural Networks (CNNs) for tasks such as image classification. It covers both pre-trained models and those that are built from scratch. The paper begins by demonstrating how to utilize the well-known AlexNet model, which is highly effective for image recognition due to transfer learning. It then explains how to load and prepare the MNIST dataset, a common choice for testing image classification methods. Additionally, it introduces a custom CNN designed specifically for recognizing MNIST digits, outlining its architecture, which includes convolutional layers, activation functions, and fully connected layers for capturing handwritten numbers' details. The paper also guides starting the model, running it on sample data, reviewing outputs, and assessing the accuracy of predictions. Furthermore, it delves into training the custom CNN and evaluating its performance by comparing it with established benchmarks, utilizing loss functions and optimization techniques to fine-tune the model and assess its classification accuracy. This work integrates theory with practical application, serving as a comprehensive guide for creating and evaluating CNNs in image classification, with implications for both research and real-world applications in computer vision.