Image Classification Task

Image classification, the task of assigning predefined labels to images, is a fundamental problem in computer vision with applications ranging from medical diagnosis to autonomous driving. Current research focuses on improving model robustness and efficiency, exploring architectures like Vision Transformers and incorporating techniques such as sharpness-aware minimization, diffusion models for data augmentation, and federated learning for privacy-preserving training. These advancements aim to enhance accuracy, reduce computational costs, and address challenges like handling long sequences, noisy data, and imbalanced datasets, ultimately leading to more reliable and practical image classification systems.