Image Datasets

Image datasets are crucial for training and evaluating computer vision models, driving advancements in diverse fields from medical diagnosis to autonomous driving. Current research focuses on addressing dataset limitations, including bias mitigation techniques for fairer models, efficient data reduction methods for sustainability, and innovative approaches to generate synthetic data using generative models like Stable Diffusion and DALL-E to supplement or replace costly and time-consuming manual labeling. These efforts aim to improve model robustness, accuracy, and generalizability, ultimately leading to more reliable and impactful applications across various scientific disciplines and real-world scenarios.