In supervised semantic segmentation, obtaining fully accurate and reliable labels is often challenging and costly. Labels are frequently contaminated by noise due to factors such as limited information, human labeling errors, or encoding mistakes. In the real-world, label noise is ubiquitous and can significantly degrade the performance of trained machine learning models. Therefore, it is essential to develop methods capable of automatically identifying noisy samples prior to training, allowing machine learning models to be optimized based on the cleanest and most reliable data available.

This competition invites you to develop AI algorithms that can accurately rank samples based on their levels of label noise. By effectively ranking the data, one can identify and select the most suitable samples for training machine learning models, thus improving both the efficiency and performance of these models.