An Extended Dataset and a Baseline for Pedestrian Attribute Recognition with Advanced Neural Networks

Recent works and challenges on pedestrian attribute recognition demonstrated the necessity to collect extensive and representative datasets and to propose effective and efficient methods based on advanced neural networks. Following on the success of the first edition, the Pedestrian Attribute Recognition (PAR) 2025 Contest, organized within CAIP 2025, is an international competition designed to evaluate advanced neural networks for recognizing pedestrian attributes. Participants are provided with the new Mivia PAR KD Dataset, which includes 106,743 newly annotated images featuring a combination of labels and pseudo-labels obtained through a knowledge distillation method for attributes such as clothing color gender, and the presence or absence of accessories like bags and hats. Competing approaches have been assessed based on the mean accuracy metric, using a separate private test set comprising over 20,000 images, distinct from the training data and not provided to any participants, in order to ensure fairness in the evaluation of results. The goal was to push the participants to effectively leverage the latest advancements in neural network technologies and enhance the scalability and real-world applicability of PAR solutions. The contest teams advanced the state of the art by proposing approaches improving computational efficiency, reducing training time, better addressing class imbalance, and incorporating effective learning procedures and data augmentation strategies. The impressive 95.4% mean accuracy obtained by the winner confirms the achievement of the goal.