Application of Mixed Precision Training in Human Pose Estimation Model Training
Jun Zhu, Jiwei Xu, Lei Feng, Hao Zhang · Processes · 2025
Human pose estimation is an important research direction in the field of computer vision, aiming to detect and locate key points of the human body from images or videos and infer human posture. It plays a significant role in many applications, such as action recognition, motion analysis, virtual reality, and human–computer interaction. As a popular research topic, it is often studied by beginners in deep learning. However, the task of human pose estimation is rather complex, and the mainstream datasets are huge. Even on high-end single-GPU devices, training models requires a considerable amount of time. To help beginners learn efficiently on devices with limited performance, this paper introduces the method of mixed-precision training into the model and combines it with early stopping to reduce the training time. The experimental results show that after introducing mixed-precision training, the training speed of the model was significantly improved and there was no significant decrease in model accuracy.