Human pose estimation is a crucial task in computer vision, involving the prediction of the spatial arrangement of a person's body from images or videos. The accurate estimation of human poses has numerous applications, including activity recognition, human-computer interaction, and augmented reality. However, pose estimation is challenging due to variations in human body shapes, poses, and environmental conditions.

In this project, we aim to develop a human pose estimation model using deep learning techniques. Our goal is to accurately predict keypoint coordinates corresponding to anatomical landmarks on the human body, such as joints and limbs. By addressing this problem, we aim to contribute to advancements in computer vision and enable applications that require precise understanding of human movements and interactions