In addition, when working on specific complex tasks such as estimating the hand pose from an image, it is difficult to acquire the large amounts of data required to train the models. The purpose of data augmentation is to increase the variability of the input images so that the designed object detection model is more robust when applied to images obtained from different environments. Use the teacher model to generate pseudo-labels on unlabeled images Train a student model on the combination of labeled images and pseudo-labeled images.