Wearable Cognitive Assistance (WCA) employs DNNs models to provide real time step-by-step guidance to end-users for specific tasks. Collecting and annotating training data for such DNNs is painful and takes massive human effort and time. In this work, we propose tinyHulk, an automatic annotation tool that reduces human time and effort for these labor-intensive annotation tasks. Further, the tool is equipped with the background replacement, an object-aware data augmentation mechanism to increase the size and diversity of the training set. Our experimental results show that using tinyHulk can save human time up to fourfold compared to using manual methods for labeling tasks. The computer vision models trained with data annotated and generated by tinyHulk achieve high accuracy, outperforming the models trained with real or synthetic data using conventional methods for annotation.