The presented dataset consists of raw and transformed CWT images of the breathing and heart waveforms obtained from a radar and IP camera setup. The setup is a self-proposed setup with a mmWave radar mounted over an IP camera and can capture the breathing and heart waveforms of the person in the room in front of the system in any orientation. The dataset can be used to estimate the vital signs using any machine learning model and important information about the respiration rate and pulse rate can then be obtained. The dataset is a total of 1280 images- 720 raw and 720 processed. The processed dataset is labeled in six different classes. The first bifurcation is between breath and heart, breath signal is classified as low, normal, and high whereas the heart signal is classified as low, normal, and slightly low. The subject is oriented in differently so that the setup can steer towards the person and capture the breath and heart signals accordingly.