Object detection has application in numerous sectors. It is very crucial in the areas of security and surveillance. The Personal Protective Equipment (PPE) object detection Software can be used to detect people wearing masks in public places. In this paper we used the MobileNet SSD, a TensorFlow model, and Yolo v5, a Pytorch framework, to achieve PPE object identification. Many studies in the Object Detection domain focus only on accuracy. In our study we have also focused on the deployability of the model. We have obtained the 'COVID-19 Personal Protective Equipment (PPE) Detection Dataset' from Kaggle. Furthermore, we have used performance metrics such as mAP, Loss Function and Learning Rate and a comparison between TensorFlow Object Detection API and Yolo v5. These metrics would give us a deeper insight into the models. The classification loss for the Yolo v5 model is significantly lower than that of a TensorFlow model, as can be seen from the observed data. The minimum classification for the Yolo v5 model is 3.719e-3 at 500 epochs, whereas the lowest classification for TensorFlow is 0.0881 at 20250 steps. This indicates that the Yolo v5 model is more effective at categorising data. © 2021 IEEE.