Performance Analysis of the Pretrained EfficientDet for Real-time Object Detection on Raspberry Pi

Recently there has been a lot of demand for deep learning models that can operate on a constrained device. When it comes to the task of object detection, EfficientDet is a well-known model. In this study, we use the integer quantization technique to perform real-time object detection on a Raspberry Pi using the popular EfficientDet family. We use the pretrained models from the TensorFlow to perform object detection for a specific task and evaluate their on-device performance. We examined the models’ performance in terms of average precision and recall, IOU, speed, and model size. When working on a Raspberry Pi, we discovered that EfficientDet1 after quantization, with a moving average decay of 0.95 and a Stochastic Gradient Descent optimizer is a good choice.