Performance Comparison of Object Detection Algorithms with different Feature Extractors

In this work, speed vs accuracy of different Neural Network architectures using alternate feature extractors in the field of Object Detection is being computed, thereby finding the fastest and most accurate architecture out of the lot in order to carry out Object Detection. We made use of three architectures and three extractors to build different combinations of models in order to compute mAP, which is the metric used or commenting upon accuracy. COCO data-set has been used to extract sample images and the work is implemented on TensorFlow library.