Maximizing Object Detection Accuracy with FPN: A Comprehensive Overview

FPN (Feature Pyramid Network) is a type of convolutional neural network architecture for object detection tasks. It is designed to improve the performance of object detection models by making use of both high-level and low-level features from the input image. The basic idea behind FPN is to build a pyramid of features, where each level in the pyramid represents a different scale or resolution of the input image. The top of the pyramid represents the high-level, semantically rich features, while the bottom of the pyramid represents the low-level, fine-grained features. By combining features from different levels in the pyramid, the model is able to make use of both the semantically rich high-level features and the fine-grained low-level features to improve the accuracy of object detection.