Object Detection: A Comprehensive Toolkit for Advanced Object Detection
The features and architecture of PaddleDetection are detailed, its effectiveness is illustrated through benchmark comparisons, and real-world applications across different domains are showcased.
Abstract
Object detection is a critical research area in computer vision with applications ranging from autonomous driving to medical imaging. Despite significant advancements in the field, developing high-performance object detection systems remains a hard job to do because of how hard it is to training deep learning models and the need for extensive computational resources. PaddleDetection, a toolkit within PaddlePaddle, addresses these challenges by offering a comprehensive suite of pre-trained models, high-performance training and inference capabilities, and flexible APIs. This toolkit supports various popular architectures for example YOLO, SSD, and Faster R-CNN, enabling users to quickly prototype and deploy the best object detection systems. In addition, PaddleDetection includes tools for data augmentation, hyperparameter tuning, and performance evaluation, making it suitable for both academic and real life applications. This paper details the features and architecture of PaddleDetection, illustrates its effectiveness through benchmark comparisons, and showcases real-world applications across different domains.