Analysis the efficiency of object detection in images using machine learning libraries in Python

The purpose of this paper is to analyze and compare the accuracy of object detection in images using Python machine learning libraries such as PyTorch and Tensorflow. The paper describes the use of both libraries to train and test object detection models, considering architectures such as SSD and Faster R-CNN. The experiment was conducted on the Pascal VOC dataset to evaluate the effectiveness and performance of the models. The results include a comparison of metrics such as recall, precision and mAP which allows to choose the best solutions depending on the situation. The article concludes with a summary and final conclusions, allowing practical recommendations to be made for those working on object detection projects.