[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127243-en":3,"doc-seo-127243-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127243,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Analysis the Efficiency of Object Detection in Images Using Machine Learning Libraries in Python","This paper analyzes and compares object detection accuracy in images using Python machine learning libraries, focusing on PyTorch and TensorFlow. It trains and tests object detection models based on SSD and Faster R-CNN architectures, then evaluates their effectiveness on the Pascal VOC dataset. Performance is assessed through recall, precision, and mAP to support selection of the most suitable approach for different application needs. The study concludes with summarized findings and practical recommendations for teams working on object detection projects.","JCSI 35 (2025) 202–208 Received: 16 February 2025  \nAccepted: 02 April 2025  \nAnalysis the efficiency of object detection in images using machine learning libraries in Python  \nAnaliza efektywności wykrywania obiektów na obrazach z zastosowaniem bibliotek uczenia maszynowego w języku Python  \nPatryk Kalita*, Marek Miłosz  \nDepartment of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland  \nAbstract  \nThe 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.  \nKeywords: python; machine learning; object detection  \nStreszczenie  \nCelem artykułu jest dokonanie analizy i porównanie dokładności wykrywania obiektów na zdjęciach przy pomocy bibliotek uczenia maszynowego w języku Python, takich jak: PyTorch i Tensorflow. W pracy opisano wykorzystanie obu bibliotek do trenowania i testowania modeli detekcji obiektów, uwzględniając takie architekturyjak SSD i Faster R-CNN. Eksperyment został przeprowadzony na zestawie danych Pascal VOC, aby ocenić skuteczność i wydajność modeli. Wyniki obejmują porównanie takich metryk jak: recall, precision i mAP co pozwala na wybranie najlepszych rozwiązań w zależności od sytuacji. Artykuł kończy się podsumowaniem i końcowymi wnioskami, pozwalającymi dokonać praktycznych rekomendacji dla osób pracujących nad projektami związanymi z detekcją obiektów.  \nSłowa kluczowe: python; uczenie maszynowe; detekcja obiektów  \n*Corresponding author  \n[Email address](Email address: patryk.kalita@pollub.edu.pl)[:](Email address: patryk.kalita@pollub.edu.pl)[ ](Email address: patryk.kalita@pollub.edu.pl)[patryk.kalita@pollub.edu.pl](Email address: patryk.kalita@pollub.edu.pl) (P. Kalita)  \nPublished under Creative Common License (CC BY 4.0 Int.)  \n1. Introduction  \nIn the field of computer vision, one of the key aspects is the detection of objects in images. This has broad applications in issues such as autonomous vehicles or surveillance systems. Thanks to significant advances in machine learning, the development of these fields has become much simpler. Two platforms: PyTorch and Tensorflow have played a key role in the development of deep neural network models in the context of object detection.  \nThe two solutions have many similarities, but each has its own unique characteristics that are critical when choosing a technology for a given problem. PyTorch exhibits a flexible architecture and intuitive approach, and is easy to prototype and debug. Tensorflow, is developed by Google, excels in production support and high scalability, making it popular in the industry.  \nThis paper compares and analyzes the accuracy of object detection in images using models based on PyTorch and Tensorflow libraries. The purpose of this paper is to compare the effectiveness and performance of Faster R-CNN (Faster Region-based Convolutional Neural Network) and SSD (Single Shot MultiBox Detector) algorithms, which are among the most widely used solutions in the aspect of object detection. The  \nadvantages and disadvantages of each model will be presented in the context of implementation, allowing to make the most optimal choice depending on the needs and requirements of the user. The metrics that will be considered are recall, precision and mAP (Mean Average Precision) . 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