stručni rad
The image preprocessing optimization is a challenging task with numerous applications including classification and object detection on which this paper is oriented the most. Enhancing the performance in terms of processing time for image preprocessing is crucial to every researcher engaged into deep learning. A few common mistakes and practices are presented in this paper which can greatly impact training time, alongside practices and tools used for diagnosing potential pitfalls. In this paper, we evaluate several common Python libraries which are used for image preprocessing and analyze the impact of different augmentation ordering with respect to central processing unit (CPU) usage.
computer vision, deep learning, training optimization
Razvojem virtualizacijskih tehnologija raste kompleksnost IT sustava, dok korisnici istovremeno zahtijevaju brži pristup resursima, napredne integracije i jednostavnost korištenja. Jedno od ključnih rješenja za ove izazove primjena je…
Ovaj rad istražuje i uspoređuje mogućnosti prosljeđivanja grafičkih kartica na Hyper-V i KVM hipervizorima prema virtualnim strojevima, detaljno opisujući metode, postupke i razlike u arhitekturi koje utječu na…
The ever-growing usage and adaptation of HTTP/3 in web traffic leverages Quick UDP Internet Connections (QUIC) as a transport protocol. QUIC is widely recognized for reduced latency, enhanced…