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5. [PDF] Assessment and Estimation of Face Detection Performance Based on ...
11 aug 2020 · In this work, we present a comparison of the speed and the accuracy of three popular face detectors based on deep learning—MTCNN, PyramidBox, ...
6. Best Sports Games 2023! - AllKeyShop.com
GPU: NVIDIA GeForce GTX 1050 Ti, AMD Radeon RX 570; DirectX: 12. OS: Windows ... Nail iconic ollies and kickflips as the Birdman or Rodney Mullen, grind Venice ...
Get ready for some thrilling action and immersive gameplay! Check out our lineup of the best 15 sports games from 2023.
7. [PDF] EASY WAYS TO MAKE YOUR PC RUN FASTER - Vintage Apple
30 aug 2006 · nVidia GeForce 7900 GTX. Sony DVDirect VRO-MCl. 33 DISPLAYS. LaCie 120 ... games; the $79 Turtle Beach Montego. (www.turtlebeach.com) is a ...
8. Enhancement of Multi-Class Structural Defect Recognition Using ... - MDPI
In this study, experiments were implemented using the Keras platform on a workstation with a GPU (GeForce GTX ... Venice, Italy, 22–29 October 2017; pp.
Recently, in the building and infrastructure fields, studies on defect detection methods using deep learning have been widely implemented. For robust automatic recognition of defects in buildings, a sufficiently large training dataset is required for the target defects. However, it is challenging to collect sufficient data from degrading building structures. To address the data shortage and imbalance problem, in this study, a data augmentation method was developed using a generative adversarial network (GAN). To confirm the effect of data augmentation in the defect dataset of old structures, two scenarios were compared and experiments were conducted. As a result, in the models that applied the GAN-based data augmentation experimentally, the average performance increased by approximately 0.16 compared to the model trained using a small dataset. Based on the results of the experiments, the GAN-based data augmentation strategy is expected to be a reliable alternative to complement defect datasets with an unbalanced number of objects.
