Faster R-CNN implementation using CUDA architecture in GeForce GTX 10 series / Basyir Adam

Open-source deep learning tools has been distributed
numerously and has gain popularity in the past decades. Training
a large dataset in a deep neural network is a process which
consumes a large amount of time. Recently, the knowledge of deep
learning has been expand with introducing the integration
between neural network and the use of graphical processing unit
(GPU) which was formerly and commonly known to be used with
a central processing unit (CPU). This has been one of the big leap
forward in deep learning as it increases the speed of computing
from weeks to hours. This paper aims to study the various stateof-the-art GPU in deep learning which included Matrix
Laboratory (MATLAB) with Caffe network. The benchmark of
the performance is run on three latest series of GPU platforms as
of year 2017 by implementing Faster Region-based Convolutional
Neural Network (R-CNN) method. Different parameters are
varied to analyze the performance of mean average precision
(mAP) on these different GPU platforms. The best result obtained
in this paper is 60.3% of mAP using the GTX 1080.