Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Vehicle Detection Approach using Deep Learning Methodologies   The purpose of this study is to successfully train our vehicle detector using
R-CNN, Faster R-CNN deep learning methods on a sample vehicle data sets and to
optimize the success rate of the trained detector by providing efficient
results for vehicle detection by testing the trained vehicle detector on the
test data. The working method consists of six main stages. These are
respectively; loading the data set, the design of the convolutional neural
network, configuration of training options, training of the Faster R-CNN object
detector and evaluation of trained detector. In addition, in the scope of the
study, Faster R-CNN, R-CNN deep learning methods were mentioned and
experimental analysis comparisons were made with the results obtained from
vehicle detection.

13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn