  To understand the real world using various types of data, Artificial
Intelligence (AI) is the most used technique nowadays. While finding the
pattern within the analyzed data represents the main task. This is performed by
extracting representative features step, which is proceeded using the
statistical algorithms or using some specific filters. However, the selection
of useful features from large-scale data represented a crucial challenge. Now,
with the development of convolution neural networks (CNNs), the feature
extraction operation has become more automatic and easier. CNNs allow to work
on large-scale size of data, as well as cover different scenarios for a
specific task. For computer vision tasks, convolutional networks are used to
extract features also for the other parts of a deep learning model. The
selection of a suitable network for feature extraction or the other parts of a
DL model is not random work. So, the implementation of such a model can be
related to the target task as well as the computational complexity of it. Many
networks have been proposed and become the famous networks used for any DL
models in any AI task. These networks are exploited for feature extraction or
at the beginning of any DL model which is named backbones. A backbone is a
known network trained in many other tasks before and demonstrates its
effectiveness. In this paper, an overview of the existing backbones, e.g. VGGs,
ResNets, DenseNet, etc, is given with a detailed description. Also, a couple of
computer vision tasks are discussed by providing a review of each task
regarding the backbones used. In addition, a comparison in terms of performance
is also provided, based on the backbone used for each task.
