5847652
doi
10.35940/ijrte.C4606.099320
oai:zenodo.org:5847652
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Publisher
Saksham Mansotra
B.Tech in Information Technology from GITAM Institute of Technology, Visakhapatnam
Genderpredictions using Convolution Neural Networks
Kurshid Madina
B.Tech in Information Technology from GITAM Institute of Technology, Visakhapatnam
issn:2277-3878
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Computer Vision, Gender Classification, Human computer interaction, Convolution Neural Network (CNN).
<p>Nowadays Deep learning was advanced so much in our daily life. From 2014, there is massive growth in this technology as there is a vast amount of data present. We are even getting better results from whatever we may do. In my work, I have used Convolution Neural Networks as my project depends on image classification. So what I’m trying to do is I’m using two classes in which one class is male and one class is female. I’m classifying both the classes and trying to predict who is male and who is female. For this, I have been using layers like Sequential, Convolution2D, Max-pooling, Flattening, and finally Dense. So, I connect all of these layers. I have been using two more extra layers which are Convolution2D and max-pooling connected as one layer for better classifications. In my model, I’m using Adam optimizer as I’m having only two classes and in my experiments, I found Adam as a good optimizer and I use binary cross entropy as my loss function as I’m using only two classes if we have more than two classes we can use categorical loss function and the images which I use for predictions will be converted into 64*64 matrix form. In the end, I will be getting predictions as 1 for male and 0 for female.</p>
Zenodo
2020-09-30
info:eu-repo/semantics/article
5847651
1642168127.388096
885292
md5:632e20de70b24f3a3e7a9a2a2dadf436
https://zenodo.org/records/5847652/files/C4606099320.pdf
public
2277-3878
Is cited by
issn
International Journal of Recent Technology and Engineering (IJRTE)
9
3
537-540
2020-09-30