Exploring The Efficiency of Resnet and Densenet in Gender Recognition
DOI:
https://doi.org/10.54097/hset.v38i.5992Keywords:
Gender classification, deep learning, Densenet, Resnet.Abstract
With the development of face recognition and image classification. There are many scenarios where gender classification techniques need to be applied. For example, the stores can recommend different products according to the gender of customers. There are several methods of gender classification, one of them is the convolutional neural network (CNN), which shows its promising potential in image classification. With other relevant variables and parameters being the same, this work tries to compare the Resnet with the Densenet to find a model that is suitable for specific gender classification datasets. The classification efficiency of the model is examined from the three directions of running prediction loss. This paper finds that in the case of small-scale data sets, the classification performanceof the Densenet model ourperforms that of the Resnet model, and the number of parameters used by the Densenet is significantly less than that of the Resnet. When a high-precision classification or recognition model is needed, Densenet is preferred.
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