A Novel Image Classification Approach via Dense-MobileNet Models

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Attribute</td><td class="align_center">Configuration information</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">OS</td><td class="align_center">Ubuntu 14.04.5 LTS</td></tr><tr><td class="align_left">CPU</td><td class="align_center">Intel® Xeon® CPU E5-2670 v3 @ 2.30 GHz</td></tr><tr><td class="align_left">GPU</td><td class="align_center">Nvidia GeForce GTX TITAN X</td></tr><tr><td class="align_left">CuDNN</td><td class="align_center">CuDNN 6.0.21</td></tr><tr><td class="align_left">CUDA</td><td class="align_center">CUDA 18.0.61</td></tr><tr><td class="align_left">Framework</td><td class="align_center">PyTorch</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Experimental settings on children’s colonoscopy polyp data set.</div>

Mobile Information Systems

tab4

Table 4

Table 4: A Novel Image Classification Approach via Dense-MobileNet Models 