A hybrid deep-learning-architecture for identifying cotton content in fabric materials
Table 6
Ablation study of the proposed architecture. DN = DenseNet121, Swin = Swin Transformer V2, DConv = deformable convolution layer, AFPN = adaptive feature pyramid network, 2nd FC = second fully connected layer. Paired tests were conducted across N = 10 measurements (two independent runs of 5-fold cross-validation). Gain denotes the accuracy difference in percentage points (pp) relative to the full architecture shown in the first row. The p-values result from paired t-tests comparing each configuration to the first-row architecture. Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001.