Introduction India’s handloom sector represents the diversity of local culture, which is reflected in the traditional handwoven products from India. Regional fabric varieties such as Bandhani, Banarasi, Kancheepuram, Patola and Tussar are characterized by unique pattern structures, in terms of yarns as well as color arrangements. Accurate identification between them is crucial for tasks such as digital archiving, automatic categorization of fabric and e-commerce. Manually categorizing these designs would be tedious and subjective. Methods Further, the existing conventional deep learning and computer vision approaches encounter significant challenges when processing complex motifs, fine-grained textures and substantial intra-class variability. For this purpose, we propose HybridWeaveNet, a custom deep learning architecture specifically developed to mitigate these shortcomings. The model integrates both a Dual Attention mechanism and pretrained EfficientNetV2 backbone to enhance pattern recognition and variate feature learning. Training of the proposed model is carried out using balanced sampling among the available five-class in the dataset. Further, various augmentation methods, including Mixup, Cutmix, GridDropout, Cutout, Blur, and ColorJitter, have been employed for effective generalization. Results The proposed HybridWeaveNet model is evaluated using various metrics such as accuracy, macro F1-score, Cohen’s kappa, Matthews correlation coefficient (MCC), Jaccard index and log loss and exhibited 91% performance. Discussion Grad-CAM visualizations validated that the model concentrates on culturally related motifs, thereby highlighting its potential for extensive fabric classification.
Shankar et al. (Thu,) studied this question.