Traditional Art and Craft Item Recognition Using Deep Learning

Recognizing and preserving traditional art and craft is essential to save cultural heritage and gain global acceptance. To promote our culture and tradition worldwide, this research uses deep learning methods to classify traditional art and craft products from Bengal. We scraped 1,872 photos from online and divided into nine categories to create the dataset. Our methodology includes 80:20 train-test, data labelling, and image augmentation. Apart from that we trained InceptionResNetV2, Xception and VGG16. The Inception-ResNetV2 outperformed all other models with an accuracy of 95.73%. Model performance was measureable via metrics like confusion matrices, classification report and train & validation accuracy loss curve. The results demonstrate the impressive adaptive capabilities of transfer learning models spotting the items of traditional art and craft using InceptionResNetV2.