Motif-Sikka-ROI: A Curated and Balanced Image Dataset of Traditional Sikka Ikat Weaving Motifs for Classification and Textile Pattern Analysis

Motif-Sikka-ROI is a curated and balanced image dataset documenting 24 classes of traditional ikat weaving motifs from Sikka Regency, East Nusa Tenggara (NTT), Indonesia. The Sikka ikat weaving tradition is a significant part of the cultural identity of the Sikka people, with each motif carrying distinct symbolic and aesthetic meanings that have been preserved and transmitted across generations. Images were collected through direct field photography at multiple locations, including weavers' homes, fabric shops, traditional markets, and cultural exhibitions in Sikka Regency, using smartphones and digital cameras. This multi-device and multi-location acquisition strategy introduces natural variations in illumination, viewpoint, scale, and background conditions, making the dataset representative of real-world image collection scenarios. Each image was processed using the Roboflow platform, where the primary motif area was manually cropped using Region of Interest (ROI) extraction to remove irrelevant background information and focus on the core weaving pattern. Following ROI extraction, all images underwent a strict manual quality assessment procedure. Images exhibiting blur, excessive occlusion, poor visibility, or insufficient motif detail were excluded from the final dataset. The retained images were standardized to a resolution of 224 × 224 pixels and stored in PNG format to preserve image quality without lossy compression. The dataset contains exactly 180 images for each of the 24 motif classes, resulting in a total of 4,320 ROI images. To improve dataset robustness and support research on illumination-invariant pattern recognition, image enhancement, and domain generalization, the dataset is balanced not only at the class level but also across acquisition conditions. For every motif class, the dataset includes 30 images for each of the following six conditions: morning indoor, morning outdoor, noon indoor, noon outdoor, night indoor, and night outdoor. This condition-balanced design minimizes potential bias arising from unequal lighting and environmental distributions. The balanced composition ensures that no single motif class or acquisition condition dominates during model training, making the dataset suitable for fair benchmarking of machine learning and deep learning algorithms. The dataset is published in an unsplit form to provide maximum flexibility for downstream research tasks, including motif classification, feature extraction, image enhancement, computer vision applications, and the digital preservation of Indonesian cultural heritage.