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 meaning passed down through generations.
Images were collected through direct field photography at multiple locations, including weavers' homes, fabric shops, and cultural exhibitions in Sikka Regency, using smartphones and digital cameras. This multi-device, multi-location approach introduces natural variation in lighting, angle, and background conditions, making the dataset representative of real-world 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 eliminate irrelevant background and focus on the core weaving pattern. A strict manual filtering process was applied after ROI extraction, retaining only images with sufficient visual clarity and pattern readability. All images were standardized to 224x224 pixels in PNG format— a resolution widely adopted in deep learning pipelines.
The dataset contains exactly 160 images per class across all 24 classes, resulting in a total of 3,840 images. This balanced composition ensures that no single class dominates during model training, making the dataset suitable for the fair benchmarking of classification algorithms. The dataset is published in an unsplit form to maximize flexibility for downstream research tasks, including motif classification, feature extraction, image enhancement, and cultural heritage digitization.