Sikka-MoDiVers: A Curated Image Dataset of Traditional Sikka Ikat Weaving Motifs for Textile Pattern Analysis

This dataset was created under the hypothesis that image-based computational methods can effectively be used to classify and recognize traditional textile motifs, particularly Sikka ikat weaving patterns. Variations in lighting, device resolution, and motif structure are significant challenges in textile image analysis. By capturing these variations in structured dataset, we hypothesize that it will support the development of more robust and generalizable computer vision models for motif recognition and cultural heritage preservation.
The dataset presents 36 distinct Sikka ikat motif classes, images captured under three natural lighting conditions (morning, noon, and night). The images exhibit a wide range of visual properties, including different resolutions, color intensity, texture complexity, and lighting effects. Among these, 24 classes are fully verified and culturally described by experts, while the remaining 12 are pending further documentation and image collection. The dataset is suitable for training and testing machine learning and deep learning models in classification, retrieval, segmentation, and enhancement tasks.
Image were collected using DSKR cameras Nikon D5100 and various smartphone devices in real-world indoor and outdoor environments across Sikka, East Nusa Tenggara, Indonesia. No artificial lighting or digital enhancement was applied during image acquisition. Each image was manually labelled according to motif type and lighting condition and verified by expert in traditional Sikka ikat weaving. Metadata was compiled for each image, including filename, class label, resolution, capture location, lighting condition, and device used.
The dataset is organized into folders by motif class, with images named according to lighting and capture conditions. A metadata file (metadata_motif.csv) provides technical details per image, while additional files (class_description_motif.csv, motif_class_status.csv) describe the motif meanings and verification status. The data is divided into two parts, sikka_modivers_part1.zip and sikka_modivers_part2.zip. Researchers can us this dataset for developing image classification pipelines, illumination-invariant models, textile retrieval systems, and algorithms ethnographic studies involving traditional Indonesian textiles.