TextileClass-7: Dataset for Fabric Material Classification

This dataset presents a comprehensive collection of real-world fabric images developed to support research in computer vision, image classification, and intelligent textile recognition. The dataset consists of seven commonly used fabric categories: Cotton, Cotton Mixed, Denim, Polyester, Silk, Viscose, and Wool. Images were captured under diverse real-world conditions, including variations in illumination, viewing angles, texture patterns, wrinkles, folds, and background complexity, making the dataset suitable for developing robust deep learning models. All images were manually annotated using the Roboflow annotation platform specifically for image classification. Unlike object detection or instance segmentation datasets, no bounding boxes, polygons, or segmentation masks were created. Instead, each image was assigned a single class label corresponding to the dominant fabric type. The annotation process included careful manual verification to ensure label consistency and minimize annotation errors. The dataset is organized into class-specific folders and is compatible with common deep learning frameworks such as TensorFlow, PyTorch, and Keras. It can be readily used for supervised image classification tasks, transfer learning, benchmark evaluations, and comparative studies of machine learning and deep learning algorithms. Researchers may also utilize this dataset for feature extraction, texture analysis, explainable artificial intelligence (XAI), and vision-based textile inspection systems. This dataset provides a valuable benchmark for the textile industry and the research community by enabling the development of automated fabric recognition systems for smart manufacturing, quality inspection, inventory management, e-commerce product categorization, and intelligent textile applications.