20250129_Fabric_defects
Description
1. Dataset Overview The DME Fabric Defect Detection Dataset is an industrial microscopy image collection designed for research on automated fabric defect detection and classification. The dataset captures a diverse range of real textile defects under controlled acquisition conditions and reflects practical challenges encountered in textile quality inspection environments. The dataset is intended for use in computer vision, deep learning, and industrial inspection research, particularly for evaluating CNN-based and hybrid architectures. Class Distribution Class Label Stain |Damage |Broken Thread |Holes |Non-defective 3. Fabric Types and Visual Diversity Images were collected from multiple fabric categories to ensure diversity and generalization: Plain fabrics Textured fabrics Woven fabrics Printed fabrics Satin fabrics Denim fabrics Defects vary in size, orientation, contrast, and texture, reflecting real industrial variability. 4. Image Resolution and Format Original acquisition resolution: 1920 × 1080 pixels Processed resolution (for model training): 128 × 128 pixels Color format: RGB (3 channels) File format: JPG Downsampling was applied only during model training to ensure computational feasibility, while original high-resolution images are preserved.
Files
Steps to reproduce
This is a subset of fabric defects dataset