This repo contains implementation of deep learning-based steel surface defect segmentation models. Extensive experiments on several deep learning frameworks have been presented with various performance analysis and comparison.

This repo contains implementation of uncertainty estimation, rectification, and minimization for guiding the pseudo-label learning in semi-supervised defect segmentation setting.

Official implementation of "Dynamic Label Injection for Imbalanced Industrial Defect Segmentation" accepted at VISION Workshop - ECCV 2024

This repo contains implementation of semi-supervised defect segmentation based on pairwise similarity map consistency and ensemble-based cross pseudo labels

This repo contains state-of-the-art deep learning models for industrial anomaly detection, defect segmentation, detection, and classification, with other industrial machine vision applications.

Detection and Segmentation in Powder Spreading Process of Magnetic Material Additive Manufacturing

Official implementation of "Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map" accepted at Synthetic Data for Computer Vision Workshop - CVPR 2025

A hybrid CNN–Transformer framework for precise industrial surface defect detection and segmentation, integrating Vision Transformer (ViT) with convolutional modules to effectively capture both local texture details and global contextual features.