Popular GitHub Repositories for Wafer Defects

? 目前最大的工业缺陷检测数据库及论文集 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance.

?SRepair: Powerful LLM-based Program Repairer with $0.029/Fixed Bug

This project aims to automatically detect surface defects in Hot-Rolled Steel Strips such as rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. A CNN is trained on the NEU Metal Surface Defects Database which contains 1800 grayscale images with 300 samples of each of the six different kinds of surface defects.

Run multiple independent anomaly detection (object flaws and motor defects) workloads on a single system via multiple virtual machines using a Kernel-based Virtual Machine (KVM) host.

End to end machine learning project for accurately detecting faults in wafers based on sensor data.

Steel Plate Defect Prediction | Prediction of the probabilities of various defects on steel plates

The code for "Self-supervised Context Learning for Visual Inspection of Industrial Defects"

This project aims to develop an innovative anomaly detection system using advanced data mining and deep learning techniques to accurately identify and localize defects in manufacturing components, thereby enhancing quality control processes and reducing production losses.

Human Facial Skin Defects Dataset

RVEtool for defects is a high-performance modeling package for generating, meshing and solving fiber-reinforced based micromechanical models including the presence of defects. It uses Alya as the structural mechanics solver and executed with PyCOMPSs allows synthetic data generation for AI model deployment.

Self-Supervised Representation Learning of Wafer Maps with FastSiam

Matrix decomposition and multiplication on the Cerebras Wafer-Scale Engine (WSE) architecture

Self-Supervised Representation Learning of Semiconductor Wafer Maps using PyTorch

This is an end to end production ready application built for wafer fault detection with training and prediction pipeline

Problem Statement 1 Prediction Model foe Wehack2.0, trying to find defects.

An end to end machine learning based microservices system which can be used to predict the failure of wafer sensors

This application is used for annotation on images. It was originally created for annotating defects on leather surfaces.

Detecting Defects in Construction Site Images

skInsight is a web application that detects skin defects using computer vision techniques

This project focuses on developing a cutting-edge computer vision program to identify defects on nuts, specifically black marks representing impurities.