/PCB_defect_detection

Project Overview PCB Defect Detection using the ResNet50 model is a machine learning project designed to automatically detect defects in printed circuit boards (PCBs). This project utilizes a deep learning model based on the ResNet50 architecture to classify PCB images as either defective or non-defective.

Primary LanguagePython

PCB_defect_detection

Project Overview PCB Defect Detection using the ResNet50 model is a machine learning project designed to automatically detect defects in printed circuit boards (PCBs). This project utilizes a deep learning model based on the ResNet50 architecture to classify PCB images as either defective or non-defective.