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.
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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.
likith5/PCB_defect_detection
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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.
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