PC Component Classification Using Convolutional Neural Network MobileNetV2 with Transfer Learning

This research successfully developed and evaluated a computer vision-based classification system for PC components using the MobileNetV2 Convolutional Neural Network (CNN) architecture with transfer learning. A diverse dataset comprising 14 categories of PC components was collected and processed through stratified data splitting (70% training, 15% validation, 15% testing) and extensive data augmentation to enhance model robustness. The MobileNetV2 model, initialized with ImageNet pre-trained weights and fine-tuned with a custom classification layer, demonstrated effective convergence over 30 epochs. The model achieved a peak validation accuracy of 0.9143 and a final test accuracy of 92.38% with a loss of 0.2890. Performance analysis per class, using a classification report, indicated strong results, with most classes exhibiting precision, recall, and F1-scores above 0.90. Specifically, classes like keyboard, speakers, and webcam achieved perfect recall (1.00). While the confusion matrix revealed some misclassifications, particularly for 'case' and 'hdd' components, the overall performance is highly satisfactory, confirming the feasibility of MobileNetV2 for accurate PC component classification. This study contributes to automating PC component identification in industrial and technical contexts, paving the way for more efficient quality control, inventory management, and diagnostics.