Analysis of Training and Validation Loss in Deep Learning Models

A study examines deep learning methodologies which use Convolutional Neural Networks (CNNs) to recognize chess pieces because these pieces form the basis for automated chess platforms and solutions. The training database consisted of Bishop, King, Knight, Pawn, Queen, and Rook chess pieces. The network established its foundation through a VGG19 architectural design which received reinforcement from data augmentation to boost model generalization capabilities. The strategy for training involved running base model procedures followed by more precise fine-tuned approaches to improve precision levels. The base model attained 92.5% accuracy in training but validation phase accuracy measured at 88.3%. Although the fine-tuned model reached perfect training accuracy its validation accuracy remained at 80% thus requiring additional enhancements for better performance. The confusion matrix provided straightforward classification outcomes because it showed that only certain categories experienced minor classification mistakes thus proving the model worked efficiently. All classes benefited from superior performance according to the precision and recall and F1-score results but Knight and Rook classes in particular received outstanding results. The F1-score for Queen class was lower than other pieces because recall numbers surpassed precision values. The research shows that CNN can detect chess pieces yet further improvements need to happen for better identification accuracy within diverse chess objects.