Gemstone Classification using Image Classification

Abstract

This study presents automatic image-based classification of 12 popular gemstones using Convolutional Neural Network. A comprehensive and diverse dataset consisting of 24,000 images was created by the author, with 70% for training, 20% for validation, and 10% for testing, where each gemstone had 1400 images for training, 400 images for validation, and 200 images for testing. Various preprocessing steps were applied, including resizing, renaming, removing duplicate images, data augmentation, and normalization. The CNN model was built by visualizing the training history of a model, specifically the loss and accuracy over epochs. The proposed system achieved an accuracy of 98% on the test set, with high precision and recall values for each class. The results of the study indicate that the proposed methodology is effective in accurately classifying gemstones, and can potentially be extended to other areas of study.