Classifying Diverse Material Images based on Transfer Learnin

Transfer learning plays a significant role in classification tasks within the domain of expert systems. It entails applying knowledge acquired from one job or topic to another that is unrelated but nevertheless uses that knowledge. Utilizing pre-trained models and features, transfer learning has been particularly effective in enhancing the performance of classification models. This paper evaluates the impact of pre-training upon a sizeable dataset, investigates adjusting on a more compact target dataset, and assesses accuracy and efficiency improvements. It also explores transfer learning’s potential to address challenges like limited labelled datasets and improve the generalization capabilities of deep neural networks in material classification tasks.