Fine-Tuning Pre-Trained Deep Learning Models for Multiclass Grayscale Images Classification
Published in KINGPC International Conference on Next Generation Computing (ICNGC), 2021
Abstract
Transfer learning significantly improves the performance of a deep learning model on challenging datasets. However, the pre-trained models have certain constraints in terms of their architecture. For example, the state-of-the-art pre-trained models expect an input image with three-color channels because of the wide availability of color images. However, there are certain domains, e.g., medical applications, where grayscale images are produced and the models are required to perform certain tasks on them. Therefore, in this work we propose an approach to run pre-trained models on grayscale images while benefiting from transfer learning for multiclass classification task. We have used the MobileNetV2 pre-trained model to classify the CIFAR datasets. We have compared our results with a conventional method where the grayscale image is stacked up to form a pseudo-color image. Our analysis have shown that the proposed method reduces the computational time per epoch while improves the accuracy of the model.