Evaluation of Top Pretrained Models Using Transfer Learning on Banknote Dataset with Quality Parameter
Vidula V. Meshram, Kailas R. Patil, Vishal Ambadas Meshram · Ingénierie des systèmes d information · 2023
Building a machine learning (ml) model for fast and accurate banknote classification is an open challenging problem.Image classification problems can be addressed in two ways: by building own model from scratch or by using the transfer learning technique.Building your model from scratch is time-consuming and does not guarantee the best results in the stipulated time.Transfer learning, on the other hand, is a popular technique used by many researchers to deploy ml models in less time with higher accuracy.This paper presents the evaluation of the top five pre-trained convolution neural network (CNN) models.This research aims to evaluate performance and find out the best suitable model from the available list for banknote classification with quality parameters.The model training was done on dataset of Indian banknote which included images from 16 classes, split into 8 classes for clean banknotes and 8 classes of spoilt banknotes.While performing the evaluations, we also consider the performance of models without fine-tuning and after finetuning.