  Automatic detection and recognition of banknotes can be a very useful
technology for people with visual difficulties and also for the banks itself by
providing efficient management for handling different paper currencies.
Lightweight models can easily be integrated into any handy IoT based
gadgets/devices. This article presents our experiments on several
state-of-the-art deep learning methods based on Lightweight Convolutional
Neural Network architectures combining with transfer learning. ResNet152v2,
MobileNet, and NASNetMobile were used as the base models with two different
datasets containing Bangladeshi banknote images. The Bangla Currency dataset
has 8000 Bangladeshi banknote images where the Bangla Money dataset consists of
1970 images. The performances of the models were measured using both the
datasets and the combination of the two datasets. In order to achieve maximum
efficiency, we used various augmentations, hyperparameter tuning, and
optimizations techniques. We have achieved maximum test accuracy of 98.88\% on
8000 images dataset using MobileNet, 100\% on the 1970 images dataset using
NASNetMobile, and 97.77\% on the combined dataset (9970 images) using
MobileNet.
