  In the realm of practical fine-grained visual classification applications
rooted in deep learning, a common scenario involves training a model using a
pre-existing dataset. Subsequently, a new dataset becomes available, prompting
the desire to make a pivotal decision for achieving enhanced and leveraged
inference performance on both sides: Should one opt to train datasets from
scratch or fine-tune the model trained on the initial dataset using the newly
released dataset? The existing literature reveals a lack of methods to
systematically determine the optimal training strategy, necessitating
explainability. To this end, we present an automatic best-suit training
solution searching framework, the Dual-Carriageway Framework (DCF), to fill
this gap. DCF benefits from the design of a dual-direction search (starting
from the pre-existing or the newly released dataset) where five different
training settings are enforced. In addition, DCF is not only capable of
figuring out the optimal training strategy with the capability of avoiding
overfitting but also yields built-in quantitative and visual explanations
derived from the actual input and weights of the trained model. We validated
DCF's effectiveness through experiments with three convolutional neural
networks (ResNet18, ResNet34 and Inception-v3) on two temporally continued
commercial product datasets. Results showed fine-tuning pathways outperformed
training-from-scratch ones by up to 2.13% and 1.23% on the pre-existing and new
datasets, respectively, in terms of mean accuracy. Furthermore, DCF identified
reflection padding as the superior padding method, enhancing testing accuracy
by 3.72% on average. This framework stands out for its potential to guide the
development of robust and explainable AI solutions in fine-grained visual
classification tasks.
