  The objective of this investigation is to evaluate and contrast the
effectiveness of four state-of-the-art pre-trained models, ResNet-34, VGG-19,
DenseNet-121, and Inception V3, in classifying traffic and road signs with the
utilization of the GTSRB public dataset. The study focuses on evaluating the
accuracy of these models' predictions as well as their ability to employ
appropriate features for image categorization. To gain insights into the
strengths and limitations of the model's predictions, the study employs the
local interpretable model-agnostic explanations (LIME) framework. The findings
of this experiment indicate that LIME is a crucial tool for improving the
interpretability and dependability of machine learning models for image
identification, regardless of the models achieving an f1 score of 0.99 on
classifying traffic and road signs. The conclusion of this study has important
ramifications for how these models are used in practice, as it is crucial to
ensure that model predictions are founded on the pertinent image features.
