# Evaluating the Reliability of CNN Models on Classifying Traffic and Road Signs using LIME

arXiv: https://arxiv.org/abs/2309.05747 · HTML: https://mlsanity.com/paper/2309.05747/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Cross-Modal Contrastive Learning for Text-to-Image Generation](https://mlsanity.com/paper/2101.04702/) (2021)
- [An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery](https://mlsanity.com/paper/2008.00238/) (2020)
- [DenseNet for Dense Flow](https://mlsanity.com/paper/1707.06316/) (2017)
- [Improved YOLOv5 network for real-time multi-scale traffic sign detection](https://mlsanity.com/paper/2112.08782/) (2021)
- [Cancer image classification based on DenseNet model](https://mlsanity.com/paper/2011.11186/) (2020)
- [Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework](https://mlsanity.com/paper/2110.01017/) (2021)

## Abstract (arXiv)

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.