CURE TSR

Abstract

1. Introduction

The overall goal of this project is to analyze the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (>2M images) recognition dataset (CURE-TSR) which is among the most comprehensive dataset with controlled synthetic challenging conditions. Also, this repository contains codes to reproduce the benchmarking result for CNN presented in our NIPS workshop paper. For detailed information, please refer to our paper CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition.

CURE-TSR: Challenging unreal and real environments for traffic sign recognition

Traffic sign images in the CURE-TSR dataset were cropped from the CURE-TSD dataset, which includes around 1.7 million real-world and simulator images with more than 2 million traffic sign instances. Overall, there is around 2.2 million traffic sign images in the CURE-TSR dataset. Sign types include speed limit, goods vehicles, no overtaking, no stopping, no parking, stop, bicycle, hump, no left, no right, priority to, no entry, yield, and parking. To receive the download link, please fill out this form and agree the conditions of use. These information will be kept confidential and will not be released to anybody outside the OLIVES administration team.

2. Objective

CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/olivesgatech/CURE-TSR.git
cd CURE-TSR

1. Training example:
2. Testing example: You need to change the variable 'testdir' to test trained models on different challenging conditions.

usage: train.py [-h] [-j N] [--epochs N] [--start-epoch N] [-b N] [--lr LR]
                [--momentum M] [--weight-decay W] [--print-freq N]
                [--resume PATH] [-e]
                DIR

CURE-TSR Training and Evaluation

positional arguments:
  DIR                   path to dataset

optional arguments:
  -h, --help            show this help message and exit
  -j N, --workers N     number of data loading workers (default: 4)
  --epochs N            number of total epochs to run
  --start-epoch N       manual epoch number (useful on restarts)
  -b N, --batch-size N  mini-batch size (default: 256)
  --lr LR, --learning-rate LR
                        initial learning rate
  --momentum M          momentum
  --weight-decay W, --wd W
                        weight decay (default: 1e-4)
  --print-freq N, -p N  print frequency (default: 10)
  --resume PATH         path to latest checkpoint (default: none)
  -e, --evaluate        evaluate model on validation set

python train.py --lr 0.001 ./CURE-TSR

python train.py -e --resume  ./checkpoints/checkpoint.pth.tar

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by olivesgatech and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.