Traffic Sign Recognition Using Deep Learning

Developed a deep learning-based traffic sign recognition system using transfer learning and pre-trained CNN architectures. The system classifies traffic signs into 52 categories and identifies their corresponding meanings.

Key Contributions:

- Implemented image preprocessing and data preparation.

- Applied transfer learning using ResNet50, VGG16, and InceptionV3.

- Developed model training, evaluation, and inference pipelines.

- Used Python, TensorFlow/Keras, and OpenCV.

The project demonstrates the application of computer vision and deep learning to traffic sign classification.