• Used a dataset containing 50000+ images of traffic signs.
• Trained a Convolutional Neural Network model to classify between 43 different classes.
• Converted images to grayscale.
• Normalized the images.
• Achieved an accuracy of 92.22%.

Images

Loss/Accuracy Graph

Confusion Matrix

Link to GitHub Repository