Training a Convolutional Neural Network

To develop my skills in deep learning and computer vision, I trained a Convolutional Neural Network to classify eight common traffic signs: Stop, Yield, Do Not Enter, No U-Turn, No Left Turn, No Right Turn, One Way (Left), and One Way (Right).

First, I custom-built a dataset by creating miniature, freestanding traffic signs, and took dozens of pictures of each sign. I then applied OpenCV transformations to each image to artificially enlarge and augment the dataset.

I used the ResNet-50 Neural Network architecture to train the dataset. I chose this architecture experimentally, as it yielded the best accuracies of all the architectures that I tested over numerous runs.

During the initial stages of training, I only achieved accuracies that were at best 60-80% on the validation set. After experimenting with numerous architectures and tuning the hyperparameters, I achieved a 94.04% accuracy on the validation set and a 95.24% accuracy on the testing set.

Learning AI had been on my bucket list for a while, and this project was my first significant experience in AI. I'm really happy with how this project turned out!