This project classifies galaxies into barred and unbarred types using a VGG16-based neural network, achieving 87% accuracy. Trained on the Galaxy10 SDSS dataset, it demonstrates effective transfer learning for astronomical image classification. Scripts for training, evaluation, and testing with example predictions are included.