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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.