By leveraging Convolutional Neural Networks (CNN) combined with data augmentation and dropout techniques, the project significantly improved model performance.

The process began with building a comprehensive data augmentation pipeline, incorporating flips, rotations, translations, and zoom layers to enhance the diversity of the training data. Convolutional layers were then constructed, along with max-pooling and dropout layers, to create a robust CNN model. The model was trained and evaluated across multiple configurations to determine the most effective architecture.

Additionally, transfer learning was employed by integrating a pre-trained MobileNetV3 model, which was fine-tuned for the specific task of indoor scene recognition. The comparison of models revealed that the combination of transfer learning with data augmentation and dropout layers provided the best results, achieving a test accuracy of 57%.

This project demonstrates the power of CNNs and transfer learning in improving image classification tasks, particularly in complex scenarios like indoor scene recognition, and highlights the importance of data preprocessing techniques in achieving higher model accuracy.