In my image classification project using TensorFlow, I focused on building a model to accurately classify images into predefined categories. One of the main challenges I faced was dealing with a limited dataset, which could potentially lead to overfitting. To address this, I implemented data augmentation techniques such as rotation, flipping, and scaling to artificially expand the dataset and improve the model's generalization. Another challenge was optimizing the model's performance. I experimented with different architectures and hyperparameters, such as learning rates and batch sizes, to find the optimal configuration. Additionally, I utilized TensorFlow's built-in tools for monitoring training progress and adjusting strategies as needed. These approaches helped me enhance the model's accuracy and robustness, ultimately leading to successful image classification outcomes.