Image Classification: Overfitting and Accuracy Measures

This lecture covers the concepts of overfitting and accuracy measures in image classification, focusing on the trade-off between model complexity and generalization capability. It discusses the importance of avoiding overfitting by tuning free parameters and using techniques like spatial splits and cross-validation. The instructor presents practical examples and best practices to achieve optimal accuracy in image classification projects, emphasizing the significance of model training and evaluation. The lecture also delves into the process of comparing predicted classifications with true labels and interpreting confusion matrices to assess model performance.