This week, we successfully fine-tuned our CLIP model with an 80-20 train-test split, achieving 84% accuracy. Our audio team made progress by implementing grid search capabilities for the audio modality. We delivered our DFX presentation in class and expanded our dataset by annotating additional data for CLIP. We also integrated our best-performing models into the fusion system and successfully produced output.

Next week, we’ll evaluate late fusion performance and conduct grid searches for audio and CLIP models using Hipergator. The audio and CLIP teams will incorporate preprocessing steps into the intermediate fusion model, followed by training and performance reporting. We’re also preparing comprehensive documentation slides detailing each model’s specifications, including input/output shapes, performance metrics, next steps, and dataset information.