Evaluate the whole setup, not just the model: a practical workflow for video VLMs

1. 1Build a test dataset for video VLMs evaluation
2. 2Run experiments to measure model performance
3. 3Configure metrics to assess the entire setup
4. 4Test the workflow with different models and parameters
5. 5Apply the results to optimize the video VLMs pipeline

Data scientists and AI engineers on a team benefit from this workflow as it helps them to identify and optimize bottlenecks in their video VLMs pipeline

💡 Evaluating the entire setup, not just the model, is crucial for optimal video VLMs performance

📹 Evaluate video VLMs holistically, not just the model! 🚀