Summary
In this chapter, we learned how to use Lightly to efficiently select the most informative frames in videos to improve object detection models using diverse sampling strategies. We also saw how to send these selected frames to the labeling platform Encord, thereby completing an end-to-end use case. Finally, we explored how to further enhance sampling by incorporating an SSL step into the active ML pipeline.
Moving forward, our focus will shift to exploring how to effectively evaluate, monitor, and test the active ML pipeline. This step is essential in ensuring that the pipeline remains robust and reliable throughout its deployment. By implementing comprehensive evaluation strategies, we can assess the performance of the pipeline against predefined metrics and benchmarks. Additionally, continuous monitoring will allow us to identify any potential issues or deviations from expected behavior, enabling us to take proactive measures to maintain optimal performance.
Furthermore...
