Hello
I am using DIVE to annotate and analyze video datasets from multiple camera sources (e.g., drones, static surveillance, bodycams) & I am noticing inconsistent classifier confidence scores across different environments even when detecting the same class of object. 
Is there a recommended way to normalize or calibrate confidence values for better cross-video consistency? 
I understand that DIVE & VIAME can be fine-tuned for specific datasets but I would love to hear from others who have addressed similar issues with classifier thresholds / scoring pipelines. Has anyone implemented a post-processing step to align classifier outputs / perhaps used ensemble detection techniques? Checked Home - DIVE guide related to this .
On a related note, while researching model performance metrics; I stumbled upon the concept of what is Perplexity AI, and it made me wonder whether any similar perplexity-style evaluation could be adapted for visual classifiers in DIVE. 
Thank you!!