I’ll 2nd Rob as well,
If both models have suitably sized training datasets, then there is little doubt that the highly specific, separated models will outperform a single combination model - that is, if you can manage to always run the IR model on IR images only, and daytime model on non-IR images only, then of coarse do that. As I doubt that IR image training would help a non-IR image detection (if your daytime dataset is already sufficient). If your dataset was small, they could maybe help one another to combine, but i think this is for a different reason. How much better would they be? I don’t know, but probably not that much. The new deepstack ‘Dark mode’ custom model for person detection seems to be really really good (in the dark), not sure if this is one of the reasons they didn’t just combine it with the main general model (to keep it specific)?
Scheduling the models to run based on time might be fine, but could have it’s own challenges too. For instance, sunrise/sunset changes throughout the year, or camera time keeping drift (if they don’t regularly sync to NTP), etc.
I thought about this as well recently for a model, but ended up combining them purely because i did not think the potential benefit was worth the effort to try to control my variable scheduling for IR. At least you don’t have to re-label your images, just move to separate folders and retrain, so pretty easy if you want to give it a go and tell us.