Log trained model used and other essentials during inference

[sfmig]

Usually (in training and evaluation) we do this using MLflow.

However, it may be more complex to integrate MLflow in the inference step than in the other cases. I think it may require to define a dataloader from a video (see #238).

In the meantime, it may be a good idea to implement a simple way of keeping track of basic metrics:

• detector model used,
• tracking config used,
  • in the bash script for an array job in the HPC we save the tracking config along the output data (implemented in Bash script for running inference on clips #251 , but we don't do this when we run the command locally. A "local solution" that extends to the bash script would be ideal.
• ground truth used if available,
• selected outputs (are videos saved, are frames saved),
• anything else?