Hi Paulo!



 paraujo:

Hi @KFrank,
its normalization parameters are a bit different due to the quality of the cameras and the weather.


If the difference is primarily one of normalization, you might be able
to normalize one or the other of the two datasets to make them match
better.  But see below.


My expectation is to enhance the performance of the model in the first dataset, but supplementing it with the second dataset.


The important question is what does the real-world data to which you’ll
be applying your model for inference look like?
You have two (potentially competing) goals:  You want to have the data
you train on be as representative as possible of the data on which you
will be running inference.  But you also want to have enough training
data to be able to do a good job of training your model.
If you have more than enough training data, stick to the data that most
closely matches your inference data.  But if you’re short on training data
it can be helpful to add more data, even if it’s not a perfect match for
your real-world data.
Which dataset best matches the normalization of your real-world data?
If both datasets otherwise match reasonably well, I would adjust the
normalization of the dataset whose normalization matches less well.
As a final comment, let’s say that your first dataset is really rather
different than your real-world data (and your second dataset matches
the real-world data reasonably well), but you spent a lot of time training
with it.  Even if it would not make sense to use a model trained on the
first dataset on your real-world data, you can still use it as a pre-trained
model that you “fine tune” by further training it on the second dataset,
potentially saving significant time in comparison to training from scratch
on the second dataset.


I did a few tests keeping the weights as you suggested, and the performance with the first dataset got worse.


Remember, your goal is not narrowly to have the model perform
well on either the first or second dataset, but to have it perform well
on your real-world data.  So you want it to perform well on whatever
test / training data best represents your real-world data.
Best.
K. Frank