Hello everyone! I search for a more broad explanation of some output. Maybe someone can explain briefly or provide some links to ressources.
If I run the help command python3 train_ssd.py --help
I get a lot of output. Can someone elebarate what these flags mean or what they do?
--balance-data
Balance training data by down-sampling more frequent labels.
--momentum MOMENTUM
Momentum value for optim
--debug-steps DEBUG_STEPS
Set the debug log output frequency.
What does this parameters do? Can you give an example?
--net NET
The network architecture, it can be mb1-ssd, mb1-lite-
ssd, mb2-ssd-lite or vgg16-ssd.
Are the networks preinstalled? If yes, where? If no, where do I get them? Is mb2-ssd-lite-mp-0_686.pth in the models folder the underlying net architecture?
Futhermore.
Let’s observe a single line from the logs of train_ssd.py
Hi @a.marthin, I would recommend referring to the original upstream GitHub repo for train_ssd.py here:
I forked this and added a few tweaks of my own, however I don’t use all of the command-line options myself.
If there is a class that has a lot more data samples than the others, then samples will be randomly skipped from that class as to not bias the dataset. It appears this only applies to the Open Images type of dataset.
Not all of them are tested to be working with ONNX export from PyTorch and ONNX import into TensorRT. This is why I stick with SSD-Mobilenet-v1 version.
Avg Regression Loss is the average error of the bounding box locations.
Avg Classification Loss is the average error of the object classifications.
Together they combine to form the total average loss (detecting the right class of object in the correct location)