Comments (5)
I'm using Pytorch Lightning, so I don't have to write all of the training code with backward()
and other things. In the following article you can see how can Pytorch code be transformed into Pytorch Lightning code.
https://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09
Here is the part about training loop:
https://miro.medium.com/max/700/1*sary1TInxeXMLW-44-_RDg.jpeg
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I see. Thank you for that. I am currently using it for multi-class detection. Is it correct that if i have 19 classes, it has to be labelled 1-19 but num_classes to the predictor should be 20?
from wheat.
Yes, you are correct!
from wheat.
I also noticed that you did not have something like
{'class_name': 'albumentations.augmentations.transforms.Normalize', 'params': {'p': 1.0}},
From PyTorch docs in training FasterRCNN, they seem to get images normalized by those values on transforms.Normalize. Any particular reason why there isnt such on yours? Is it because you already performed this:
image /= 255
from wheat.
Yes, in this repository I did this basic preprocessing as a baseline. Indeed, it would be better to remove this line from the Dataset and add Normalize
from albumentations
to config.
from wheat.
Related Issues (2)
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