I am combining esrgan and crnn to recognize objects with different approach. It is just an experiment. Basically combining i am summing up esrgan generator loss and recognition loss. In this way i am tring to optimize both different architectures at one time.
I am using bunch of different datasets, however my main test dataset is the UFPR-ALPR dataset.
link: http://www.inf.ufpr.br/vri/databases/UFPR-ALPR.zip
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It is just a prototype!
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You should know that dataset class first load all dataset in to ram and starts the process. UFPR-ALPR dataset is a big one and memorizing takes very long time. read -> transform -> assign to preallocated array as an improvement.
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Takes very long time to complete.
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ESRGAN: it has 3 networks.
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Ocr model has a attention and ctc loss based architecture. it has 1 networks.. Output channels order have to change.
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There is 4 different networks have to be trained.
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I don't have resources to continue optimization process much longer. This models are pretty heavy for any king of desktop.
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The main deep learning framework in this repository is Pytorch
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!!! There is not going to frequently update !!!
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I do not provide any support or assistance for the supplied code nor we offer any other compilation/variant of it.
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I assume no responsibility regarding the provided code.