Hello Sir, The suggestions that you gave me seem to work well for me. The losses are getting reduced. I just want to check if the model will work on my data or not. I want to test it on my data. The training is a bit time consuming. So I want to have the pre-trained weights. I am attaching a sample of the data. Please look over them and reply if the model can work on detecting the text within these images or not, if it works please let me know if you can provide me the pretrained weights or not. Within the images, I want to detect the channel name and number

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On Mon, Oct 22, 2018 at 7:57 PM Christian Bartz ***@***.***> wrote: Hmm, looking at your code I can only say the following: - try to use a lower learning rate like 0.0001 or even 0.00001 - increase your batch size! Will never work, because the network uses BatchNorm. A batch size of 32 should work quite nicely - try to use Adam instead of SGD. Adam converges more quickly. - try to create a similar tool like the BBoxPlotter that I created (you can find it in the insights folder). This tool lets you observe the progress of the training. It does so by using the network to do a prediction on a given image for each iteration of the training. This image is then saved to the hard disk, so you can inspect the state of the network at a given time step. With such a tool you can very quickly determine whether the network diverges or not. This is something you can not directly see from the loss values. So I highly recommend doing this! — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#25 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AfupNNBM9UB97eT1l1tcU59qxfiD0t-tks5undW-gaJpZM4Xv0rX> .

-- Thank and Regards Aditya Rustagi

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