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I have data where the input is of size [1,1,625] ( width and height is 1 and number of channels is 625). The input size cannot be changed since I need to use the weights of a pretrained model which was trained using this as the input size. I am new to caffe and hence am not able to convert this data into lmdb format for caffe training. Each cell of input can either be 0 or 1 and the target values for the meantime can be considered to be 1 for all the inputs.
Also how to do it if the input is of size [1,1,2] and both the values in the output can be float.

I am trying to run this code on my own dataset.

Tomarthegod
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1 Answers1

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You can force your data into lmdb format, but I'm afraid this can be quite messy. This github snippet shows how you can use python to write custom lmdb data for caffe.

However, I think that in your case, where you have floating point labels in mind, a better strategy would be to use hdf5 file format for the inputs. This answer shows how this can be done.

Shai
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