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How to train with our own dataset ?

What is the format/assumption about how the data is stored if we are training on our own data? Does each class contain its own folder of images in the root data directory?

Hello,
When I try to train my own dataset, after training I try to run the notebook, and at "Visualize the output of the network at any layer" step, I also get:
"ValueError: CorrMM images and kernel must have the same stack size"
I checked my resolution size, and made sure its 50, in all files. But still I get the error.

Hi @pkmital. In Line 2258 of siamese_net.py,
for split in ['train', 'valid', 'test']:
The pairs in the training set seems to be used to measure the performance of the trained model. Is this intentional? Aren't one supposed to not include the training set to evaluate the model? Or is it a special case in Siamese Networks?

Is there any specific reason why the size of the final output layer of the Hani network is set to be 40?

At the notebook, when I am trying to load the model. I keep getting this error:

TypeError: init() got multiple values for keyword argument 'images'

I get this error using the attached mode.pkl, and my trained output.

I tried running the current example using "model.pkl", but the results are always "same person". Also I tried training my own model, and same result.

In the siamese_net.py in the contrastive_loss function I think the definition of the loss is implemented wrong.

The output of the function is the following:
return T.mean(y * d + (1.0 - y) * T.maximum(margin - d, 0.0))
but according to the paper, if you see page 3 (bottom) according to the definition, the output must be:
return T.mean((1.0 - y) * d + y * T.maximum(margin - d, 0.0))

I am getting same label all the pairs with the pre-trained model on LFW. Why is it so?