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Lightweight version of mxnet neural art implementation

neural-art-mini Lightweight version of mxnet neural art implementation using ~4.8M SqueezeNet model. Compressed model is less than 500KB Sources SqueezeNet: AlexNet-level accuracy with 50x fewer parameters an
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gram_loss.append(mx.sym.sum(mx.sym.square(gvar - gram[i])))

hi!when i running ,there is an error: gram_loss.append(mx.sym.sum(mx.sym.square(gvar - gram[i]))) AttributeError: 'module' object has no attribute 'sum' my mxnet tag is 20160321 and neural-style is ok.

opened Jun 6, 2016 by yuzhuqingyun 1
Closed

Re-impementation in other framework

Hello. For my project I need implementation of neural style with small-size models, like this. Unfortunately, in the project we use other framework.

And now my question is : is there any mxnet-only specific staff in this implementation? Can I rewrite it with other framework (keras\tf\theano) rapidly?

P.s. also can this implementation mix to or more styles?

Thanks and best regards

opened Nov 13, 2016 by gogolgrind 0
Closed

About more information

Can you tell us more information about training time and the result. I think you can update readme for showing more information

opened Apr 2, 2017 by dovanchan 0
Open

About init_image

I think you forget to add a parameter:init_imgae. As I know,the init image is very important for the result; It's useful for choose content,style,or random init_image for init_image So I hope oneday you can update this parameter

opened Apr 2, 2017 by dovanchan 0
Open

Could SqueezeNet_v1.1 make this faster?

Would using the new SqueezeNet be faster without sacrificing quality?

opened Jun 6, 2016 by udf 2
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Category: Python / Deep Learning
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Last update: Jul 31, 2021

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    https://github.com/pavelgonchar/neural-art-mini
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