SqueezeNet model with functional API #14
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added python file and explanations to readme file
added cite info
fchollet
requested changes
Oct 15, 2016
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| - [Original Squeezenet](https://github.com/DeepScale/SqueezeNet) | ||
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| ''' |
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The docstring should come at the top of the file.
| ''' | ||
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| TH_WEIGHTS_PATH = 'PATH/squeezenet_weights_th_dim_ordering_th_kernels.h5' | ||
| TF_WEIGHTS_PATH = 'PATH/squeezenet_weights_tf_dim_ordering_tf_kernels.h5' |
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Where can the weights be downloaded?
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| def SqueezeNet(nb_classes, finetune=False, input_tensor=None, weights=None): | ||
| '''Instantiate the SqueezeNet architecture, | ||
| optionally loading weights pre-trained |
| return x | ||
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| def SqueezeNet(nb_classes, finetune=False, input_tensor=None, weights=None): |
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Shouldn't weights default to "imagenet"?
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In general: please match the same constructor arguments as every other image classification model in the repo / in keras.applications: (include_top=True, weights='imagenet', input_tensor=None)
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Sorry for the delay. I do intend on merging this, I just have limited bandwidth. |
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@rcmalli do you think if it would be a good idea to add also head-less versions of the architecture (see, e.g. ResNet50) so to ease the potential usage in transfer learning? |
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TO-DO
Reference:
Keras Project Reference:
Original Project Reference: