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Currently, if I were to use Gluon Model Zoo and use any model for eg DenseNet, I face 2 issues regarding APIs
a. Lack of clarity about differences in the APIs (Titlecase vs lowercase)
E.g.
mxnet.gluon.model_zoo.vision.AlexNet(classes=1000, **kwargs)
and
mxnet.gluon.model_zoo.vision.alexnet(pretrained=False, ctx=cpu(0), root='/home/jenkins_slave/.mxnet/models', **kwargs)
b. Even if that were clear, APIs themselves weren't consistent with function definitions for few networks
By inconsistency I mean, **kwargs instead of mentioning all the parameters that are required by the function. (e.g. pretrained=False, ctx=cpu(0))
Case in point - mobile nets, densenets, resnets, squeezenets (note the case sensitivity of these APIs)
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Currently, if I were to use Gluon Model Zoo and use any model for eg DenseNet, I face 2 issues regarding APIs a. Lack of clarity about differences in the APIs (Titlecase vs lowercase) E.g.
mxnet.gluon.model_zoo.vision.AlexNet(classes=1000, **kwargs)
andmxnet.gluon.model_zoo.vision.alexnet(pretrained=False, ctx=cpu(0), root='/home/jenkins_slave/.mxnet/models', **kwargs)
b. Even if that were clear, APIs themselves weren't consistent with function definitions for few networks
By inconsistency I mean,
**kwargs
instead of mentioning all the parameters that are required by the function. (e.g.pretrained=False, ctx=cpu(0)
)Case in point - mobile nets, densenets, resnets, squeezenets (note the case sensitivity of these APIs)
Compared to the likes of alexnet and inceptions