Looking at the source code, here it seems that when you are using the function `get_model`

it preloads the set of classes because I am getting this error:

```
In [23]: model = gluoncv.model_zoo.get_model('deeplab_resnet101_ade', nclass=2)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-23-8a1eb9de3344> in <module>()
----> 1 model = gluoncv.model_zoo.get_model('deeplab_resnet101_ade', nclass=2)
/usr/local/lib/python3.5/dist-packages/gluoncv/model_zoo/model_zoo.py in get_model(name, **kwargs)
184 err_str += '%s' % ('\n\t'.join(sorted(_models.keys())))
185 raise ValueError(err_str)
--> 186 net = _models[name](**kwargs)
187 return net
188
/usr/local/lib/python3.5/dist-packages/gluoncv/model_zoo/deeplabv3.py in get_deeplab_resnet101_ade(**kwargs)
294 >>> print(model)
295 """
--> 296 return get_deeplab('ade20k', 'resnet101', **kwargs)
/usr/local/lib/python3.5/dist-packages/gluoncv/model_zoo/deeplabv3.py in get_deeplab(dataset, backbone, pretrained, root, ctx, **kwargs)
175 from ..data import datasets
176 # infer number of classes
--> 177 model = DeepLabV3(datasets[dataset].NUM_CLASS, backbone=backbone, ctx=ctx, **kwargs)
178 if pretrained:
179 from .model_store import get_model_file
TypeError: __init__() got multiple values for argument 'nclass'
```

The parameter that defines the expected classes for this family of models is `nclass`

, as described here and here. So what you can do, is use an alternative definition for the model you want (use nclass = 2 for a binary classification scheme, default functions in mxnet/gluon behave better):

```
model = gluoncv.model_zoo.DeepLabV3(nclass=2,backbone='resnet101',pretrained_base=True)
```

I tested it with pretrained_base = False, then

```
In [20]: model.auxlayer
Out[20]:
_FCNHead(
(block): HybridSequential(
(0): Conv2D(1024 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(fix_gamma=False, eps=1e-05, axis=1, momentum=0.9, use_global_stats=False, in_channels=256)
(2): Activation(relu)
(3): Dropout(p = 0.1, axes=())
(4): Conv2D(256 -> 2, kernel_size=(1, 1), stride=(1, 1))
)
)
```

I don’t know, using this definition, on which dataset this model is trained.

**edit** alternatively, hack the definition of getting the model:

```
In [28]: def get_deeplab(nclass, dataset='pascal_voc', backbone='resnet50', pretrained=False,
...: root='~/.mxnet/models', ctx=mx.cpu(0), **kwargs):
...: r"""DeepLabV3
...: Parameters
...: ----------
...: dataset : str, default pascal_voc
...: The dataset that model pretrained on. (pascal_voc, ade20k)
...: pretrained : bool or str
...: Boolean value controls whether to load the default pretrained weights for model.
...: String value represents the hashtag for a certain version of pretrained weights.
...: ctx : Context, default CPU
...: The context in which to load the pretrained weights.
...: root : str, default '~/.mxnet/models'
...: Location for keeping the model parameters.
...: Examples
...: --------
...: >>> model = get_fcn(dataset='pascal_voc', backbone='resnet50', pretrained=False)
...: >>> print(model)
...: """
...: acronyms = {
...: 'pascal_voc': 'voc',
...: 'pascal_aug': 'voc',
...: 'ade20k': 'ade',
...: 'coco': 'coco',
...: }
...: #from ..data import datasets
...: # infer number of classes
...: model = gluoncv.model_zoo.DeepLabV3(nclass, backbone=backbone, ctx=ctx, **kwargs)
...: if pretrained:
...: from .model_store import get_model_file
...: model.load_parameters(get_model_file('deeplab_%s_%s'%(backbone, acronyms[dataset]),
...: tag=pretrained, root=root), ctx=ctx)
...: return model
...:
In [29]: model = get_deeplab(2,'ade20k', 'resnet101')
In [30]: model.auxlayer
Out[30]:
_FCNHead(
(block): HybridSequential(
(0): Conv2D(1024 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(fix_gamma=False, eps=1e-05, axis=1, momentum=0.9, use_global_stats=False, in_channels=256)
(2): Activation(relu)
(3): Dropout(p = 0.1, axes=())
(4): Conv2D(256 -> 2, kernel_size=(1, 1), stride=(1, 1))
)
)
```