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# Gluon Model Zoo
```eval_rst
.. currentmodule:: mxnet.gluon.model_zoo
```
## Overview
This document lists the model APIs in Gluon:
```eval_rst
.. autosummary::
:nosignatures:
mxnet.gluon.model_zoo
mxnet.gluon.model_zoo.vision
```
The `Gluon Model Zoo` API, defined in the `gluon.model_zoo` package, provides pre-defined
and pre-trained models to help bootstrap machine learning applications.
In the rest of this document, we list routines provided by the `gluon.model_zoo` package.
### Vision
```eval_rst
.. currentmodule:: mxnet.gluon.model_zoo.vision
.. automodule:: mxnet.gluon.model_zoo.vision
```
The following table summarizes the available models.
| Alias | Network | # Parameters | Top-1 Accuracy | Top-5 Accuracy | Origin |
|---------------|---------------------------------------------------------------------------------------|--------------|----------------|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------|
| alexnet | [AlexNet](https://arxiv.org/abs/1404.5997) | 61,100,840 | 0.5492 | 0.7803 | Converted from pytorch vision |
| densenet121 | [DenseNet-121](https://arxiv.org/pdf/1608.06993.pdf) | 8,062,504 | 0.7497 | 0.9225 | Converted from pytorch vision |
| densenet161 | [DenseNet-161](https://arxiv.org/pdf/1608.06993.pdf) | 28,900,936 | 0.7770 | 0.9380 | Converted from pytorch vision |
| densenet169 | [DenseNet-169](https://arxiv.org/pdf/1608.06993.pdf) | 14,307,880 | 0.7617 | 0.9317 | Converted from pytorch vision |
| densenet201 | [DenseNet-201](https://arxiv.org/pdf/1608.06993.pdf) | 20,242,984 | 0.7732 | 0.9362 | Converted from pytorch vision |
| inceptionv3 | [Inception V3 299x299](http://arxiv.org/abs/1512.00567) | 23,869,000 | 0.7755 | 0.9364 | Converted from pytorch vision |
| mobilenet0.25 | [MobileNet 0.25](https://arxiv.org/abs/1704.04861) | 475,544 | 0.5185 | 0.7608 | Trained with [script](https://github.com/zhreshold/mxnet/blob/2fbfdbcbacff8b738bd9f44e9c8cefc84d6dfbb5/example/gluon/train_imagenet.py) |
| mobilenet0.5 | [MobileNet 0.5](https://arxiv.org/abs/1704.04861) | 1,342,536 | 0.6307 | 0.8475 | Trained with [script](https://github.com/zhreshold/mxnet/blob/2fbfdbcbacff8b738bd9f44e9c8cefc84d6dfbb5/example/gluon/train_imagenet.py) |
| mobilenet0.75 | [MobileNet 0.75](https://arxiv.org/abs/1704.04861) | 2,601,976 | 0.6738 | 0.8782 | Trained with [script](https://github.com/zhreshold/mxnet/blob/2fbfdbcbacff8b738bd9f44e9c8cefc84d6dfbb5/example/gluon/train_imagenet.py) |
| mobilenet1.0 | [MobileNet 1.0](https://arxiv.org/abs/1704.04861) | 4,253,864 | 0.7105 | 0.9006 | Trained with [script](https://github.com/zhreshold/mxnet/blob/2fbfdbcbacff8b738bd9f44e9c8cefc84d6dfbb5/example/gluon/train_imagenet.py) |
| resnet18_v1 | [ResNet-18 V1](http://arxiv.org/abs/1512.03385) | 11,699,112 | 0.6803 | 0.8818 | Converted from pytorch vision |
| resnet34_v1 | [ResNet-34 V1](http://arxiv.org/abs/1512.03385) | 21,814,696 | 0.7202 | 0.9066 | Converted from pytorch vision |
| resnet50_v1 | [ResNet-50 V1](http://arxiv.org/abs/1512.03385) | 25,629,032 | 0.7540 | 0.9266 | Trained with [script](https://github.com/zhreshold/mxnet/blob/2fbfdbcbacff8b738bd9f44e9c8cefc84d6dfbb5/example/gluon/train_imagenet.py) |
| resnet101_v1 | [ResNet-101 V1](http://arxiv.org/abs/1512.03385) | 44,695,144 | 0.7693 | 0.9334 | Trained with [script](https://github.com/zhreshold/mxnet/blob/2fbfdbcbacff8b738bd9f44e9c8cefc84d6dfbb5/example/gluon/train_imagenet.py) |
| resnet152_v1 | [ResNet-152 V1](http://arxiv.org/abs/1512.03385) | 60,404,072 | 0.7727 | 0.9353 | Trained with [script](https://github.com/zhreshold/mxnet/blob/2fbfdbcbacff8b738bd9f44e9c8cefc84d6dfbb5/example/gluon/train_imagenet.py) |
| resnet18_v2 | [ResNet-18 V2](https://arxiv.org/abs/1603.05027) | 11,695,796 | 0.6961 | 0.8901 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/4dcd96ae2f6820e01455079d00f49db1cd21eda9/example/gluon/image_classification.py) |
| resnet34_v2 | [ResNet-34 V2](https://arxiv.org/abs/1603.05027) | 21,811,380 | 0.7324 | 0.9125 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/4dcd96ae2f6820e01455079d00f49db1cd21eda9/example/gluon/image_classification.py) |
| resnet50_v2 | [ResNet-50 V2](https://arxiv.org/abs/1603.05027) | 25,595,060 | 0.7622 | 0.9297 | Trained with [script](https://github.com/zhreshold/mxnet/blob/2fbfdbcbacff8b738bd9f44e9c8cefc84d6dfbb5/example/gluon/train_imagenet.py) |
| resnet101_v2 | [ResNet-101 V2](https://arxiv.org/abs/1603.05027) | 44,639,412 | 0.7747 | 0.9375 | Trained with [script](https://github.com/zhreshold/mxnet/blob/2fbfdbcbacff8b738bd9f44e9c8cefc84d6dfbb5/example/gluon/train_imagenet.py) |
| resnet152_v2 | [ResNet-152 V2](https://arxiv.org/abs/1603.05027) | 60,329,140 | 0.7833 | 0.9409 | Trained with [script](https://github.com/zhreshold/mxnet/blob/2fbfdbcbacff8b738bd9f44e9c8cefc84d6dfbb5/example/gluon/train_imagenet.py) |
| squeezenet1.0 | [SqueezeNet 1.0](https://arxiv.org/abs/1602.07360) | 1,248,424 | 0.5611 | 0.7909 | Converted from pytorch vision |
| squeezenet1.1 | [SqueezeNet 1.1](https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1) | 1,235,496 | 0.5496 | 0.7817 | Converted from pytorch vision |
| vgg11 | [VGG-11](https://arxiv.org/abs/1409.1556) | 132,863,336 | 0.6662 | 0.8734 | Converted from pytorch vision |
| vgg13 | [VGG-13](https://arxiv.org/abs/1409.1556) | 133,047,848 | 0.6774 | 0.8811 | Converted from pytorch vision |
| vgg16 | [VGG-16](https://arxiv.org/abs/1409.1556) | 138,357,544 | 0.6986 | 0.8945 | Converted from pytorch vision |
| vgg19 | [VGG-19](https://arxiv.org/abs/1409.1556) | 143,667,240 | 0.7072 | 0.8988 | Converted from pytorch vision |
| vgg11_bn | [VGG-11 with batch normalization](https://arxiv.org/abs/1409.1556) | 132,874,344 | 0.6859 | 0.8872 | Converted from pytorch vision |
| vgg13_bn | [VGG-13 with batch normalization](https://arxiv.org/abs/1409.1556) | 133,059,624 | 0.6884 | 0.8882 | Converted from pytorch vision |
| vgg16_bn | [VGG-16 with batch normalization](https://arxiv.org/abs/1409.1556) | 138,374,440 | 0.7142 | 0.9043 | Converted from pytorch vision |
| vgg19_bn | [VGG-19 with batch normalization](https://arxiv.org/abs/1409.1556) | 143,689,256 | 0.7241 | 0.9093 | Converted from pytorch vision |
```eval_rst
.. autosummary::
:nosignatures:
get_model
```
#### ResNet
```eval_rst
.. autosummary::
:nosignatures:
resnet18_v1
resnet34_v1
resnet50_v1
resnet101_v1
resnet152_v1
resnet18_v2
resnet34_v2
resnet50_v2
resnet101_v2
resnet152_v2
```
```eval_rst
.. autosummary::
:nosignatures:
ResNetV1
ResNetV2
BasicBlockV1
BasicBlockV2
BottleneckV1
BottleneckV2
get_resnet
```
#### VGG
```eval_rst
.. autosummary::
:nosignatures:
vgg11
vgg13
vgg16
vgg19
vgg11_bn
vgg13_bn
vgg16_bn
vgg19_bn
```
```eval_rst
.. autosummary::
:nosignatures:
VGG
get_vgg
```
#### Alexnet
```eval_rst
.. autosummary::
:nosignatures:
alexnet
```
```eval_rst
.. autosummary::
:nosignatures:
AlexNet
```
#### DenseNet
```eval_rst
.. autosummary::
:nosignatures:
densenet121
densenet161
densenet169
densenet201
```
```eval_rst
.. autosummary::
:nosignatures:
DenseNet
```
#### SqueezeNet
```eval_rst
.. autosummary::
:nosignatures:
squeezenet1_0
squeezenet1_1
```
```eval_rst
.. autosummary::
:nosignatures:
SqueezeNet
```
#### Inception
```eval_rst
.. autosummary::
:nosignatures:
inception_v3
```
```eval_rst
.. autosummary::
:nosignatures:
Inception3
```
#### MobileNet
```eval_rst
.. autosummary::
:nosignatures:
mobilenet1_0
mobilenet0_75
mobilenet0_5
mobilenet0_25
```
```eval_rst
.. autosummary::
:nosignatures:
MobileNet
```
## API Reference
<script type="text/javascript" src='../../../_static/js/auto_module_index.js'></script>
```eval_rst
.. automodule:: mxnet.gluon.model_zoo
.. automodule:: mxnet.gluon.model_zoo.vision
:members:
:imported-members:
```
<script>auto_index("api-reference");</script>