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Posted to commits@mxnet.apache.org by jx...@apache.org on 2018/01/03 18:38:20 UTC
[incubator-mxnet] branch master updated: update front-page model
zoo to gluon model zoo (#9286)
This is an automated email from the ASF dual-hosted git repository.
jxie pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 4e3ac33 update front-page model zoo to gluon model zoo (#9286)
4e3ac33 is described below
commit 4e3ac338c84e9b6662f9fa5a40d84c3c39fdef42
Author: Sheng Zha <sz...@users.noreply.github.com>
AuthorDate: Wed Jan 3 10:38:14 2018 -0800
update front-page model zoo to gluon model zoo (#9286)
* update front-page model zoo to gluon model zoo
* Update index.html
---
docs/_static/mxnet-theme/index.html | 6 ++---
docs/api/python/gluon/model_zoo.md | 35 +++++++++++++++++++++++++
python/mxnet/gluon/model_zoo/vision/__init__.py | 4 +--
3 files changed, 40 insertions(+), 5 deletions(-)
diff --git a/docs/_static/mxnet-theme/index.html b/docs/_static/mxnet-theme/index.html
index 96f61de..f3f98f7 100644
--- a/docs/_static/mxnet-theme/index.html
+++ b/docs/_static/mxnet-theme/index.html
@@ -47,10 +47,10 @@
<div class="row">
<div id="model-zoo-blk" class="col-lg-4 col-sm-12">
<span class="glyphicon glyphicon-folder-open"></span>
- <h2>Model Zoo</h2>
- <p>Off the shelf pre-trained models. Fast implementations of many state-of-art models.</p>
+ <h2>Gluon Model Zoo</h2>
+ <p>One-click pre-trained models, included in Gluon. Fast implementations of many state-of-the-art models, for plug-and-play effortless use.</p>
<div class='util-btn'>
- <a id="model-zoo-link" href="model_zoo/index.html">Model zoo</a>
+ <a id="model-zoo-link" href="api/python/gluon/model_zoo.html">Gluon model zoo</a>
</div>
</div>
<div id="example-blk" class="col-lg-4 col-sm-12">
diff --git a/docs/api/python/gluon/model_zoo.md b/docs/api/python/gluon/model_zoo.md
index 8310461..27d2647 100644
--- a/docs/api/python/gluon/model_zoo.md
+++ b/docs/api/python/gluon/model_zoo.md
@@ -28,6 +28,41 @@ In the rest of this document, we list routines provided by the `gluon.model_zoo`
.. 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:
diff --git a/python/mxnet/gluon/model_zoo/vision/__init__.py b/python/mxnet/gluon/model_zoo/vision/__init__.py
index 619711e..5df777f 100644
--- a/python/mxnet/gluon/model_zoo/vision/__init__.py
+++ b/python/mxnet/gluon/model_zoo/vision/__init__.py
@@ -39,8 +39,8 @@ You can construct a model with random weights by calling its constructor:
squeezenet = vision.squeezenet1_0()
densenet = vision.densenet_161()
-We provide pre-trained models for all the models except ResNet V2.
-These can constructed by passing ``pretrained=True``:
+We provide pre-trained models for all the listed models.
+These models can constructed by passing ``pretrained=True``:
.. code::
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