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 <div class="content">
 <div class="section" id="mxnet-model-zoo">
 <span id="mxnet-model-zoo"></span><h1>MXNet Model Zoo<a class="headerlink" href="#mxnet-model-zoo" title="Permalink to this headline">¶</a></h1>
 <p>MXNet features fast implementations of many state-of-the-art models reported in the academic literature. This Model Zoo is an
 ongoing project to collect complete models, with python scripts, pre-trained weights as well as instructions on how to build and fine tune these models.</p>
 <div class="section" id="how-to-contribute-a-pre-trained-model-and-what-to-include">
 <span id="how-to-contribute-a-pre-trained-model-and-what-to-include"></span><h2>How to Contribute a Pre-Trained Model (and what to include)<a class="headerlink" href="#how-to-contribute-a-pre-trained-model-and-what-to-include" title="Permalink to this headline">¶</a></h2>
 <p>The Model Zoo has good entries for CNNs but is seeking content in other areas.</p>
 <p>Issue a Pull Request containing the following:</p>
 <ul class="simple">
 <li>Gist Log</li>
 <li>.json model definition</li>
 <li>Model parameter file</li>
 <li>Readme file (details below)</li>
 </ul>
 <p>Readme file should contain:</p>
 <ul class="simple">
 <li>Model Location, access instructions (wget)</li>
 <li>Confirmation the trained model meets published accuracy from original paper</li>
 <li>Step by step instructions on how to use the trained model</li>
 <li>References to any other applicable docs or arxiv papers the model is based on</li>
 </ul>
 </div>
 <div class="section" id="convolutional-neural-networks-cnns">
 <span id="convolutional-neural-networks-cnns"></span><h2>Convolutional Neural Networks (CNNs)<a class="headerlink" href="#convolutional-neural-networks-cnns" title="Permalink to this headline">¶</a></h2>
 <p>Convolutional neural networks are the state-of-art architecture for many image and video processing problems. Some available datasets include:</p>
 <ul class="simple">
 <li><a class="reference external" href="http://image-net.org/">ImageNet</a>: a large corpus of 1 million natural images, divided into 1000 categories.</li>
 <li><a class="reference external" href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR10</a>: 60,000 natural images (32 x 32 pixels) from 10 categories.</li>
 <li><a class="reference external" href="http://host.robots.ox.ac.uk/pascal/VOC/">PASCAL_VOC</a>: A subset of ImageNet images with object bounding boxes.</li>
 <li><a class="reference external" href="http://crcv.ucf.edu/data/UCF101.php">UCF101</a>: 13,320 videos from 101 action categories.</li>
 <li><a class="reference external" href="http://6.869.csail.mit.edu/fa15/project.html">Mini-Places2</a>: Subset of the Places2 dataset. Includes 100,000 images from 100 scene categories.</li>
 <li>ImageNet 11k</li>
 <li><a class="reference external" href="http://places2.csail.mit.edu/download.html">Places2</a>: There are 1.6 million train images from 365 scene categories in the Places365-Standard, which are used to train the Places365 CNNs. There are 50 images per category in the validation set and 900 images per category in the testing set. Compared to the train set of Places365-Standard, the train set of Places365-Challenge has 6.2 million extra images, leading to totally 8 million train images for the Places365 challenge 2016. The validation set and testing set are the same as the Places365-Standard.</li>
 <li><a class="reference external" href="https://aws.amazon.com/public-datasets/multimedia-commons/">Multimedia Commons</a>: YFCC100M (99.2 million images and 0.8 million videos from Flickr) and supplemental material (pre-extracted features, additional annotations).</li>
 </ul>
 <p>For instructions on using these models, see <a class="reference external" href="http://mxnet.io/tutorials/python/predict_imagenet.html">the python tutorial on using pre-trained ImageNet models</a>.</p>
 <table border="1" class="docutils">
 <colgroup>
 <col width="20%"/>
 <col width="20%"/>
 <col width="20%"/>
 <col width="20%"/>
 <col width="20%"/>
 </colgroup>
 <thead valign="bottom">
 <tr class="row-odd"><th class="head">Model Definition</th>
 <th class="head">Dataset</th>
 <th class="head">Model Weights</th>
 <th class="head">Research Basis</th>
 <th class="head">Contributors</th>
 </tr>
 </thead>
 <tbody valign="top">
 <tr class="row-even"><td><a class="reference external" href="http://data.dmlc.ml/mxnet/models/imagenet/caffenet/caffenet-symbol.json">CaffeNet</a></td>
 <td>ImageNet</td>
 <td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/caffenet/caffenet-0000.params">Param File</a></td>
 <td><a class="reference external" href="http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks">Krizhevsky, 2012</a></td>
 <td>@jspisak</td>
 </tr>
 <tr class="row-odd"><td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/nin/nin-symbol.json">Network in Network (NiN)</a></td>
 <td>ImageNet</td>
 <td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/nin/nin-0000.params">Param File</a></td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1312.4400v3.pdf">Lin et al.., 2014</a></td>
 <td>@jspisak</td>
 </tr>
 <tr class="row-even"><td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/squeezenet/squeezenet_v1.1-symbol.json">SqueezeNet v1.1</a></td>
 <td>ImageNet</td>
 <td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/squeezenet/squeezenet_v1.1-0000.params">Param File</a></td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1602.07360v4.pdf">Iandola et al.., 2016</a></td>
 <td>@jspisak</td>
 </tr>
 <tr class="row-odd"><td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/vgg/vgg16-symbol.json">VGG16</a></td>
 <td>ImageNet</td>
 <td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/vgg/vgg16-0000.params">Param File</a></td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1409.1556v6.pdf">Simonyan et al.., 2015</a></td>
 <td>@jspisak</td>
 </tr>
 <tr class="row-even"><td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/vgg/vgg19-symbol.json">VGG19</a></td>
 <td>ImageNet</td>
 <td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/vgg/vgg19-0000.params">Param File</a></td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1409.1556v6.pdf">Simonyan et al.., 2015</a></td>
 <td>@jspisak</td>
 </tr>
 <tr class="row-odd"><td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/inception-bn/Inception-BN-symbol.json">Inception v3 w/BatchNorm</a></td>
 <td>ImageNet</td>
 <td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/inception-bn/Inception-BN-0126.params">Param File</a></td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1512.00567.pdf">Szegedy et al.., 2015</a></td>
 <td>@jspisak</td>
 </tr>
 <tr class="row-even"><td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/resnet/152-layers/resnet-152-symbol.json">ResidualNet152</a></td>
 <td>ImageNet</td>
 <td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/resnet/152-layers/resnet-152-0000.params">Param File</a></td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1512.03385v1.pdf">He et al.., 2015</a></td>
 <td>@jspisak</td>
 </tr>
 <tr class="row-odd"><td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/resnext/101-layers/resnext-101-64x4d-symbol.json">ResNext101-64x4d</a></td>
 <td>ImageNet</td>
 <td><a class="reference external" href="http://data.dmlc.ml/models/imagenet/resnext/101-layers/resnext-101-64x4d-0000.params">Param File</a></td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1611.05431.pdf">Xie et al.., 2016</a></td>
 <td>@Jerryzcn</td>
 </tr>
 <tr class="row-even"><td>Fast-RCNN</td>
 <td>PASCAL VOC</td>
 <td>[Param File]</td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1504.08083v2.pdf">Girshick, 2015</a></td>
 <td> </td>
 </tr>
 <tr class="row-odd"><td>Faster-RCNN</td>
 <td>PASCAL VOC</td>
 <td>[Param File]</td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1506.01497v3.pdf">Ren et al..,2016</a></td>
 <td> </td>
 </tr>
 <tr class="row-even"><td>Single Shot Detection (SSD)</td>
 <td>PASCAL VOC</td>
 <td>[Param File]</td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1512.02325v4.pdf">Liu et al.., 2016</a></td>
 <td> </td>
 </tr>
 <tr class="row-odd"><td><a class="reference external" href="https://s3.amazonaws.com/mmcommons-tutorial/models/RN101-5k500-symbol.json">LocationNet</a></td>
 <td><a class="reference external" href="https://aws.amazon.com/public-datasets/multimedia-commons/">MultimediaCommons</a></td>
 <td><a class="reference external" href="https://s3.amazonaws.com/mmcommons-tutorial/models/RN101-5k500-0012.params">Param File</a></td>
 <td><a class="reference external" href="https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45488.pdf">Weyand et al.., 2016</a></td>
 <td>@jychoi84 @kevinli7</td>
 </tr>
 </tbody>
 </table>
 </div>
 <div class="section" id="recurrent-neural-networks-rnns-including-lstms">
 <span id="recurrent-neural-networks-rnns-including-lstms"></span><h2>Recurrent Neural Networks (RNNs) including LSTMs<a class="headerlink" href="#recurrent-neural-networks-rnns-including-lstms" title="Permalink to this headline">¶</a></h2>
 <p>MXNet supports many types of recurrent neural networks (RNNs), including Long Short-Term Memory (<a class="reference external" href="http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf">LSTM</a>)
 and Gated Recurrent Units (GRU) networks. Some available datasets include:</p>
 <ul class="simple">
 <li><a class="reference external" href="https://www.cis.upenn.edu/~treebank/">Penn Treebank (PTB)</a>: Text corpus with ~1 million words. Vocabulary is limited to 10,000 words. The task is predicting downstream words/characters.</li>
 <li><a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/">Shakespeare</a>: Complete text from Shakespeare’s works.</li>
 <li><a class="reference external" href="https://s3.amazonaws.com/text-datasets">IMDB reviews</a>: 25,000 movie reviews, labeled as positive or negative</li>
 <li><a class="reference external" href="https://research.facebook.com/researchers/1543934539189348">Facebook bAbI</a>: As a set of 20 question &amp; answer tasks, each with 1,000 training examples.</li>
 <li><a class="reference external" href="http://mscoco.org/">Flickr8k, COCO</a>: Images with associated caption (sentences). Flickr8k consists of 8,092 images captioned by AmazonTurkers with ~40,000 captions. COCO has 328,000 images, each with 5 captions. The COCO images also come with labeled objects using segmentation algorithms.</li>
 </ul>
 <table border="1" class="docutils">
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 <col width="20%"/>
 <col width="20%"/>
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 </colgroup>
 <thead valign="bottom">
 <tr class="row-odd"><th class="head">Model Definition</th>
 <th class="head">Dataset</th>
 <th class="head">Model Weights</th>
 <th class="head">Research Basis</th>
 <th class="head">Contributors</th>
 </tr>
 </thead>
 <tbody valign="top">
 <tr class="row-even"><td>LSTM - Image Captioning</td>
 <td>Flickr8k, MS COCO</td>
 <td> </td>
 <td><a class="reference external" href="https://arxiv.org/pdf/%201411.4555v2.pdf">Vinyals et al.., 2015</a></td>
 <td>@...</td>
 </tr>
 <tr class="row-odd"><td>LSTM - Q&amp;A System</td>
 <td>bAbl</td>
 <td> </td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1502.05698v10.pdf">Weston et al.., 2015</a></td>
 <td> </td>
 </tr>
 <tr class="row-even"><td>LSTM - Sentiment Analysis</td>
 <td>IMDB</td>
 <td> </td>
 <td><a class="reference external" href="http://arxiv.org/pdf/1503.00185v5.pdf">Li et al.., 2015</a></td>
 <td> </td>
 </tr>
 </tbody>
 </table>
 </div>
 <div class="section" id="generative-adversarial-networks-gans">
 <span id="generative-adversarial-networks-gans"></span><h2>Generative Adversarial Networks (GANs)<a class="headerlink" href="#generative-adversarial-networks-gans" title="Permalink to this headline">¶</a></h2>
 <p><a class="reference external" href="http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf">Generative Adversarial Networks</a> train a competing pair of
 neural networks: a generator network which transforms a latent vector into content like an image, and a discriminator
 network that tries to distinguish between generated content and supplied “real” training content.  When properly
 trained the two achieve a <a class="reference external" href="https://en.wikipedia.org/wiki/Nash_equilibrium">Nash equilibrium</a>.</p>
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 <tr class="row-odd"><th class="head">Model Definition</th>
 <th class="head">Dataset</th>
 <th class="head">Model Weights</th>
 <th class="head">Research Basis</th>
 <th class="head">Contributors</th>
 </tr>
 </thead>
 <tbody valign="top">
 <tr class="row-even"><td>DCGANs</td>
 <td>ImageNet</td>
 <td> </td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1511.06434v2.pdf">Radford et al..,2016</a></td>
 <td>@...</td>
 </tr>
 <tr class="row-odd"><td>Text to Image Synthesis</td>
 <td>MS COCO</td>
 <td> </td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1605.05396v2.pdf">Reed et al.., 2016</a></td>
 <td> </td>
 </tr>
 <tr class="row-even"><td>Deep Jazz</td>
 <td> </td>
 <td> </td>
 <td><a class="reference external" href="https://deepjazz.io">Deepjazz.io</a></td>
 <td> </td>
 </tr>
 </tbody>
 </table>
 </div>
 <div class="section" id="other-models">
 <span id="other-models"></span><h2>Other Models<a class="headerlink" href="#other-models" title="Permalink to this headline">¶</a></h2>
 <p>MXNet Supports a variety of model types beyond the canonical CNN and LSTM model types. These include deep reinforcement learning, linear models, etc.. Some available datasets and sources include:</p>
 <ul class="simple">
 <li><a class="reference external" href="https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit">Google News</a>: A text corpus with a vocabulary of 3 million words architected for word2vec.</li>
 <li><a class="reference external" href="http://grouplens.org/datasets/movielens/">MovieLens 20M Dataset</a>: 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users. Includes tag genome data with 12 million relevance scores across 1,100 tags.</li>
 <li><a class="reference external" href="http://stella.sourceforge.net/">Atari Video Game Emulator</a>: Stella is a multi-platform Atari 2600 VCS emulator released under the GNU General Public License (GPL).</li>
 </ul>
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 <tr class="row-odd"><th class="head">Model Definition</th>
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 <th class="head">Model Weights</th>
 <th class="head">Research Basis</th>
 <th class="head">Contributors</th>
 </tr>
 </thead>
 <tbody valign="top">
 <tr class="row-even"><td>Word2Vec</td>
 <td>Google News</td>
 <td> </td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1310.4546v1.pdf">Mikolov et al.., 2013</a></td>
 <td>@...</td>
 </tr>
 <tr class="row-odd"><td>Matrix Factorization</td>
 <td>MovieLens 20M</td>
 <td> </td>
 <td><a class="reference external" href="https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf">Huang et al.., 2013</a></td>
 <td> </td>
 </tr>
 <tr class="row-even"><td>Deep Q-Network</td>
 <td>Atari video games</td>
 <td> </td>
 <td><a class="reference external" href="http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html">Minh et al.., 2015</a></td>
 <td> </td>
 </tr>
 <tr class="row-odd"><td>Asynchronous advantage actor-critic (A3C)</td>
 <td>Atari video games</td>
 <td> </td>
 <td><a class="reference external" href="https://arxiv.org/pdf/1602.01783.pdf">Minh et al.., 2016</a></td>
 <td> </td>
 </tr>
 </tbody>
 </table>
 </div>
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 <h3><a href="../index.html">Table Of Contents</a></h3>
 <ul>
 <li><a class="reference internal" href="#">MXNet Model Zoo</a><ul>
 <li><a class="reference internal" href="#how-to-contribute-a-pre-trained-model-and-what-to-include">How to Contribute a Pre-Trained Model (and what to include)</a></li>
 <li><a class="reference internal" href="#convolutional-neural-networks-cnns">Convolutional Neural Networks (CNNs)</a></li>
 <li><a class="reference internal" href="#recurrent-neural-networks-rnns-including-lstms">Recurrent Neural Networks (RNNs) including LSTMs</a></li>
 <li><a class="reference internal" href="#generative-adversarial-networks-gans">Generative Adversarial Networks (GANs)</a></li>
 <li><a class="reference internal" href="#other-models">Other Models</a></li>
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