Improving Inception and Image Classification in TensorFlow

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glue-grid__col--span-8-lg"> <div class="basic-hero--blog-detail__description"><p>August 31, 2016</p><span class="dot-separator"></span><p>Posted by Alex Alemi, Software Engineer</p></div> </div> <div class="bhoig__cta glue-grid__col--span-8 glue-grid__col--span-7-md glue-grid__col--span-8-lg"> </div> </div> </div> </section> <div class="glue-page"> <div class="glue-grid blog-detail-page__grid"> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <div class="quicklinks-wrapper--mobile"> <div class="block-quick_links"> <section class="quicklinks"> <h2 class="eyebrow">Quick links</h2> <ul class="quicklinks__list"> <li class="quicklinks__item quicklinks__item--share js-quicklinks-share"> <button class="quicklinks__share-button js-quicklinks-share__button" aria-expanded="false" aria-controls="js-quicklinks-share__list"> <span class="icon icon--share"></span> <span class="quicklinks__item__text">Share</span> </button> <section 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competitive networks in the field of image classification. <br /><br />In order to spur even further progress in the field, today we are happy to announce the release of <a href="https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py" target="_blank" rel="noopener noreferrer">Inception-ResNet-v2</a>, a convolutional neural network (CNN) that achieves a new state of the art in terms of accuracy on the <a href="http://image-net.org/challenges/LSVRC/2012/" target="_blank" rel="noopener noreferrer">ILSVRC image classification benchmark</a>. Inception-ResNet-v2 is a variation of our earlier <a href="http://arxiv.org/abs/1512.00567" target="_blank" rel="noopener noreferrer">Inception V3</a> model which borrows some ideas from Microsoft's ResNet papers <a href="https://arxiv.org/abs/1512.03385" target="_blank" rel="noopener noreferrer">[1]</a><a href="https://arxiv.org/abs/1603.05027" target="_blank" rel="noopener noreferrer">[2]</a>. The full details of the model are in our arXiv preprint <a href="http://arxiv.org/abs/1602.07261" target="_blank" rel="noopener noreferrer"><i>Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning</i></a>.<br /><br />Residual connections allow shortcuts in the model and have allowed researchers to successfully train even deeper neural networks, which have lead to even better performance. This has also enabled significant simplification of the Inception blocks. Just compare the model architectures in the figures below:<br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-9KD48z54MBs/V8cVz11fM0I/AAAAAAAABKM/sCC0vVEz_dMOsyb0D8AFwqkrrCavdlkSACLcB/s1600/image02.png" target="_blank" rel="noopener noreferrer"><img border="0" height="238" src="https://2.bp.blogspot.com/-9KD48z54MBs/V8cVz11fM0I/AAAAAAAABKM/sCC0vVEz_dMOsyb0D8AFwqkrrCavdlkSACLcB/s640/image02.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Schematic diagram of Inception V3</td></tr></tbody></table><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-O7AznVGY9js/V8cV_wKKsMI/AAAAAAAABKQ/maO7n2w3dT4Pkcmk7wgGqiSX5FUW2sfZgCLcB/s1600/image00.png" target="_blank" rel="noopener noreferrer"><img border="0" height="402" src="https://1.bp.blogspot.com/-O7AznVGY9js/V8cV_wKKsMI/AAAAAAAABKQ/maO7n2w3dT4Pkcmk7wgGqiSX5FUW2sfZgCLcB/s640/image00.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Schematic diagram of Inception-ResNet-v2</td></tr></tbody></table>At the top of the second Inception-ResNet-v2 figure, you'll see the full network expanded. Notice that this network is considerably deeper than the previous Inception V3. Below in the main figure is an easier to read version of the same network where the repeated residual blocks have been compressed. Here, notice that the inception blocks have been simplified, containing fewer parallel towers than the previous Inception V3.<br /><br />The Inception-ResNet-v2 architecture is more accurate than previous state of the art models, as shown in the table below, which reports the Top-1 and Top-5 validation accuracies on the <a href="http://image-net.org/challenges/LSVRC/2012/" target="_blank" rel="noopener noreferrer">ILSVRC 2012 image classification benchmark</a> based on a single crop of the image. Furthermore, this new model only requires roughly twice the memory and computation compared to Inception V3.<br /><br /><table border="1" bordercolor="#888" cellspacing="0" style="border-collapse: collapse; border-color: rgb(136 , 136 , 136); border-width: 1px; width: 100%;"><tbody><tr> <td><b></b><br /><center><b>Model</b></center></td> <td><div style="text-align: center;"><b>Architecture</b></div></td> <td><b></b><br /><center><b>Checkpoint</b></center></td> <td><b></b><br /><center><b>Top-1 Accuracy</b></center></td> <td><b></b><br /><center><b>Top-5 Accuracy</b></center></td> </tr><tr> <td><div style="text-align: center;"><a href="http://arxiv.org/abs/1602.07261" target="_blank" rel="noopener noreferrer">Inception-ResNet-v2</a></div></td> <td><a href="https://github.com/tensorflow/models/blob/master/slim/nets/inception_resnet_v2.py" target="_blank" rel="noopener noreferrer"></a><br /><center><a href="https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py" target="_blank" rel="noopener noreferrer">Code</a></center></td> <td><div style="text-align: center;"><a href="http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz" target="_blank" rel="noopener noreferrer">inception_resnet_v2_2016_08_30.tar.gz</a></div></td> <td><center>80.4</center></td> <td><center>95.3</center></td> </tr><tr> <td><div style="text-align: center;"><a href="http://arxiv.org/abs/1512.00567" target="_blank" rel="noopener noreferrer">Inception V3</a></div></td> <td><a href="https://github.com/tensorflow/models/blob/master/slim/nets/inception_v3.py" target="_blank" rel="noopener noreferrer"></a><br /><center><a href="https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py" target="_blank" rel="noopener noreferrer">Code</a></center></td> <td><div style="text-align: center;"><a href="http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz" target="_blank" rel="noopener noreferrer">inception_v3_2016_08_28.tar.gz</a></div></td> <td><center>78.0</center></td> <td><center>93.9</center></td> </tr><tr> <td><div style="text-align: center;"><a href="https://arxiv.org/abs/1512.03385" target="_blank" rel="noopener noreferrer">ResNet 152</a></div></td> <td><a href="https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v1.py" target="_blank" rel="noopener noreferrer"></a><br /><center><a href="https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py" target="_blank" rel="noopener noreferrer">Code</a></center></td> <td><div style="text-align: center;"><a href="http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz" target="_blank" rel="noopener noreferrer">resnet_v1_152_2016_08_28.tar.gz</a></div></td> <td><center>76.8</center></td> <td><center>93.2</center></td> </tr><tr> <td><div style="text-align: center;"><a href="http://arxiv.org/abs/1603.05027" target="_blank" rel="noopener noreferrer">ResNet V2 200</a></div></td> <td><a href="https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v2.py" target="_blank" rel="noopener noreferrer"></a><br /><center><a href="https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py" target="_blank" rel="noopener noreferrer">Code</a></center></td> <td><div style="text-align: center;">TBA</div></td> <td><center>79.9*</center></td> <td><center>95.2*</center></td> </tr></tbody> </table><div style="text-align: center;"><span style="font-size: x-small;">(*): Results quoted in ResNet paper.</span></div><br />As an example, while both Inception V3 and Inception-ResNet-v2 models excel at identifying individual dog breeds, the new model does noticeably better. For instance, whereas the old model mistakenly reported Alaskan Malamute for the picture on the right, the new Inception-ResNet-v2 model correctly identifies the dog breeds in both images.<br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-icBtPPVrGF4/V8cXIbbXxaI/AAAAAAAABLA/PTu1-rdmS5A7zn0nFcjA4XmyUpFE3epnwCEw/s1600/performance.png" target="_blank" rel="noopener noreferrer"><img border="0" height="242" src="https://1.bp.blogspot.com/-icBtPPVrGF4/V8cXIbbXxaI/AAAAAAAABLA/PTu1-rdmS5A7zn0nFcjA4XmyUpFE3epnwCEw/s640/performance.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">An <a href="https://en.wikipedia.org/wiki/Alaskan_Malamute" target="_blank" rel="noopener noreferrer">Alaskan Malamute</a> (<a href="https://en.wikipedia.org/wiki/Alaskan_Malamute#/media/File:Alaskan_Malamute.jpg" target="_blank" rel="noopener noreferrer">left</a>) and a <a href="https://en.wikipedia.org/wiki/Siberian_Husky" target="_blank" rel="noopener noreferrer">Siberian Husky</a> (<a href="https://commons.wikimedia.org/wiki/Siberian_Husky#/media/File:Siberian-husky.jpg" target="_blank" rel="noopener noreferrer">right</a>). Images from Wikipedia</td></tr></tbody></table>In order to allow people to immediately begin experimenting, we are also releasing a <a href="http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz" target="_blank" rel="noopener noreferrer">pre-trained instance</a> of the new Inception-ResNet-v2, as part of the <a href="https://github.com/tensorflow/models/blob/master/research/slim/" target="_blank" rel="noopener noreferrer">TF-Slim Image Model Library</a>.<br /><br />We are excited to see what the community does with this improved model, following along as people adapt it and compare its performance on various tasks. Want to get started? See the accompanying <a href="https://github.com/tensorflow/models/blob/master/research/slim/README.md" target="_blank" rel="noopener noreferrer">instructions</a> on how to train, evaluate or fine-tune a network.<br /><br />As always, releasing the code was a team effort. 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Improving Inception and Image Classification in TensorFlow