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</li> <li class="md-nav__item"> <a href="../se-resnet/" class="md-nav__link"> SE-ResNet </a> </li> <li class="md-nav__item"> <a href="../selecsls/" class="md-nav__link"> SelecSLS </a> </li> <li class="md-nav__item"> <a href="../seresnext/" class="md-nav__link"> SE-ResNeXt </a> </li> <li class="md-nav__item"> <a href="../skresnet/" class="md-nav__link"> SK-ResNet </a> </li> <li class="md-nav__item"> <a href="../skresnext/" class="md-nav__link"> SK-ResNeXt </a> </li> <li class="md-nav__item"> <a href="../spnasnet/" class="md-nav__link"> SPNASNet </a> </li> <li class="md-nav__item"> <a href="../ssl-resnet/" class="md-nav__link"> SSL ResNet </a> </li> <li class="md-nav__item"> <a href="../ssl-resnext/" class="md-nav__link"> SSL ResNeXT </a> </li> <li class="md-nav__item md-nav__item--active"> <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc"> <label class="md-nav__link md-nav__link--active" for="__toc"> SWSL ResNet <span class="md-nav__icon md-icon"></span> </label> <a href="./" class="md-nav__link md-nav__link--active"> SWSL ResNet </a> <nav class="md-nav md-nav--secondary" aria-label="Table of contents"> <label class="md-nav__title" for="__toc"> <span class="md-nav__icon md-icon"></span> Table of contents </label> <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix> <li class="md-nav__item"> <a href="#how-do-i-use-this-model-on-an-image" class="md-nav__link"> How do I use this model on an image? </a> </li> <li class="md-nav__item"> <a href="#how-do-i-finetune-this-model" class="md-nav__link"> How do I finetune this model? </a> </li> <li class="md-nav__item"> <a href="#how-do-i-train-this-model" class="md-nav__link"> How do I train this model? </a> </li> <li class="md-nav__item"> <a href="#citation" class="md-nav__link"> Citation </a> </li> </ul> </nav> </li> <li class="md-nav__item"> <a href="../swsl-resnext/" class="md-nav__link"> SWSL ResNeXt </a> </li> <li class="md-nav__item"> <a href="../tf-efficientnet-condconv/" class="md-nav__link"> (Tensorflow) EfficientNet CondConv </a> </li> <li class="md-nav__item"> <a href="../tf-efficientnet-lite/" class="md-nav__link"> (Tensorflow) EfficientNet Lite </a> </li> <li class="md-nav__item"> <a href="../tf-efficientnet/" class="md-nav__link"> (Tensorflow) EfficientNet </a> </li> <li class="md-nav__item"> <a href="../tf-inception-v3/" class="md-nav__link"> (Tensorflow) Inception v3 </a> </li> <li class="md-nav__item"> <a href="../tf-mixnet/" class="md-nav__link"> (Tensorflow) MixNet </a> </li> <li class="md-nav__item"> <a href="../tf-mobilenet-v3/" class="md-nav__link"> (Tensorflow) MobileNet v3 </a> </li> <li class="md-nav__item"> <a href="../tresnet/" class="md-nav__link"> TResNet </a> </li> <li class="md-nav__item"> <a href="../vision-transformer/" class="md-nav__link"> Vision Transformer (ViT) </a> </li> <li class="md-nav__item"> <a href="../wide-resnet/" class="md-nav__link"> Wide ResNet </a> </li> <li 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<label class="md-nav__title" for="__toc"> <span class="md-nav__icon md-icon"></span> Table of contents </label> <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix> <li class="md-nav__item"> <a href="#how-do-i-use-this-model-on-an-image" class="md-nav__link"> How do I use this model on an image? </a> </li> <li class="md-nav__item"> <a href="#how-do-i-finetune-this-model" class="md-nav__link"> How do I finetune this model? </a> </li> <li class="md-nav__item"> <a href="#how-do-i-train-this-model" class="md-nav__link"> How do I train this model? </a> </li> <li class="md-nav__item"> <a href="#citation" class="md-nav__link"> Citation </a> </li> </ul> </nav> </div> </div> </div> <div class="md-content" data-md-component="content"> <article class="md-content__inner md-typeset"> <h1 id="swsl-resnet">SWSL ResNet</h1> <p><strong>Residual Networks</strong>, or <strong>ResNets</strong>, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack <a href="https://paperswithcode.com/method/residual-block">residual blocks</a> ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks. </p> <p>The models in this collection utilise semi-weakly supervised learning to improve the performance of the model. The approach brings important gains to standard architectures for image, video and fine-grained classification. </p> <p>Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.</p> <h2 id="how-do-i-use-this-model-on-an-image">How do I use this model on an image?</h2> <p>To load a pretrained model:</p> <div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">timm</span> <span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">'swsl_resnet18'</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> </code></pre></div> <p>To load and preprocess the image: <div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">urllib</span> <span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span> <span class="kn">from</span> <span class="nn">timm.data</span> <span class="kn">import</span> <span class="n">resolve_data_config</span> <span class="kn">from</span> <span class="nn">timm.data.transforms_factory</span> <span class="kn">import</span> <span class="n">create_transform</span> <span class="n">config</span> <span class="o">=</span> <span class="n">resolve_data_config</span><span class="p">({},</span> <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">)</span> <span class="n">transform</span> <span class="o">=</span> <span class="n">create_transform</span><span class="p">(</span><span class="o">**</span><span class="n">config</span><span class="p">)</span> <span class="n">url</span><span class="p">,</span> <span class="n">filename</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"https://github.com/pytorch/hub/raw/master/images/dog.jpg"</span><span class="p">,</span> <span class="s2">"dog.jpg"</span><span class="p">)</span> <span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">urlretrieve</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span> <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s1">'RGB'</span><span class="p">)</span> <span class="n">tensor</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">img</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># transform and add batch dimension</span> </code></pre></div></p> <p>To get the model predictions: <div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">torch</span> <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span> <span class="n">out</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span> <span class="n">probabilities</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="nb">print</span><span class="p">(</span><span class="n">probabilities</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="c1"># prints: torch.Size([1000])</span> </code></pre></div></p> <p>To get the top-5 predictions class names: <div class="highlight"><pre><span></span><code><span class="c1"># Get imagenet class mappings</span> <span class="n">url</span><span class="p">,</span> <span class="n">filename</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"</span><span class="p">,</span> <span class="s2">"imagenet_classes.txt"</span><span class="p">)</span> <span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">urlretrieve</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span> <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"imagenet_classes.txt"</span><span class="p">,</span> <span class="s2">"r"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> <span class="n">categories</span> <span class="o">=</span> <span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()]</span> <span class="c1"># Print top categories per image</span> <span class="n">top5_prob</span><span class="p">,</span> <span class="n">top5_catid</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">probabilities</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">top5_prob</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)):</span> <span class="nb">print</span><span class="p">(</span><span class="n">categories</span><span class="p">[</span><span class="n">top5_catid</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">top5_prob</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">())</span> <span class="c1"># prints class names and probabilities like:</span> <span class="c1"># [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]</span> </code></pre></div></p> <p>Replace the model name with the variant you want to use, e.g. <code>swsl_resnet18</code>. You can find the IDs in the model summaries at the top of this page.</p> <p>To extract image features with this model, follow the <a href="https://rwightman.github.io/pytorch-image-models/feature_extraction/">timm feature extraction examples</a>, just change the name of the model you want to use.</p> <h2 id="how-do-i-finetune-this-model">How do I finetune this model?</h2> <p>You can finetune any of the pre-trained models just by changing the classifier (the last layer). <div class="highlight"><pre><span></span><code><span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">'swsl_resnet18'</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">NUM_FINETUNE_CLASSES</span><span class="p">)</span> </code></pre></div> To finetune on your own dataset, you have to write a training loop or adapt <a href="https://github.com/rwightman/pytorch-image-models/blob/master/train.py">timm's training script</a> to use your dataset.</p> <h2 id="how-do-i-train-this-model">How do I train this model?</h2> <p>You can follow the <a href="https://rwightman.github.io/pytorch-image-models/scripts/">timm recipe scripts</a> for training a new model afresh.</p> <h2 id="citation">Citation</h2> <div class="highlight"><pre><span></span><code><span class="nc">@article</span><span class="p">{</span><span class="nl">DBLP:journals/corr/abs-1905-00546</span><span class="p">,</span> <span class="w"> </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{I. Zeki Yalniz and</span> <span class="s"> Herv{\'{e}} J{\'{e}}gou and</span> <span class="s"> Kan Chen and</span> <span class="s"> Manohar Paluri and</span> <span class="s"> Dhruv Mahajan}</span><span class="p">,</span> <span class="w"> </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Billion-scale semi-supervised learning for image classification}</span><span class="p">,</span> <span class="w"> </span><span class="na">journal</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{CoRR}</span><span class="p">,</span> <span class="w"> </span><span class="na">volume</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{abs/1905.00546}</span><span class="p">,</span> <span class="w"> </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2019}</span><span class="p">,</span> <span class="w"> </span><span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{http://arxiv.org/abs/1905.00546}</span><span class="p">,</span> <span class="w"> </span><span class="na">archivePrefix</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{arXiv}</span><span class="p">,</span> <span class="w"> </span><span class="na">eprint</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{1905.00546}</span><span class="p">,</span> <span class="w"> </span><span class="na">timestamp</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Mon, 28 Sep 2020 08:19:37 +0200}</span><span class="p">,</span> <span class="w"> </span><span class="na">biburl</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{https://dblp.org/rec/journals/corr/abs-1905-00546.bib}</span><span class="p">,</span> <span class="w"> </span><span class="na">bibsource</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{dblp computer science bibliography, https://dblp.org}</span> <span class="p">}</span> </code></pre></div> <!-- Type: model-index Collections: - Name: SWSL ResNet Paper: Title: Billion-scale semi-supervised learning for image classification URL: https://paperswithcode.com/paper/billion-scale-semi-supervised-learning-for Models: - Name: swsl_resnet18 In Collection: SWSL ResNet Metadata: FLOPs: 2337073152 Parameters: 11690000 File Size: 46811375 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - IG-1B-Targeted - ImageNet Training Resources: 64x GPUs ID: swsl_resnet18 LR: 0.0015 Epochs: 30 Layers: 18 Crop Pct: '0.875' Batch Size: 1536 Image Size: '224' Weight Decay: 0.0001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L954 Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 73.28% Top 5 Accuracy: 91.76% - Name: swsl_resnet50 In Collection: SWSL ResNet Metadata: FLOPs: 5282531328 Parameters: 25560000 File Size: 102480594 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - IG-1B-Targeted - ImageNet Training Resources: 64x GPUs ID: swsl_resnet50 LR: 0.0015 Epochs: 30 Layers: 50 Crop Pct: '0.875' Batch Size: 1536 Image Size: '224' Weight Decay: 0.0001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L965 Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 81.14% Top 5 Accuracy: 95.97% --> </article> </div> </div> </main> <footer class="md-footer"> <div class="md-footer-meta md-typeset"> <div class="md-footer-meta__inner md-grid"> <div class="md-copyright"> Made with <a href="https://squidfunk.github.io/mkdocs-material/" target="_blank" rel="noopener"> Material for MkDocs </a> </div> </div> </div> </footer> </div> <div class="md-dialog" data-md-component="dialog"> <div class="md-dialog__inner md-typeset"></div> </div> <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.12658920.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script> <script src="../../assets/javascripts/bundle.5cf534bf.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/tablesort/5.2.1/tablesort.min.js"></script> <script src="../../javascripts/tables.js"></script> </body> </html>