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<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">&#39;swsl_resnet18&#39;</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">&quot;https://github.com/pytorch/hub/raw/master/images/dog.jpg&quot;</span><span class="p">,</span> <span class="s2">&quot;dog.jpg&quot;</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">&#39;RGB&#39;</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">&quot;https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;imagenet_classes.txt&quot;</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">&quot;imagenet_classes.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</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"># [(&#39;Samoyed&#39;, 0.6425196528434753), (&#39;Pomeranian&#39;, 0.04062102362513542), (&#39;keeshond&#39;, 0.03186424449086189), (&#39;white wolf&#39;, 0.01739676296710968), (&#39;Eskimo dog&#39;, 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">&#39;swsl_resnet18&#39;</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{\&#39;{e}} J{\&#39;{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%
-->





                
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