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ECA-ResNet
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EfficientNet
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Ensemble Adversarial Inception ResNet v2
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FBNet
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(Gluon) Inception v3
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(Gluon) ResNet
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(Gluon) ResNeXt
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(Gluon) SENet
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(Gluon) SE-ResNeXt
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(Gluon) Xception
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HRNet
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Inception ResNet v2
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(Legacy) SENet
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MixNet
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MnasNet
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MobileNet v2
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MobileNet v3
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Res2Net
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Res2Net
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How do I train this model?
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ResNeSt
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ResNet-D
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ResNet
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ResNeXt
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RexNet
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SE-ResNet
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SelecSLS
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SE-ResNeXt
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SK-ResNet
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SK-ResNeXt
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SPNASNet
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SSL ResNet
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SSL ResNeXT
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SWSL ResNet
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SWSL ResNeXt
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(Tensorflow) EfficientNet CondConv
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(Tensorflow) EfficientNet Lite
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(Tensorflow) EfficientNet
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(Tensorflow) Inception v3
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(Tensorflow) MixNet
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(Tensorflow) MobileNet v3
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TResNet
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<article class="md-content__inner md-typeset">
<h1 id="res2net">Res2Net</h1>
<p><strong>Res2Net</strong> is an image model that employs a variation on bottleneck residual blocks, <a href="https://paperswithcode.com/method/res2net-block">Res2Net Blocks</a>. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.</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;res2net101_26w_4s&#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>res2net101_26w_4s</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;res2net101_26w_4s&#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">Gao_2021</span><span class="p">,</span>
<span class="w"> </span><span class="na">title</span><span class="p">=</span><span class="s">{Res2Net: A New Multi-Scale Backbone Architecture}</span><span class="p">,</span>
<span class="w"> </span><span class="na">volume</span><span class="p">=</span><span class="s">{43}</span><span class="p">,</span>
<span class="w"> </span><span class="na">ISSN</span><span class="p">=</span><span class="s">{1939-3539}</span><span class="p">,</span>
<span class="w"> </span><span class="na">url</span><span class="p">=</span><span class="s">{http://dx.doi.org/10.1109/TPAMI.2019.2938758}</span><span class="p">,</span>
<span class="w"> </span><span class="na">DOI</span><span class="p">=</span><span class="s">{10.1109/tpami.2019.2938758}</span><span class="p">,</span>
<span class="w"> </span><span class="na">number</span><span class="p">=</span><span class="s">{2}</span><span class="p">,</span>
<span class="w"> </span><span class="na">journal</span><span class="p">=</span><span class="s">{IEEE Transactions on Pattern Analysis and Machine Intelligence}</span><span class="p">,</span>
<span class="w"> </span><span class="na">publisher</span><span class="p">=</span><span class="s">{Institute of Electrical and Electronics Engineers (IEEE)}</span><span class="p">,</span>
<span class="w"> </span><span class="na">author</span><span class="p">=</span><span class="s">{Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip}</span><span class="p">,</span>
<span class="w"> </span><span class="na">year</span><span class="p">=</span><span class="s">{2021}</span><span class="p">,</span>
<span class="w"> </span><span class="na">month</span><span class="p">=</span><span class="s">{Feb}</span><span class="p">,</span>
<span class="w"> </span><span class="na">pages</span><span class="p">=</span><span class="s">{652–662}</span>
<span class="p">}</span>
</code></pre></div>
<!--
Type: model-index
Collections:
- Name: Res2Net
Paper:
Title: 'Res2Net: A New Multi-scale Backbone Architecture'
URL: https://paperswithcode.com/paper/res2net-a-new-multi-scale-backbone
Models:
- Name: res2net101_26w_4s
In Collection: Res2Net
Metadata:
FLOPs: 10415881200
Parameters: 45210000
File Size: 181456059
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net101_26w_4s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L152
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net101_26w_4s-02a759a1.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.19%
Top 5 Accuracy: 94.43%
- Name: res2net50_14w_8s
In Collection: Res2Net
Metadata:
FLOPs: 5403546768
Parameters: 25060000
File Size: 100638543
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_14w_8s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L196
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_14w_8s-6527dddc.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.14%
Top 5 Accuracy: 93.86%
- Name: res2net50_26w_4s
In Collection: Res2Net
Metadata:
FLOPs: 5499974064
Parameters: 25700000
File Size: 103110087
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_4s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L141
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_4s-06e79181.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.99%
Top 5 Accuracy: 93.85%
- Name: res2net50_26w_6s
In Collection: Res2Net
Metadata:
FLOPs: 8130156528
Parameters: 37050000
File Size: 148603239
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_6s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L163
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_6s-19041792.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.57%
Top 5 Accuracy: 94.12%
- Name: res2net50_26w_8s
In Collection: Res2Net
Metadata:
FLOPs: 10760338992
Parameters: 48400000
File Size: 194085165
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_8s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L174
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_8s-2c7c9f12.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.19%
Top 5 Accuracy: 94.37%
- Name: res2net50_48w_2s
In Collection: Res2Net
Metadata:
FLOPs: 5375291520
Parameters: 25290000
File Size: 101421406
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_48w_2s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L185
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_48w_2s-afed724a.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.53%
Top 5 Accuracy: 93.56%
-->
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