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Cross-Stage Partial Networks [cspnet.py]
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DenseNet [densenet.py]
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DLA [dla.py]
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Dual-Path Networks [dpn.py]
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Inception-V4 [inception_v4.py]
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PNasNet-5 [pnasnet.py]
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MobileNet-V3 [mobilenetv3.py]
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RegNet [regnet.py]
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ResNet, ResNeXt [resnet.py]
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Res2Net [res2net.py]
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ResNeSt [resnest.py]
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ReXNet [rexnet.py]
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DLA [dla.py]
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Dual-Path Networks [dpn.py]
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HRNet [hrnet.py]
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Inception-V4 [inception_v4.py]
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PNasNet-5 [pnasnet.py]
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EfficientNet [efficientnet.py]
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MobileNet-V3 [mobilenetv3.py]
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RegNet [regnet.py]
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ResNet, ResNeXt [resnet.py]
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Res2Net [res2net.py]
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ResNeSt [resnest.py]
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ReXNet [rexnet.py]
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Selective-Kernel Networks [sknet.py]
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SelecSLS [selecsls.py]
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Squeeze-and-Excitation Networks [senet.py]
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TResNet [tresnet.py]
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VovNet V2 and V1 [vovnet.py]
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Xception [xception.py]
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Xception (Modified Aligned, Gluon) [gluon_xception.py]
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Xception (Modified Aligned, TF) [aligned_xception.py]
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<h1 id="model-architectures">Model Architectures</h1>
<p>The model architectures included come from a wide variety of sources. Sources, including papers, original impl ("reference code") that I rewrote / adapted, and PyTorch impl that I leveraged directly ("code") are listed below.</p>
<p>Most included models have pretrained weights. The weights are either:
1. from their original sources
2. ported by myself from their original impl in a different framework (e.g. Tensorflow models)
3. trained from scratch using the included training script</p>
<p>The validation results for the pretrained weights can be found <a href="../results/">here</a></p>
<h2 id="cross-stage-partial-networks-cspnetpy">Cross-Stage Partial Networks [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/cspnet.py">cspnet.py</a>]</h2>
<ul>
<li>Paper: <code>CSPNet: A New Backbone that can Enhance Learning Capability of CNN</code> - <a href="https://arxiv.org/abs/1911.11929">https://arxiv.org/abs/1911.11929</a></li>
<li>Reference impl: <a href="https://github.com/WongKinYiu/CrossStagePartialNetworks">https://github.com/WongKinYiu/CrossStagePartialNetworks</a></li>
</ul>
<h2 id="densenet-densenetpy">DenseNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/densenet.py">densenet.py</a>]</h2>
<ul>
<li>Paper: <code>Densely Connected Convolutional Networks</code> - <a href="https://arxiv.org/abs/1608.06993">https://arxiv.org/abs/1608.06993</a></li>
<li>Code: <a href="https://github.com/pytorch/vision/tree/master/torchvision/models">https://github.com/pytorch/vision/tree/master/torchvision/models</a></li>
</ul>
<h2 id="dla-dlapy">DLA [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dla.py">dla.py</a>]</h2>
<ul>
<li>Paper: <a href="https://arxiv.org/abs/1707.06484">https://arxiv.org/abs/1707.06484</a></li>
<li>Code: <a href="https://github.com/ucbdrive/dla">https://github.com/ucbdrive/dla</a></li>
</ul>
<h2 id="dual-path-networks-dpnpy">Dual-Path Networks [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dpn.py">dpn.py</a>]</h2>
<ul>
<li>Paper: <code>Dual Path Networks</code> - <a href="https://arxiv.org/abs/1707.01629">https://arxiv.org/abs/1707.01629</a></li>
<li>My PyTorch code: <a href="https://github.com/rwightman/pytorch-dpn-pretrained">https://github.com/rwightman/pytorch-dpn-pretrained</a></li>
<li>Reference code: <a href="https://github.com/cypw/DPNs">https://github.com/cypw/DPNs</a></li>
</ul>
<h2 id="hrnet-hrnetpy">HRNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/hrnet.py">hrnet.py</a>]</h2>
<ul>
<li>Paper: <code>Deep High-Resolution Representation Learning for Visual Recognition</code> - <a href="https://arxiv.org/abs/1908.07919">https://arxiv.org/abs/1908.07919</a></li>
<li>Code: <a href="https://github.com/HRNet/HRNet-Image-Classification">https://github.com/HRNet/HRNet-Image-Classification</a></li>
</ul>
<h2 id="inception-v3-inception_v3py">Inception-V3 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v3.py">inception_v3.py</a>]</h2>
<ul>
<li>Paper: <code>Rethinking the Inception Architecture for Computer Vision</code> - <a href="https://arxiv.org/abs/1512.00567">https://arxiv.org/abs/1512.00567</a></li>
<li>Code: <a href="https://github.com/pytorch/vision/tree/master/torchvision/models">https://github.com/pytorch/vision/tree/master/torchvision/models</a></li>
</ul>
<h2 id="inception-v4-inception_v4py">Inception-V4 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v4.py">inception_v4.py</a>]</h2>
<ul>
<li>Paper: <code>Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning</code> - <a href="https://arxiv.org/abs/1602.07261">https://arxiv.org/abs/1602.07261</a></li>
<li>Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/slim/nets">https://github.com/tensorflow/models/tree/master/research/slim/nets</a></li>
</ul>
<h2 id="inception-resnet-v2-inception_resnet_v2py">Inception-ResNet-V2 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_resnet_v2.py">inception_resnet_v2.py</a>]</h2>
<ul>
<li>Paper: <code>Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning</code> - <a href="https://arxiv.org/abs/1602.07261">https://arxiv.org/abs/1602.07261</a></li>
<li>Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/slim/nets">https://github.com/tensorflow/models/tree/master/research/slim/nets</a></li>
</ul>
<h2 id="nasnet-a-nasnetpy">NASNet-A [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/nasnet.py">nasnet.py</a>]</h2>
<ul>
<li>Papers: <code>Learning Transferable Architectures for Scalable Image Recognition</code> - <a href="https://arxiv.org/abs/1707.07012">https://arxiv.org/abs/1707.07012</a></li>
<li>Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet">https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet</a></li>
</ul>
<h2 id="pnasnet-5-pnasnetpy">PNasNet-5 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/pnasnet.py">pnasnet.py</a>]</h2>
<ul>
<li>Papers: <code>Progressive Neural Architecture Search</code> - <a href="https://arxiv.org/abs/1712.00559">https://arxiv.org/abs/1712.00559</a></li>
<li>Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet">https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet</a></li>
</ul>
<h2 id="efficientnet-efficientnetpy">EfficientNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py">efficientnet.py</a>]</h2>
<ul>
<li>Papers<ul>
<li>EfficientNet NoisyStudent (B0-B7, L2) - <a href="https://arxiv.org/abs/1911.04252">https://arxiv.org/abs/1911.04252</a></li>
<li>EfficientNet AdvProp (B0-B8) - <a href="https://arxiv.org/abs/1911.09665">https://arxiv.org/abs/1911.09665</a></li>
<li>EfficientNet (B0-B7) - <a href="https://arxiv.org/abs/1905.11946">https://arxiv.org/abs/1905.11946</a></li>
<li>EfficientNet-EdgeTPU (S, M, L) - <a href="https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html">https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html</a></li>
<li>MixNet - <a href="https://arxiv.org/abs/1907.09595">https://arxiv.org/abs/1907.09595</a></li>
<li>MNASNet B1, A1 (Squeeze-Excite), and Small - <a href="https://arxiv.org/abs/1807.11626">https://arxiv.org/abs/1807.11626</a></li>
<li>MobileNet-V2 - <a href="https://arxiv.org/abs/1801.04381">https://arxiv.org/abs/1801.04381</a></li>
<li>FBNet-C - <a href="https://arxiv.org/abs/1812.03443">https://arxiv.org/abs/1812.03443</a></li>
<li>Single-Path NAS - <a href="https://arxiv.org/abs/1904.02877">https://arxiv.org/abs/1904.02877</a></li>
</ul>
</li>
<li>My PyTorch code: <a href="https://github.com/rwightman/gen-efficientnet-pytorch">https://github.com/rwightman/gen-efficientnet-pytorch</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet">https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet</a></li>
</ul>
<h2 id="mobilenet-v3-mobilenetv3py">MobileNet-V3 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py">mobilenetv3.py</a>]</h2>
<ul>
<li>Paper: <code>Searching for MobileNetV3</code> - <a href="https://arxiv.org/abs/1905.02244">https://arxiv.org/abs/1905.02244</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet">https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet</a></li>
</ul>
<h2 id="regnet-regnetpy">RegNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/regnet.py">regnet.py</a>]</h2>
<ul>
<li>Paper: <code>Designing Network Design Spaces</code> - <a href="https://arxiv.org/abs/2003.13678">https://arxiv.org/abs/2003.13678</a></li>
<li>Reference code: <a href="https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py">https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py</a></li>
</ul>
<h2 id="resnet-resnext-resnetpy">ResNet, ResNeXt [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnet.py">resnet.py</a>]</h2>
<ul>
<li>ResNet (V1B)<ul>
<li>Paper: <code>Deep Residual Learning for Image Recognition</code> - <a href="https://arxiv.org/abs/1512.03385">https://arxiv.org/abs/1512.03385</a></li>
<li>Code: <a href="https://github.com/pytorch/vision/tree/master/torchvision/models">https://github.com/pytorch/vision/tree/master/torchvision/models</a></li>
</ul>
</li>
<li>ResNeXt<ul>
<li>Paper: <code>Aggregated Residual Transformations for Deep Neural Networks</code> - <a href="https://arxiv.org/abs/1611.05431">https://arxiv.org/abs/1611.05431</a></li>
<li>Code: <a href="https://github.com/pytorch/vision/tree/master/torchvision/models">https://github.com/pytorch/vision/tree/master/torchvision/models</a></li>
</ul>
</li>
<li>'Bag of Tricks' / Gluon C, D, E, S ResNet variants<ul>
<li>Paper: <code>Bag of Tricks for Image Classification with CNNs</code> - <a href="https://arxiv.org/abs/1812.01187">https://arxiv.org/abs/1812.01187</a></li>
<li>Code: <a href="https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnetv1b.py">https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnetv1b.py</a></li>
</ul>
</li>
<li>Instagram pretrained / ImageNet tuned ResNeXt101<ul>
<li>Paper: <code>Exploring the Limits of Weakly Supervised Pretraining</code> - <a href="https://arxiv.org/abs/1805.00932">https://arxiv.org/abs/1805.00932</a></li>
<li>Weights: <a href="https://pytorch.org/hub/facebookresearch_WSL-Images_resnext">https://pytorch.org/hub/facebookresearch_WSL-Images_resnext</a> (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)</li>
</ul>
</li>
<li>Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet and ResNeXts<ul>
<li>Paper: <code>Billion-scale semi-supervised learning for image classification</code> - <a href="https://arxiv.org/abs/1905.00546">https://arxiv.org/abs/1905.00546</a></li>
<li>Weights: <a href="https://github.com/facebookresearch/semi-supervised-ImageNet1K-models">https://github.com/facebookresearch/semi-supervised-ImageNet1K-models</a> (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)</li>
</ul>
</li>
<li>Squeeze-and-Excitation Networks<ul>
<li>Paper: <code>Squeeze-and-Excitation Networks</code> - <a href="https://arxiv.org/abs/1709.01507">https://arxiv.org/abs/1709.01507</a></li>
<li>Code: Added to ResNet base, this is current version going forward, old <code>senet.py</code> is being deprecated</li>
</ul>
</li>
<li>ECAResNet (ECA-Net)<ul>
<li>Paper: <code>ECA-Net: Efficient Channel Attention for Deep CNN</code> - <a href="https://arxiv.org/abs/1910.03151v4">https://arxiv.org/abs/1910.03151v4</a></li>
<li>Code: Added to ResNet base, ECA module contributed by @VRandme, reference <a href="https://github.com/BangguWu/ECANet">https://github.com/BangguWu/ECANet</a></li>
</ul>
</li>
</ul>
<h2 id="res2net-res2netpy">Res2Net [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/res2net.py">res2net.py</a>]</h2>
<ul>
<li>Paper: <code>Res2Net: A New Multi-scale Backbone Architecture</code> - <a href="https://arxiv.org/abs/1904.01169">https://arxiv.org/abs/1904.01169</a></li>
<li>Code: <a href="https://github.com/gasvn/Res2Net">https://github.com/gasvn/Res2Net</a></li>
</ul>
<h2 id="resnest-resnestpy">ResNeSt [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnest.py">resnest.py</a>]</h2>
<ul>
<li>Paper: <code>ResNeSt: Split-Attention Networks</code> - <a href="https://arxiv.org/abs/2004.08955">https://arxiv.org/abs/2004.08955</a></li>
<li>Code: <a href="https://github.com/zhanghang1989/ResNeSt">https://github.com/zhanghang1989/ResNeSt</a></li>
</ul>
<h2 id="rexnet-rexnetpy">ReXNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/rexnet.py">rexnet.py</a>]</h2>
<ul>
<li>Paper: <code>ReXNet: Diminishing Representational Bottleneck on CNN</code> - <a href="https://arxiv.org/abs/2007.00992">https://arxiv.org/abs/2007.00992</a></li>
<li>Code: <a href="https://github.com/clovaai/rexnet">https://github.com/clovaai/rexnet</a></li>
</ul>
<h2 id="selective-kernel-networks-sknetpy">Selective-Kernel Networks [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/sknet.py">sknet.py</a>]</h2>
<ul>
<li>Paper: <code>Selective-Kernel Networks</code> - <a href="https://arxiv.org/abs/1903.06586">https://arxiv.org/abs/1903.06586</a></li>
<li>Code: <a href="https://github.com/implus/SKNet">https://github.com/implus/SKNet</a>, <a href="https://github.com/clovaai/assembled-cnn">https://github.com/clovaai/assembled-cnn</a></li>
</ul>
<h2 id="selecsls-selecslspy">SelecSLS [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/selecsls.py">selecsls.py</a>]</h2>
<ul>
<li>Paper: <code>XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera</code> - <a href="https://arxiv.org/abs/1907.00837">https://arxiv.org/abs/1907.00837</a></li>
<li>Code: <a href="https://github.com/mehtadushy/SelecSLS-Pytorch">https://github.com/mehtadushy/SelecSLS-Pytorch</a></li>
</ul>
<h2 id="squeeze-and-excitation-networks-senetpy">Squeeze-and-Excitation Networks [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/senet.py">senet.py</a>]</h2>
<p>NOTE: I am deprecating this version of the networks, the new ones are part of <code>resnet.py</code>
* Paper: <code>Squeeze-and-Excitation Networks</code> - <a href="https://arxiv.org/abs/1709.01507">https://arxiv.org/abs/1709.01507</a>
* Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a> </p>
<h2 id="tresnet-tresnetpy">TResNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/tresnet.py">tresnet.py</a>]</h2>
<ul>
<li>Paper: <code>TResNet: High Performance GPU-Dedicated Architecture</code> - <a href="https://arxiv.org/abs/2003.13630">https://arxiv.org/abs/2003.13630</a></li>
<li>Code: <a href="https://github.com/mrT23/TResNet">https://github.com/mrT23/TResNet</a></li>
</ul>
<h2 id="vovnet-v2-and-v1-vovnetpy">VovNet V2 and V1 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vovnet.py">vovnet.py</a>]</h2>
<ul>
<li>Paper: <code>CenterMask : Real-Time Anchor-Free Instance Segmentation</code> - <a href="https://arxiv.org/abs/1911.06667">https://arxiv.org/abs/1911.06667</a></li>
<li>Reference code: <a href="https://github.com/youngwanLEE/vovnet-detectron2">https://github.com/youngwanLEE/vovnet-detectron2</a></li>
</ul>
<h2 id="xception-xceptionpy">Xception [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/xception.py">xception.py</a>]</h2>
<ul>
<li>Paper: <code>Xception: Deep Learning with Depthwise Separable Convolutions</code> - <a href="https://arxiv.org/abs/1610.02357">https://arxiv.org/abs/1610.02357</a></li>
<li>Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a></li>
</ul>
<h2 id="xception-modified-aligned-gluon-gluon_xceptionpy">Xception (Modified Aligned, Gluon) [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/gluon_xception.py">gluon_xception.py</a>]</h2>
<ul>
<li>Paper: <code>Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation</code> - <a href="https://arxiv.org/abs/1802.02611">https://arxiv.org/abs/1802.02611</a></li>
<li>Reference code: <a href="https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo">https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo</a>, <a href="https://github.com/jfzhang95/pytorch-deeplab-xception/">https://github.com/jfzhang95/pytorch-deeplab-xception/</a></li>
</ul>
<h2 id="xception-modified-aligned-tf-aligned_xceptionpy">Xception (Modified Aligned, TF) [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/aligned_xception.py">aligned_xception.py</a>]</h2>
<ul>
<li>Paper: <code>Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation</code> - <a href="https://arxiv.org/abs/1802.02611">https://arxiv.org/abs/1802.02611</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/deeplab">https://github.com/tensorflow/models/tree/master/research/deeplab</a></li>
</ul>
</article>
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