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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-V3 [inception_v3.py]
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EfficientNet [efficientnet.py]
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ResNet, ResNeXt [resnet.py]
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ResNeSt [resnest.py]
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ReXNet [rexnet.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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HRNet [hrnet.py]
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Inception-V3 [inception_v3.py]
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EfficientNet [efficientnet.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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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 >
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< / div >
< / a >
< / nav >
< / div >
< div class = "md-footer-meta md-typeset" >
< div class = "md-footer-meta__inner md-grid" >
< div class = "md-footer-copyright" >
Made with
< a href = "https://squidfunk.github.io/mkdocs-material/" target = "_blank" rel = "noopener" >
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< / a >
< / div >
< / div >
< / div >
< / footer >
< / div >
< script src = "../assets/javascripts/vendor.d710d30a.min.js" > < / script >
< script src = "../assets/javascripts/bundle.b39636ac.min.js" > < / script > < script id = "__lang" type = "application/json" > { "clipboard.copy" : "Copy to clipboard" , "clipboard.copied" : "Copied to clipboard" , "search.config.lang" : "en" , "search.config.pipeline" : "trimmer, stopWordFilter" , "search.config.separator" : "[\\s\\-]+" , "search.result.placeholder" : "Type to start searching" , "search.result.none" : "No matching documents" , "search.result.one" : "1 matching document" , "search.result.other" : "# matching documents" } < / script >
< script >
app = initialize({
base: "..",
features: [],
search: Object.assign({
worker: "../assets/javascripts/worker/search.a68abb33.min.js"
}, typeof search !== "undefined" & & search)
})
< / 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 >
