Model Architectures

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.

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

The validation results for the pretrained weights can be found here

Cross-Stage Partial Networks [cspnet.py]

• Paper: CSPNet: A New Backbone that can Enhance Learning Capability of CNN - https://arxiv.org/abs/1911.11929
• Reference impl: https://github.com/WongKinYiu/CrossStagePartialNetworks

DenseNet [densenet.py]

• Paper: Densely Connected Convolutional Networks - https://arxiv.org/abs/1608.06993
• Code: https://github.com/pytorch/vision/tree/master/torchvision/models

DLA [dla.py]

• Paper: https://arxiv.org/abs/1707.06484
• Code: https://github.com/ucbdrive/dla

Dual-Path Networks [dpn.py]

• Paper: Dual Path Networks - https://arxiv.org/abs/1707.01629
• My PyTorch code: https://github.com/rwightman/pytorch-dpn-pretrained
• Reference code: https://github.com/cypw/DPNs

HRNet [hrnet.py]

• Paper: Deep High-Resolution Representation Learning for Visual Recognition - https://arxiv.org/abs/1908.07919
• Code: https://github.com/HRNet/HRNet-Image-Classification

Inception-V3 [inception_v3.py]

• Paper: Rethinking the Inception Architecture for Computer Vision - https://arxiv.org/abs/1512.00567
• Code: https://github.com/pytorch/vision/tree/master/torchvision/models

Inception-V4 [inception_v4.py]

• Paper: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning - https://arxiv.org/abs/1602.07261
• Code: https://github.com/Cadene/pretrained-models.pytorch
• Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets

Inception-ResNet-V2 [inception_resnet_v2.py]

• Paper: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning - https://arxiv.org/abs/1602.07261
• Code: https://github.com/Cadene/pretrained-models.pytorch
• Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets

NASNet-A [nasnet.py]

• Papers: Learning Transferable Architectures for Scalable Image Recognition - https://arxiv.org/abs/1707.07012
• Code: https://github.com/Cadene/pretrained-models.pytorch
• Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet

PNasNet-5 [pnasnet.py]

• Papers: Progressive Neural Architecture Search - https://arxiv.org/abs/1712.00559
• Code: https://github.com/Cadene/pretrained-models.pytorch
• Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet

EfficientNet [efficientnet.py]

• Papers:
  • EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252
  • EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665
  • EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946
  • EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html
  • MixNet - https://arxiv.org/abs/1907.09595
  • MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626
  • MobileNet-V2 - https://arxiv.org/abs/1801.04381
  • FBNet-C - https://arxiv.org/abs/1812.03443
  • Single-Path NAS - https://arxiv.org/abs/1904.02877
• My PyTorch code: https://github.com/rwightman/gen-efficientnet-pytorch
• Reference code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

MobileNet-V3 [mobilenetv3.py]

• Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
• Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet

RegNet [regnet.py]

• Paper: Designing Network Design Spaces - https://arxiv.org/abs/2003.13678
• Reference code: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py

ResNet, ResNeXt [resnet.py]

• ResNet (V1B)
  • Paper: Deep Residual Learning for Image Recognition - https://arxiv.org/abs/1512.03385
  • Code: https://github.com/pytorch/vision/tree/master/torchvision/models
• ResNeXt
  • Paper: Aggregated Residual Transformations for Deep Neural Networks - https://arxiv.org/abs/1611.05431
  • Code: https://github.com/pytorch/vision/tree/master/torchvision/models
• 'Bag of Tricks' / Gluon C, D, E, S ResNet variants
  • Paper: Bag of Tricks for Image Classification with CNNs - https://arxiv.org/abs/1812.01187
  • Code: https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnetv1b.py
• Instagram pretrained / ImageNet tuned ResNeXt101
  • Paper: Exploring the Limits of Weakly Supervised Pretraining - https://arxiv.org/abs/1805.00932
  • Weights: https://pytorch.org/hub/facebookresearch_WSL-Images_resnext (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)
• Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet and ResNeXts
  • Paper: Billion-scale semi-supervised learning for image classification - https://arxiv.org/abs/1905.00546
  • Weights: https://github.com/facebookresearch/semi-supervised-ImageNet1K-models (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)
• Squeeze-and-Excitation Networks
  • Paper: Squeeze-and-Excitation Networks - https://arxiv.org/abs/1709.01507
  • Code: Added to ResNet base, this is current version going forward, old senet.py is being deprecated
• ECAResNet (ECA-Net)
  • Paper: ECA-Net: Efficient Channel Attention for Deep CNN - https://arxiv.org/abs/1910.03151v4
  • Code: Added to ResNet base, ECA module contributed by @VRandme, reference https://github.com/BangguWu/ECANet

Res2Net [res2net.py]

• Paper: Res2Net: A New Multi-scale Backbone Architecture - https://arxiv.org/abs/1904.01169
• Code: https://github.com/gasvn/Res2Net

ResNeSt [resnest.py]

• Paper: ResNeSt: Split-Attention Networks - https://arxiv.org/abs/2004.08955
• Code: https://github.com/zhanghang1989/ResNeSt

ReXNet [rexnet.py]

• Paper: ReXNet: Diminishing Representational Bottleneck on CNN - https://arxiv.org/abs/2007.00992
• Code: https://github.com/clovaai/rexnet

Selective-Kernel Networks [sknet.py]

• Paper: Selective-Kernel Networks - https://arxiv.org/abs/1903.06586
• Code: https://github.com/implus/SKNet, https://github.com/clovaai/assembled-cnn

SelecSLS [selecsls.py]

• Paper: XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera - https://arxiv.org/abs/1907.00837
• Code: https://github.com/mehtadushy/SelecSLS-Pytorch

Squeeze-and-Excitation Networks [senet.py]

NOTE: I am deprecating this version of the networks, the new ones are part of resnet.py

• Paper: Squeeze-and-Excitation Networks - https://arxiv.org/abs/1709.01507
• Code: https://github.com/Cadene/pretrained-models.pytorch

TResNet [tresnet.py]

• Paper: TResNet: High Performance GPU-Dedicated Architecture - https://arxiv.org/abs/2003.13630
• Code: https://github.com/mrT23/TResNet

Vision Transformer [vision_transformer.py]

• Paper: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale - https://arxiv.org/abs/2010.11929
• Reference code and pretrained weights: https://github.com/google-research/vision_transformer

VovNet V2 and V1 [vovnet.py]

• Paper: CenterMask : Real-Time Anchor-Free Instance Segmentation - https://arxiv.org/abs/1911.06667
• Reference code: https://github.com/youngwanLEE/vovnet-detectron2

Xception [xception.py]

• Paper: Xception: Deep Learning with Depthwise Separable Convolutions - https://arxiv.org/abs/1610.02357
• Code: https://github.com/Cadene/pretrained-models.pytorch

Xception (Modified Aligned, Gluon) [gluon_xception.py]

• Paper: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation - https://arxiv.org/abs/1802.02611
• Reference code: https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo, https://github.com/jfzhang95/pytorch-deeplab-xception/

Xception (Modified Aligned, TF) [aligned_xception.py]

• Paper: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation - https://arxiv.org/abs/1802.02611
• Reference code: https://github.com/tensorflow/models/tree/master/research/deeplab