# Model Summaries

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 are [here](results.md)

A more exciting view (with pretty pictures) of the models within `timm` can be found at [paperswithcode](https://paperswithcode.com/lib/timm).

## Big Transfer ResNetV2 (BiT) [[resnetv2.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnetv2.py)]
* Paper: `Big Transfer (BiT): General Visual Representation Learning` - https://arxiv.org/abs/1912.11370
* Reference code: https://github.com/google-research/big_transfer

## Cross-Stage Partial Networks [[cspnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dla.py)]
* Paper: https://arxiv.org/abs/1707.06484
* Code: https://github.com/ucbdrive/dla

## Dual-Path Networks [[dpn.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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

## GPU-Efficient Networks [[byobnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/byobnet.py)]
* Paper: `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
* Reference code: https://github.com/idstcv/GPU-Efficient-Networks

## HRNet [[hrnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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

## RepVGG [[byobnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/byobnet.py)]
* Paper: `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
* Reference code: https://github.com/DingXiaoH/RepVGG

## ResNet, ResNeXt [[resnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnest.py)]
* Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
* Code: https://github.com/zhanghang1989/ResNeSt

## ReXNet [[rexnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/tresnet.py)]
* Paper: `TResNet: High Performance GPU-Dedicated Architecture` - https://arxiv.org/abs/2003.13630
* Code: https://github.com/mrT23/TResNet

## VGG [[vgg.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vgg.py)]
* Paper: `Very Deep Convolutional Networks For Large-Scale Image Recognition` - https://arxiv.org/pdf/1409.1556.pdf
* Reference code: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py

## Vision Transformer [[vision_transformer.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/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