模型概述

包含的模型架构来自各种来源。来源包括论文、我重写/改编的原始实现（“参考代码”）以及我直接利用的 PyTorch 实现（“代码”），如下所示。

大多数包含的模型都具有预训练权重。权重要么是

1. 来自其原始来源的
2. 由我自己从其在不同框架中的原始实现移植而来（例如 Tensorflow 模型）
3. 使用包含的训练脚本从头开始训练的

预训练权重的验证结果在此

关于 timm 中模型的更精彩视图（附带精美图片）可以在paperswithcode中找到。

Big Transfer ResNetV2 (BiT)

• 实现：resnetv2.py
• 论文：Big Transfer (BiT): General Visual Representation Learning - https://arxiv.org/abs/1912.11370
• 参考代码：https://github.com/google-research/big_transfer

跨阶段局部网络

• 实现：cspnet.py
• 论文：CSPNet: A New Backbone that can Enhance Learning Capability of CNN - https://arxiv.org/abs/1911.11929
• 参考实现：https://github.com/WongKinYiu/CrossStagePartialNetworks

DenseNet

• 实现：densenet.py
• 论文：Densely Connected Convolutional Networks - https://arxiv.org/abs/1608.06993
• 代码：https://github.com/pytorch/vision/tree/master/torchvision/models

DLA

• 实现：dla.py
• 论文：https://arxiv.org/abs/1707.06484
• 代码：https://github.com/ucbdrive/dla

双路径网络

• 实现：dpn.py
• 论文：Dual Path Networks - https://arxiv.org/abs/1707.01629
• 我的 PyTorch 代码：https://github.com/rwightman/pytorch-dpn-pretrained
• 参考代码：https://github.com/cypw/DPNs

GPU 高效网络

• 实现：byobnet.py
• 论文：Neural Architecture Design for GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
• 参考代码：https://github.com/idstcv/GPU-Efficient-Networks

HRNet

• 实现：hrnet.py
• 论文：Deep High-Resolution Representation Learning for Visual Recognition - https://arxiv.org/abs/1908.07919
• 代码：https://github.com/HRNet/HRNet-Image-Classification

Inception-V3

• 实现：inception_v3.py
• 论文：Rethinking the Inception Architecture for Computer Vision - https://arxiv.org/abs/1512.00567
• 代码：https://github.com/pytorch/vision/tree/master/torchvision/models

Inception-V4

• 实现：inception_v4.py
• 论文：Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning - https://arxiv.org/abs/1602.07261
• 代码：https://github.com/Cadene/pretrained-models.pytorch
• 参考代码：https://github.com/tensorflow/models/tree/master/research/slim/nets

Inception-ResNet-V2

• 实现：inception_resnet_v2.py
• 论文：Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning - https://arxiv.org/abs/1602.07261
• 代码：https://github.com/Cadene/pretrained-models.pytorch
• 参考代码：https://github.com/tensorflow/models/tree/master/research/slim/nets

NASNet-A

• 实现：nasnet.py
• 论文：Learning Transferable Architectures for Scalable Image Recognition - https://arxiv.org/abs/1707.07012
• 代码：https://github.com/Cadene/pretrained-models.pytorch
• 参考代码：https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet

PNasNet-5

• 实施: pnasnet.py
• 论文: Progressive Neural Architecture Search - https://arxiv.org/abs/1712.00559
• 代码：https://github.com/Cadene/pretrained-models.pytorch
• 参考代码：https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet

EfficientNet

• 实施: efficientnet.py
• 论文
  • 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
• 我的 PyTorch 代码: https://github.com/rwightman/gen-efficientnet-pytorch
• 参考代码: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

MobileNet-V3

• 实施: mobilenetv3.py
• 论文: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
• 参考代码: https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet

RegNet

• 实施: regnet.py
• 论文: Designing Network Design Spaces - https://arxiv.org/abs/2003.13678
• 参考代码: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py

RepVGG

• 实现：byobnet.py
• 论文: Making VGG-style ConvNets Great Again - https://arxiv.org/abs/2101.03697
• 参考代码: https://github.com/DingXiaoH/RepVGG

ResNet, ResNeXt

• 实施: resnet.py

• ResNet (V1B)

  • 论文: Deep Residual Learning for Image Recognition - https://arxiv.org/abs/1512.03385
  • 代码：https://github.com/pytorch/vision/tree/master/torchvision/models

• ResNeXt

  • 论文: Aggregated Residual Transformations for Deep Neural Networks - https://arxiv.org/abs/1611.05431
  • 代码：https://github.com/pytorch/vision/tree/master/torchvision/models

• ‘Bag of Tricks’ / Gluon C, D, E, S ResNet 变体

  • 论文: Bag of Tricks for Image Classification with CNNs - https://arxiv.org/abs/1812.01187
  • 代码: https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnetv1b.py

• Instagram 预训练 / ImageNet 调优 ResNeXt101

  • 论文: Exploring the Limits of Weakly Supervised Pretraining - https://arxiv.org/abs/1805.00932
  • 权重: https://pytorch.ac.cn/hub/facebookresearch_WSL-Images_resnext (注意: CC BY-NC 4.0 许可证，对商业用途不友好)

• 半监督 (SSL) / 半弱监督 (SWSL) ResNet 和 ResNeXt

  • 论文: Billion-scale semi-supervised learning for image classification - https://arxiv.org/abs/1905.00546
  • 权重: https://github.com/facebookresearch/semi-supervised-ImageNet1K-models (注意: CC BY-NC 4.0 许可证，对商业用途不友好)

• Squeeze-and-Excitation Networks

  • 论文: Squeeze-and-Excitation Networks - https://arxiv.org/abs/1709.01507
  • 代码: 已添加到 ResNet 基础架构中，这是当前的最新版本，旧的 senet.py 正在被弃用

• ECAResNet (ECA-Net)

  • 论文: ECA-Net: Efficient Channel Attention for Deep CNN - https://arxiv.org/abs/1910.03151v4
  • 代码: 已添加到 ResNet 基础架构中，ECA 模块由 @VRandme 贡献，参考 https://github.com/BangguWu/ECANet

Res2Net

• 实施: res2net.py
• 论文: Res2Net: A New Multi-scale Backbone Architecture - https://arxiv.org/abs/1904.01169
• 代码: https://github.com/gasvn/Res2Net

ResNeSt

• 实施: resnest.py
• 论文: ResNeSt: Split-Attention Networks - https://arxiv.org/abs/2004.08955
• 代码: https://github.com/zhanghang1989/ResNeSt

ReXNet

• 实施: rexnet.py
• 论文: ReXNet: Diminishing Representational Bottleneck on CNN - https://arxiv.org/abs/2007.00992
• 代码: https://github.com/clovaai/rexnet

Selective-Kernel Networks

• 实施: sknet.py
• 论文: Selective-Kernel Networks - https://arxiv.org/abs/1903.06586
• 代码: https://github.com/implus/SKNet, https://github.com/clovaai/assembled-cnn

SelecSLS

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

Squeeze-and-Excitation Networks

• 实施: senet.py 注意: 我正在弃用此版本的网络，新版本是 resnet.py 的一部分

• 论文: Squeeze-and-Excitation Networks - https://arxiv.org/abs/1709.01507

• 代码：https://github.com/Cadene/pretrained-models.pytorch

TResNet

• 实施: tresnet.py
• 论文: TResNet: High Performance GPU-Dedicated Architecture - https://arxiv.org/abs/2003.13630
• 代码: https://github.com/mrT23/TResNet

VGG

• 实施: vgg.py
• 论文: Very Deep Convolutional Networks For Large-Scale Image Recognition - https://arxiv.org/pdf/1409.1556.pdf
• 参考代码: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py

Vision Transformer

• 实施: vision_transformer.py
• 论文: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale - https://arxiv.org/abs/2010.11929
• 参考代码和预训练权重: https://github.com/google-research/vision_transformer

VovNet V2 和 V1

• 实施: vovnet.py
• 论文: CenterMask : Real-Time Anchor-Free Instance Segmentation - https://arxiv.org/abs/1911.06667
• 参考代码: https://github.com/youngwanLEE/vovnet-detectron2

Xception

• 实施: xception.py
• 论文: Xception: Deep Learning with Depthwise Separable Convolutions - https://arxiv.org/abs/1610.02357
• 代码：https://github.com/Cadene/pretrained-models.pytorch

Xception (Modified Aligned, Gluon)

• 实施: gluon_xception.py
• 论文: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation - https://arxiv.org/abs/1802.02611
• 参考代码: https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo, https://github.com/jfzhang95/pytorch-deeplab-xception/

Xception (Modified Aligned, TF)

• 实施: aligned_xception.py
• 论文: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation - https://arxiv.org/abs/1802.02611
• 参考代码: https://github.com/tensorflow/models/tree/master/research/deeplab

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