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231 lines
12 KiB
231 lines
12 KiB
2 years ago
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# Model Summaries
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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.
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Most included models have pretrained weights. The weights are either:
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1. from their original sources
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2. ported by myself from their original impl in a different framework (e.g. Tensorflow models)
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3. trained from scratch using the included training script
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The validation results for the pretrained weights are [here](results)
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A more exciting view (with pretty pictures) of the models within `timm` can be found at [paperswithcode](https://paperswithcode.com/lib/timm).
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## Big Transfer ResNetV2 (BiT)
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* Implementation: [resnetv2.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnetv2.py)
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* Paper: `Big Transfer (BiT): General Visual Representation Learning` - https://arxiv.org/abs/1912.11370
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* Reference code: https://github.com/google-research/big_transfer
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## Cross-Stage Partial Networks
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* Implementation: [cspnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/cspnet.py)
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* Paper: `CSPNet: A New Backbone that can Enhance Learning Capability of CNN` - https://arxiv.org/abs/1911.11929
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* Reference impl: https://github.com/WongKinYiu/CrossStagePartialNetworks
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## DenseNet
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* Implementation: [densenet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/densenet.py)
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* Paper: `Densely Connected Convolutional Networks` - https://arxiv.org/abs/1608.06993
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* Code: https://github.com/pytorch/vision/tree/master/torchvision/models
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## DLA
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* Implementation: [dla.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dla.py)
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* Paper: https://arxiv.org/abs/1707.06484
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* Code: https://github.com/ucbdrive/dla
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## Dual-Path Networks
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* Implementation: [dpn.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dpn.py)
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* Paper: `Dual Path Networks` - https://arxiv.org/abs/1707.01629
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* My PyTorch code: https://github.com/rwightman/pytorch-dpn-pretrained
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* Reference code: https://github.com/cypw/DPNs
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## GPU-Efficient Networks
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* Implementation: [byobnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/byobnet.py)
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* Paper: `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
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* Reference code: https://github.com/idstcv/GPU-Efficient-Networks
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## HRNet
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* Implementation: [hrnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/hrnet.py)
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* Paper: `Deep High-Resolution Representation Learning for Visual Recognition` - https://arxiv.org/abs/1908.07919
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* Code: https://github.com/HRNet/HRNet-Image-Classification
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## Inception-V3
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* Implementation: [inception_v3.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v3.py)
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* Paper: `Rethinking the Inception Architecture for Computer Vision` - https://arxiv.org/abs/1512.00567
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* Code: https://github.com/pytorch/vision/tree/master/torchvision/models
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## Inception-V4
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* Implementation: [inception_v4.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v4.py)
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* Paper: `Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning` - https://arxiv.org/abs/1602.07261
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* Code: https://github.com/Cadene/pretrained-models.pytorch
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* Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets
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## Inception-ResNet-V2
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* Implementation: [inception_resnet_v2.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_resnet_v2.py)
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* Paper: `Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning` - https://arxiv.org/abs/1602.07261
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* Code: https://github.com/Cadene/pretrained-models.pytorch
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* Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets
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## NASNet-A
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* Implementation: [nasnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/nasnet.py)
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* Papers: `Learning Transferable Architectures for Scalable Image Recognition` - https://arxiv.org/abs/1707.07012
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* Code: https://github.com/Cadene/pretrained-models.pytorch
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* Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet
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## PNasNet-5
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* Implementation: [pnasnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/pnasnet.py)
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* Papers: `Progressive Neural Architecture Search` - https://arxiv.org/abs/1712.00559
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* Code: https://github.com/Cadene/pretrained-models.pytorch
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* Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet
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## EfficientNet
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* Implementation: [efficientnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py)
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* Papers:
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* EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252
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* EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665
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* EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946
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* EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html
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* MixNet - https://arxiv.org/abs/1907.09595
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* MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626
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* MobileNet-V2 - https://arxiv.org/abs/1801.04381
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* FBNet-C - https://arxiv.org/abs/1812.03443
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* Single-Path NAS - https://arxiv.org/abs/1904.02877
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* My PyTorch code: https://github.com/rwightman/gen-efficientnet-pytorch
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* Reference code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
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## MobileNet-V3
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* Implementation: [mobilenetv3.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py)
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* Paper: `Searching for MobileNetV3` - https://arxiv.org/abs/1905.02244
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* Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet
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## RegNet
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* Implementation: [regnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/regnet.py)
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* Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
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* Reference code: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
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## RepVGG
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* Implementation: [byobnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/byobnet.py)
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* Paper: `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
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* Reference code: https://github.com/DingXiaoH/RepVGG
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## ResNet, ResNeXt
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* Implementation: [resnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnet.py)
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* ResNet (V1B)
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* Paper: `Deep Residual Learning for Image Recognition` - https://arxiv.org/abs/1512.03385
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* Code: https://github.com/pytorch/vision/tree/master/torchvision/models
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* ResNeXt
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* Paper: `Aggregated Residual Transformations for Deep Neural Networks` - https://arxiv.org/abs/1611.05431
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* Code: https://github.com/pytorch/vision/tree/master/torchvision/models
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* 'Bag of Tricks' / Gluon C, D, E, S ResNet variants
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* Paper: `Bag of Tricks for Image Classification with CNNs` - https://arxiv.org/abs/1812.01187
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* Code: https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnetv1b.py
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* Instagram pretrained / ImageNet tuned ResNeXt101
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* Paper: `Exploring the Limits of Weakly Supervised Pretraining` - https://arxiv.org/abs/1805.00932
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* Weights: https://pytorch.org/hub/facebookresearch_WSL-Images_resnext (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)
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* Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet and ResNeXts
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* Paper: `Billion-scale semi-supervised learning for image classification` - https://arxiv.org/abs/1905.00546
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* Weights: https://github.com/facebookresearch/semi-supervised-ImageNet1K-models (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)
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* Squeeze-and-Excitation Networks
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* Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
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* Code: Added to ResNet base, this is current version going forward, old `senet.py` is being deprecated
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* ECAResNet (ECA-Net)
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* Paper: `ECA-Net: Efficient Channel Attention for Deep CNN` - https://arxiv.org/abs/1910.03151v4
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* Code: Added to ResNet base, ECA module contributed by @VRandme, reference https://github.com/BangguWu/ECANet
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## Res2Net
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* Implementation: [res2net.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/res2net.py)
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* Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
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* Code: https://github.com/gasvn/Res2Net
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## ResNeSt
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* Implementation: [resnest.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnest.py)
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* Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
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* Code: https://github.com/zhanghang1989/ResNeSt
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## ReXNet
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* Implementation: [rexnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/rexnet.py)
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* Paper: `ReXNet: Diminishing Representational Bottleneck on CNN` - https://arxiv.org/abs/2007.00992
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* Code: https://github.com/clovaai/rexnet
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## Selective-Kernel Networks
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* Implementation: [sknet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/sknet.py)
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* Paper: `Selective-Kernel Networks` - https://arxiv.org/abs/1903.06586
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* Code: https://github.com/implus/SKNet, https://github.com/clovaai/assembled-cnn
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## SelecSLS
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* Implementation: [selecsls.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/selecsls.py)
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* Paper: `XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera` - https://arxiv.org/abs/1907.00837
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* Code: https://github.com/mehtadushy/SelecSLS-Pytorch
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## Squeeze-and-Excitation Networks
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* Implementation: [senet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/senet.py)
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NOTE: I am deprecating this version of the networks, the new ones are part of `resnet.py`
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* Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
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* Code: https://github.com/Cadene/pretrained-models.pytorch
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## TResNet
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* Implementation: [tresnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/tresnet.py)
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* Paper: `TResNet: High Performance GPU-Dedicated Architecture` - https://arxiv.org/abs/2003.13630
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* Code: https://github.com/mrT23/TResNet
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## VGG
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* Implementation: [vgg.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vgg.py)
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* Paper: `Very Deep Convolutional Networks For Large-Scale Image Recognition` - https://arxiv.org/pdf/1409.1556.pdf
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* Reference code: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
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## Vision Transformer
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* Implementation: [vision_transformer.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py)
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* Paper: `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929
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* Reference code and pretrained weights: https://github.com/google-research/vision_transformer
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## VovNet V2 and V1
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* Implementation: [vovnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vovnet.py)
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* Paper: `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
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* Reference code: https://github.com/youngwanLEE/vovnet-detectron2
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## Xception
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* Implementation: [xception.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/xception.py)
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* Paper: `Xception: Deep Learning with Depthwise Separable Convolutions` - https://arxiv.org/abs/1610.02357
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* Code: https://github.com/Cadene/pretrained-models.pytorch
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## Xception (Modified Aligned, Gluon)
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* Implementation: [gluon_xception.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/gluon_xception.py)
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* Paper: `Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation` - https://arxiv.org/abs/1802.02611
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* Reference code: https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo, https://github.com/jfzhang95/pytorch-deeplab-xception/
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## Xception (Modified Aligned, TF)
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* Implementation: [aligned_xception.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/aligned_xception.py)
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* Paper: `Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation` - https://arxiv.org/abs/1802.02611
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* Reference code: https://github.com/tensorflow/models/tree/master/research/deeplab
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