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PyTorch Image Models (timm)是一个图像模型（models）、层（layers）、实用程序（utilities）、优化器（optimizers）、调度器（schedulers）、数据加载/增强（data-loaders / augmentations）和参考训练/验证脚本（reference training / validation scripts）的集合，目的是将各种SOTA模型组合在一起，从而能够重现ImageNet的训练结果。
作者：Ross Wightman，来自加拿大温哥华。
git地址：https://github.com/rwightman/pytorch-image-models#introduction
涵盖的模型：
Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723
BEiT - https://arxiv.org/abs/2106.08254
Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
Bottleneck Transformers - https://arxiv.org/abs/2101.11605
CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239
CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399
CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803
ConvNeXt - https://arxiv.org/abs/2201.03545
ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
DeiT - https://arxiv.org/abs/2012.12877
DeiT-III - https://arxiv.org/pdf/2204.07118.pdf
DenseNet - https://arxiv.org/abs/1608.06993
DLA - https://arxiv.org/abs/1707.06484
DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
EdgeNeXt - https://arxiv.org/abs/2206.10589
EfficientFormer - https://arxiv.org/abs/2206.01191
EfficientNet (MBConvNet Family)
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
EfficientNet V2 - https://arxiv.org/abs/2104.00298
FBNet-C - https://arxiv.org/abs/1812.03443
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
Single-Path NAS - https://arxiv.org/abs/1904.02877
TinyNet - https://arxiv.org/abs/2010.14819
EVA - https://arxiv.org/abs/2211.07636
GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959
GhostNet - https://arxiv.org/abs/1911.11907
gMLP - https://arxiv.org/abs/2105.08050
GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
Halo Nets - https://arxiv.org/abs/2103.12731
HRNet - https://arxiv.org/abs/1908.07919
Inception-V3 - https://arxiv.org/abs/1512.00567
Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
Lambda Networks - https://arxiv.org/abs/2102.08602
LeViT (Vision Transformer in ConvNet’s Clothing) - https://arxiv.org/abs/2104.01136
MaxViT (Multi-Axis Vision Transformer) - https://arxiv.org/abs/2204.01697
MLP-Mixer - https://arxiv.org/abs/2105.01601
MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
FBNet-V3 - https://arxiv.org/abs/2006.02049
HardCoRe-NAS - https://arxiv.org/abs/2102.11646
LCNet - https://arxiv.org/abs/2109.15099
MobileViT - https://arxiv.org/abs/2110.02178
MobileViT-V2 - https://arxiv.org/abs/2206.02680
MViT-V2 (Improved Multiscale Vision Transformer) - https://arxiv.org/abs/2112.01526
NASNet-A - https://arxiv.org/abs/1707.07012
NesT - https://arxiv.org/abs/2105.12723
NFNet-F - https://arxiv.org/abs/2102.06171
NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692
PNasNet - https://arxiv.org/abs/1712.00559
PoolFormer (MetaFormer) - https://arxiv.org/abs/2111.11418
Pooling-based Vision Transformer (PiT) - https://arxiv.org/abs/2103.16302
PVT-V2 (Improved Pyramid Vision Transformer) - https://arxiv.org/abs/2106.13797
RegNet - https://arxiv.org/abs/2003.13678
RegNetZ - https://arxiv.org/abs/2103.06877
RepVGG - https://arxiv.org/abs/2101.03697
ResMLP - https://arxiv.org/abs/2105.03404
ResNet/ResNeXt
ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
ResNeXt - https://arxiv.org/abs/1611.05431
‘Bag of Tricks’ / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187
Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932
Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/abs/1905.00546
ECA-Net (ECAResNet) - https://arxiv.org/abs/1910.03151v4
Squeeze-and-Excitation Networks (SEResNet) - https://arxiv.org/abs/1709.01507
ResNet-RS - https://arxiv.org/abs/2103.07579
Res2Net - https://arxiv.org/abs/1904.01169
ResNeSt - https://arxiv.org/abs/2004.08955
ReXNet - https://arxiv.org/abs/2007.00992
SelecSLS - https://arxiv.org/abs/1907.00837
Selective Kernel Networks - https://arxiv.org/abs/1903.06586
Sequencer2D - https://arxiv.org/abs/2205.01972
Swin S3 (AutoFormerV2) - https://arxiv.org/abs/2111.14725
Swin Transformer - https://arxiv.org/abs/2103.14030
Swin Transformer V2 - https://arxiv.org/abs/2111.09883
Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112
TResNet - https://arxiv.org/abs/2003.13630
Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf
Visformer - https://arxiv.org/abs/2104.12533
Vision Transformer - https://arxiv.org/abs/2010.11929
VOLO (Vision Outlooker) - https://arxiv.org/abs/2106.13112
VovNet V2 and V1 - https://arxiv.org/abs/1911.06667
Xception - https://arxiv.org/abs/1610.02357
Xception (Modified Aligned, Gluon) - https://arxiv.org/abs/1802.02611
Xception (Modified Aligned, TF) - https://arxiv.org/abs/1802.02611
XCiT (Cross-Covariance Image Transformers) - https://arxiv.org/abs/2106.09681
使用样例
import timm
self.backbone = timm.create_model('resnet50', pretrained=False, num_classes=500, in_chans=13)
out = self.backbone(x)
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