timm 库介绍

原文链接：https://zhuanlan.zhihu.com/p/350837279

为了尊重原作者，这边只放一个链接，只做简单介绍，感兴趣的请去原文博客。

PyTorchImageModels，简称 timm，是一个巨大的 PyTorch 代码集合，旨在将各种 SOTA 模型整合在一起，并具有复现 ImageNet 训练结果的能力。虽然模型架构是 timm 的重点，但它还包括许多数据增强 (data augmentations)、正则化技术 (regularization techniques)、优化器 (optimizers) 和学习率策略 (learning rate schedulers) 的实现。

timm作者是，Ross Wightman ，他的github留言挺好的“Always learning, constantly curious. Building ML/AI systems, watching loss curves.”

PyTorch Image Models

All model architecture families include variants with pretrained weights. There are specific model variants without any weights, it is NOT a bug. Help training new or better weights is always appreciated.

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
ConvNeXt-V2 - http://arxiv.org/abs/2301.00808
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
EfficientViT (MIT) - https://arxiv.org/abs/2205.14756
EfficientViT (MSRA) - https://arxiv.org/abs/2305.07027
EVA - https://arxiv.org/abs/2211.07636
EVA-02 - https://arxiv.org/abs/2303.11331
FastViT - https://arxiv.org/abs/2303.14189
FlexiViT - https://arxiv.org/abs/2212.08013
FocalNet (Focal Modulation Networks) - https://arxiv.org/abs/2203.11926
GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959
GhostNet - https://arxiv.org/abs/1911.11907
GhostNet-V2 - https://arxiv.org/abs/2211.12905
gMLP - https://arxiv.org/abs/2105.08050
GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
Halo Nets - https://arxiv.org/abs/2103.12731
HGNet / HGNet-V2 - TBD
HRNet - https://arxiv.org/abs/1908.07919
InceptionNeXt - https://arxiv.org/abs/2303.16900
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
MetaFormer (PoolFormer-v2, ConvFormer, CAFormer) - https://arxiv.org/abs/2210.13452
MLP-Mixer - https://arxiv.org/abs/2105.01601
MobileCLIP - https://arxiv.org/abs/2311.17049
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
MobileNetV4 - https://arxiv.org/abs/2404.10518
MobileOne - https://arxiv.org/abs/2206.04040
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
Next-ViT - https://arxiv.org/abs/2207.05501
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
RepGhostNet - https://arxiv.org/abs/2211.06088
RepViT - https://arxiv.org/abs/2307.09283
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
ViTamin - https://arxiv.org/abs/2404.02132
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

timm 库介绍

PyTorch Image Models

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