timm documentation

Model Summaries

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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

A more exciting view (with pretty pictures) of the models within timm can be found at paperswithcode.

Big Transfer ResNetV2 (BiT)

Cross-Stage Partial Networks

DenseNet

DLA

Dual-Path Networks

GPU-Efficient Networks

HRNet

Inception-V3

Inception-V4

Inception-ResNet-V2

NASNet-A

PNasNet-5

EfficientNet

MobileNet-V3

RegNet

RepVGG

ResNet, ResNeXt

Res2Net

ResNeSt

ReXNet

Selective-Kernel Networks

SelecSLS

Squeeze-and-Excitation Networks

TResNet

VGG

Vision Transformer

VovNet V2 and V1

Xception

Xception (Modified Aligned, Gluon)

Xception (Modified Aligned, TF)