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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 []

NOTE: I am deprecating this version of the networks, the new ones are part of

TResNet []

VGG []

Vision Transformer []

VovNet V2 and V1 []

Xception []

Xception (Modified Aligned, Gluon) []

Xception (Modified Aligned, TF) []