# 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.

## Squeeze-and-Excitation Networks [senet.py]

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