From fa26f6c1cfa35edbe75480541b3f77d1815b7efa Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Wed, 12 Aug 2020 23:26:59 -0700 Subject: [PATCH] Update README.md --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index d3dfef32..df5c0ca0 100644 --- a/README.md +++ b/README.md @@ -152,8 +152,8 @@ Several (less common) features that I often utilize in my projects are included. * `fused` optimizers by name with [NVIDIA Apex](https://github.com/NVIDIA/apex/tree/master/apex/optimizers) installed * `adamp` and `sgdp` by [Naver ClovAI](https://github.com/clovaai) (https://arxiv.org/abs/2006.08217) * Random Erasing from [Zhun Zhong](https://github.com/zhunzhong07/Random-Erasing/blob/master/transforms.py) (https://arxiv.org/abs/1708.04896) -* Mixup (as in https://arxiv.org/abs/1710.09412) - currently implementing/testing -* An inference script that dumps output to CSV is provided as an example +* Mixup (https://arxiv.org/abs/1710.09412) +* CutMix (https://arxiv.org/abs/1905.04899) * AutoAugment (https://arxiv.org/abs/1805.09501) and RandAugment (https://arxiv.org/abs/1909.13719) ImageNet configurations modeled after impl for EfficientNet training (https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py) * AugMix w/ JSD loss (https://arxiv.org/abs/1912.02781), JSD w/ clean + augmented mixing support works with AutoAugment and RandAugment as well * SplitBachNorm - allows splitting batch norm layers between clean and augmented (auxiliary batch norm) data @@ -170,3 +170,7 @@ Model validation results can be found in the [documentation](https://rwightman.g ## Getting Started See the [documentation](https://rwightman.github.io/pytorch-image-models/) + +## Train, Validation, Inference Scripts + +The root folder of the repository contains reference train, validation, and inference scripts that work with the included models and other features of this repository. They are adaptable for other datasets and use cases with a little hacking. See [documentation](https://rwightman.github.io/pytorch-image-models/scripts/) for some basics and [training hparams](https://rwightman.github.io/pytorch-image-models/training_hparam_examples) for some train examples that produce SOTA ImageNet results.