Update README.md

Author: rwightman

README.md

@@ -23,6 +23,10 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 
 ## What's New
 
+### March 23, 2022
+* Add `ParallelBlock` and `LayerScale` option to base vit models to support model configs in [Three things everyone should know about ViT](https://arxiv.org/abs/2203.09795)
+* `convnext_tiny_hnf` (head norm first) weights trained with (close to) A2 recipe, 82.2% top-1, could do better with more epochs.
+
 ### March 21, 2022
 * Merge `norm_norm_norm`. **IMPORTANT** this update for a coming 0.6.x release will likely de-stabilize the master branch for a while. Branch `0.5.x` or a previous 0.5.x release can be used if stability is required.
 * Significant weights update (all TPU trained) as described in this [release](https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-tpu-weights)
@@ -45,7 +49,8 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
   * `resnetrs200` - 83.85 @ 256, 84.44 @ 320
 * HuggingFace hub support fixed w/ initial groundwork for allowing alternative 'config sources' for pretrained model definitions and weights (generic local file / remote url support soon)
 * SwinTransformer-V2 implementation added. Submitted by [Christoph Reich](https://github.com/ChristophReich1996). Training experiments and model changes by myself are ongoing so expect compat breaks.
-* MobileViT models w/ weights adapted from https://github.com/apple/ml-cvnets (
+* Swin-S3 (AutoFormerV2) models / weights added from https://github.com/microsoft/Cream/tree/main/AutoFormerV2
+* MobileViT models w/ weights adapted from https://github.com/apple/ml-cvnets
 * PoolFormer models w/ weights adapted from https://github.com/sail-sg/poolformer
 * VOLO models w/ weights adapted from https://github.com/sail-sg/volo
 * Significant work experimenting with non-BatchNorm norm layers such as EvoNorm, FilterResponseNorm, GroupNorm, etc
@@ -344,13 +349,16 @@ A full version of the list below with source links can be found in the [document
   * FBNet-V3 - https://arxiv.org/abs/2006.02049
   * HardCoRe-NAS - https://arxiv.org/abs/2102.11646
   * LCNet - https://arxiv.org/abs/2109.15099
+* MobileViT - https://arxiv.org/abs/2110.02178
 * NASNet-A - https://arxiv.org/abs/1707.07012
 * NesT - https://arxiv.org/abs/2105.12723
 * 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
 * RegNet - https://arxiv.org/abs/2003.13678
+* RegNetZ - https://arxiv.org/abs/2103.06877
 * RepVGG - https://arxiv.org/abs/2101.03697
 * ResMLP - https://arxiv.org/abs/2105.03404
 * ResNet/ResNeXt
@@ -367,12 +375,15 @@ A full version of the list below with source links can be found in the [document
 * ReXNet - https://arxiv.org/abs/2007.00992
 * SelecSLS - https://arxiv.org/abs/1907.00837
 * Selective Kernel Networks - https://arxiv.org/abs/1903.06586
+* 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
+* 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