From 9e327c993269b3f5f45c6ef90e61ed932db2338f Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Wed, 22 Mar 2023 15:40:10 -0700 Subject: [PATCH] Update README --- README.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/README.md b/README.md index 523aa1db..2ef6d886 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,20 @@ And a big thanks to all GitHub sponsors who helped with some of my costs before * ❗Updates after Oct 10, 2022 are available in 0.8.x pre-releases (`pip install --pre timm`) or cloning main❗ * Stable releases are 0.6.x and available by normal pip install or clone from [0.6.x](https://github.com/rwightman/pytorch-image-models/tree/0.6.x) branch. +### March 22, 2023 +* More weights pushed to HF hub along with multi-weight support, including: `regnet.py`, `rexnet.py`, `byobnet.py`, `resnetv2.py`, `swin_transformer.py`, `swin_transformer_v2.py`, `swin_transformer_v2_cr.py` +* Swin Transformer models support feature extraction (NCHW feat maps for `swinv2_cr_*`, and NHWC for all others) and spatial embedding outputs. +* FocalNet (from https://github.com/microsoft/FocalNet) models and weights added with significant refactoring, feature extraction, no fixed resolution / sizing constraint +* RegNet weights increased with HF hub push, SWAG, SEER, and torchvision v2 weights. SEER is pretty poor wrt to performance for model size, but possibly useful. +* More ImageNet-12k pretrained and 1k fine-tuned `timm` weights: + * `rexnetr_200.sw_in12k_ft_in1k` - 82.6 @ 224, 83.2 @ 288 + * `rexnetr_300.sw_in12k_ft_in1k` - 84.0 @ 224, 84.5 @ 288 + * `regnety_120.sw_in12k_ft_in1k` - 85.0 @ 224, 85.4 @ 288 + * `regnety_160.lion_in12k_ft_in1k` - 85.6 @ 224, 86.0 @ 288 + * `regnety_160.sw_in12k_ft_in1k` - 85.6 @ 224, 86.0 @ 288 (compare to SWAG PT + 1k FT this is same BUT much lower res, blows SEER FT away) +* Model name deprecation + remapping functionality added (a milestone for bringing 0.8.x out of pre-release). Mappings being added... +* Minor bug fixes and improvements. + ### Feb 26, 2023 * Add ConvNeXt-XXLarge CLIP pretrained image tower weights for fine-tune & features (fine-tuning TBD) -- see [model card](https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup) * Update `convnext_xxlarge` default LayerNorm eps to 1e-5 (for CLIP weights, improved stability) @@ -478,6 +492,7 @@ All model architecture families include variants with pretrained weights. There * TinyNet - https://arxiv.org/abs/2010.14819 * EVA - https://arxiv.org/abs/2211.07636 * FlexiViT - https://arxiv.org/abs/2212.08013 +* FocalNet (Focal Modulation Networks) - https://arxiv.org/abs/2203.11926 * GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959 * GhostNet - https://arxiv.org/abs/1911.11907 * gMLP - https://arxiv.org/abs/2105.08050