fix: minor typos in README

convnext_nano_r384
Ruida Zeng 2024-12-31 02:37:35 -06:00 committed by Ross Wightman
parent b0068ba5d0
commit 8fd2f48b65
1 changed files with 2 additions and 2 deletions

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@ -248,7 +248,7 @@ Add a set of new very well trained ResNet & ResNet-V2 18/34 (basic block) weight
### April 11, 2024
* Prepping for a long overdue 1.0 release, things have been stable for a while now.
* Significant feature that's been missing for a while, `features_only=True` support for ViT models with flat hidden states or non-std module layouts (so far covering `'vit_*', 'twins_*', 'deit*', 'beit*', 'mvitv2*', 'eva*', 'samvit_*', 'flexivit*'`)
* Above feature support achieved through a new `forward_intermediates()` API that can be used with a feature wrapping module or direclty.
* Above feature support achieved through a new `forward_intermediates()` API that can be used with a feature wrapping module or directly.
```python
model = timm.create_model('vit_base_patch16_224')
final_feat, intermediates = model.forward_intermediates(input)
@ -486,7 +486,7 @@ Included optimizers available via `timm.optim.create_optimizer_v2` factory metho
* `madgrad` an implementation of MADGRAD adapted from https://github.com/facebookresearch/madgrad - https://arxiv.org/abs/2101.11075
* `mars` MARS optimizer from https://github.com/AGI-Arena/MARS - https://arxiv.org/abs/2411.10438
* `nadam` an implementation of Adam w/ Nesterov momentum
* `nadamw` an impementation of AdamW (Adam w/ decoupled weight-decay) w/ Nesterov momentum. A simplified impl based on https://github.com/mlcommons/algorithmic-efficiency
* `nadamw` an implementation of AdamW (Adam w/ decoupled weight-decay) w/ Nesterov momentum. A simplified impl based on https://github.com/mlcommons/algorithmic-efficiency
* `novograd` by [Masashi Kimura](https://github.com/convergence-lab/novograd) - https://arxiv.org/abs/1905.11286
* `radam` by [Liyuan Liu](https://github.com/LiyuanLucasLiu/RAdam) - https://arxiv.org/abs/1908.03265
* `rmsprop_tf` adapted from PyTorch RMSProp by myself. Reproduces much improved Tensorflow RMSProp behaviour