fix: minor typos in markdowns

README.md

@@ -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

UPGRADING.md

@@ -1,10 +1,10 @@
 # Upgrading from previous versions
 
-I generally try to maintain code interface and especially model weight compability across many `timm` versions. Sometimes there are exceptions.
+I generally try to maintain code interface and especially model weight compatibility across many `timm` versions. Sometimes there are exceptions.
 
 ## Checkpoint remapping
 
-Pretrained weight remapping is handled by `checkpoint_filter_fn` in a model implementation module. This remaps old pretrained checkpoints to new, and also 3rd party (original) checkpoints to `timm` format if the model was modified when brough into `timm`.
+Pretrained weight remapping is handled by `checkpoint_filter_fn` in a model implementation module. This remaps old pretrained checkpoints to new, and also 3rd party (original) checkpoints to `timm` format if the model was modified when brought into `timm`.
 
 The `checkpoint_filter_fn` is automatically called when loading pretrained weights via `pretrained=True`, but they can be called manually if you call the fn directly with the current model instance and old state dict.
 
@@ -19,6 +19,6 @@ Many changes were made since the 0.6.x stable releases. They were previewed in 0
   * The pretrained_tag is the specific weight variant (different head) for the architecture.
   * Using only `architecture` defaults to the first weights in the default_cfgs for that model architecture.
   * In adding pretrained tags, many model names that existed to differentiate were renamed to use the tag  (ex: `vit_base_patch16_224_in21k` -> `vit_base_patch16_224.augreg_in21k`). There are deprecation mappings for these.
-* A number of models had their checkpoints remaped to match architecture changes needed to better support `features_only=True`, there are `checkpoint_filter_fn` methods in any model module that was remapped. These can be passed to `timm.models.load_checkpoint(..., filter_fn=timm.models.swin_transformer_v2.checkpoint_filter_fn)` to remap your existing checkpoint.
+* A number of models had their checkpoints remapped to match architecture changes needed to better support `features_only=True`, there are `checkpoint_filter_fn` methods in any model module that was remapped. These can be passed to `timm.models.load_checkpoint(..., filter_fn=timm.models.swin_transformer_v2.checkpoint_filter_fn)` to remap your existing checkpoint.
 * The Hugging Face Hub (https://huggingface.co/timm) is now the primary source for `timm` weights. Model cards include link to papers, original source, license. 
 * Previous 0.6.x can be cloned from [0.6.x](https://github.com/rwightman/pytorch-image-models/tree/0.6.x) branch or installed via pip with version.