Merge pull request #660 from petervandenabeele/readme_fix_typos

README: fix simple typos

Author: rwightman

README.md

@@ -39,7 +39,7 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 * Add CoaT models and weights. Thanks [Mohammed Rizin](https://github.com/morizin)
 * Add new ImageNet-21k weights & finetuned weights for TResNet, MobileNet-V3, ViT models. Thanks [mrT](https://github.com/mrT23)
 * Add GhostNet models and weights. Thanks [Kai Han](https://github.com/iamhankai)
-* Update ByoaNet attention modles
+* Update ByoaNet attention modules
    * Improve SA module inits
    * Hack together experimental stand-alone Swin based attn module and `swinnet`
    * Consistent '26t' model defs for experiments.
@@ -282,7 +282,7 @@ Several (less common) features that I often utilize in my projects are included.
     * PyTorch DistributedDataParallel w/ multi-gpu, single process (AMP disabled as it crashes when enabled)
     * PyTorch w/ single GPU single process (AMP optional)
 * A dynamic global pool implementation that allows selecting from average pooling, max pooling, average + max, or concat([average, max]) at model creation. All global pooling is adaptive average by default and compatible with pretrained weights.
-* A 'Test Time Pool' wrapper that can wrap any of the included models and usually provide improved performance doing inference with input images larger than the training size. Idea adapted from original DPN implementation when I ported (https://github.com/cypw/DPNs)
+* A 'Test Time Pool' wrapper that can wrap any of the included models and usually provides improved performance doing inference with input images larger than the training size. Idea adapted from original DPN implementation when I ported (https://github.com/cypw/DPNs)
 * Learning rate schedulers
   * Ideas adopted from
      * [AllenNLP schedulers](https://github.com/allenai/allennlp/tree/master/allennlp/training/learning_rate_schedulers)
@@ -329,7 +329,7 @@ The root folder of the repository contains reference train, validation, and infe
 
 ## Awesome PyTorch Resources
 
-One of the greatest assets of PyTorch is the community and their contributions. A few of my favourite resources that pair well with the models and componenets here are listed below.
+One of the greatest assets of PyTorch is the community and their contributions. A few of my favourite resources that pair well with the models and components here are listed below.
 
 ### Object Detection, Instance and Semantic Segmentation
 * Detectron2 - https://github.com/facebookresearch/detectron2
@@ -353,7 +353,7 @@ One of the greatest assets of PyTorch is the community and their contributions.
 ## Licenses
 
 ### Code
-The code here is licensed Apache 2.0. I've taken care to make sure any third party code included or adapted has compatible (permissive) licenses such as MIT, BSD, etc. I've made an effort to avoid any GPL / LGPL conflicts. That said, it is your responsibility to ensure you comply with license here and conditions of any dependent licenses. Where applicable, I've linked the sources/references for various components in docstrings. If you think I've missed anything please create an issue.
+The code here is licensed Apache 2.0. I've taken care to make sure any third party code included or adapted has compatible (permissive) licenses such as MIT, BSD, etc. I've made an effort to avoid any GPL / LGPL conflicts. That said, it is your responsibility to ensure you comply with licenses here and conditions of any dependent licenses. Where applicable, I've linked the sources/references for various components in docstrings. If you think I've missed anything please create an issue.
 
 ### Pretrained Weights
 So far all of the pretrained weights available here are pretrained on ImageNet with a select few that have some additional pretraining (see extra note below). ImageNet was released for non-commercial research purposes only (http://www.image-net.org/download-faq). It's not clear what the implications of that are for the use of pretrained weights from that dataset. Any models I have trained with ImageNet are done for research purposes and one should assume that the original dataset license applies to the weights. It's best to seek legal advice if you intend to use the pretrained weights in a commercial product.