Update openclip_README.md spelling Correction

openclip_README.md

@@ -2,22 +2,22 @@
 
 [[Paper]](https://arxiv.org/abs/2109.01903) [[Colab]](https://colab.research.google.com/github/mlfoundations/open_clip/blob/master/docs/Interacting_with_open_clip.ipynb)
 
-Welcome to an open source implementation of OpenAI's [CLIP](https://arxiv.org/abs/2103.00020) (Contrastive Language-Image Pre-training).
+Welcome to an open-source implementation of OpenAI's [CLIP](https://arxiv.org/abs/2103.00020) (Contrastive Language-Image Pre-training).
 
-The goal of this repository is to enable training models with contrastive image-text supervision, and to investigate their properties such as robustness to distribution shift. Our starting point is an implementation of CLIP that matches the accuracy of the original CLIP models when trained on the same dataset.
-Specifically, a ResNet-50 model trained with our codebase on OpenAI's [15 million image subset of YFCC](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md) achieves **32.7%** top-1 accuracy on ImageNet. OpenAI's CLIP model reaches **31.3%** when trained on the same subset of YFCC. For ease of experimentation, we also provide code for training on the 3 million images in the [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/download) dataset, where a ResNet-50x4 trained with our codebase reaches 22.2% top-1 ImageNet accuracy.
+The goal of this repository is to enable training models with contrastive image-text supervision and to investigate their properties, such as robustness to distribution shift. Our starting point is an implementation of CLIP that matches the accuracy of the original CLIP models when trained on the same dataset.
+Specifically, a ResNet-50 model trained with our codebase on OpenAI's [15 million image subset of YFCC](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md) achieves **32.7%** top-1 accuracy on ImageNet. OpenAI's CLIP model reaches 31.3% when trained on the same subset of YFCC. For ease of experimentation, we also provide code for training on the 3 million images in the [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/download) dataset, where a ResNet-50x4 trained with our codebase reaches 22.2% top-1 ImageNet accuracy.
 
-We further this with a replication study on a dataset of comparable size to OpenAI's. Using [LAION-400M](https://arxiv.org/abs/2111.02114), we train CLIP with a
+We furthered this with a replication study on a dataset of comparable size to OpenAI's. Using [LAION-400M](https://arxiv.org/abs/2111.02114), we train CLIP with a
   * ViT-B/32 and achieve an accuracy of **62.9%**, comparable to OpenAI's **63.2%**, zero-shot top-1 on ImageNet1k
   * ViT-B/16 and achieve an accuracy of **67.1%**, comparable to OpenAI's **68.3%** (as measured here, 68.6% in paper)
   * ViT-B/16+ 240x240 (~50% more FLOPS than B/16 224x224) and achieve an accuracy of **69.2%**
   * ViT-L/14 and achieve an accuracy of **72.77%**, vs OpenAI's **75.5%** (as measured here, 75.3% in paper)
 
 As we describe in more detail [below](#why-are-low-accuracy-clip-models-interesting), CLIP models in a medium accuracy regime already allow us to draw conclusions about the robustness of larger CLIP models since the models follow [reliable scaling laws](https://arxiv.org/abs/2107.04649).
 
-This codebase is work in progress, and we invite all to contribute in making it more acessible and useful. In the future, we plan to add support for TPU training and release larger models. We hope this codebase facilitates and promotes further research in contrastive image-text learning. Please submit an issue or send an email if you have any other requests or suggestions.
+This codebase is work in progress, and we invite all to contribute in making it more accessible and useful. In the future, we plan to add support for TPU training and release larger models. We hope this codebase facilitates and promotes further research in contrastive image-text learning. Please submit an issue or send an email if you have any other requests or suggestions.
 
-Note that portions of `src/open_clip/` modelling and tokenizer code are adaptations of OpenAI's official [repository](https://github.com/openai/CLIP).
+Note that portions of `src/open_clip/` modeling and tokenizer code are adaptations of OpenAI's official [repository](https://github.com/openai/CLIP).
 
 ## Approach
 
@@ -76,7 +76,7 @@ Our training set contains 2.89M images, and our validation set contains 13K imag
 
 ### YFCC and other datasets
 
-In addition to specifying the training data via CSV files as mentioned above, our codebase also supports [webdataset](https://github.com/webdataset/webdataset), which is recommended for larger scale datasets. The expected format is a series of `.tar` files. Each of these `.tar` files should contain two files for each training example, one for the image and one for the corresponding text. Both files should have the same name but different extensions. For instance, `shard_001.tar` could contain files such as `abc.jpg` and `abc.txt`. You can learn more about `webdataset` at [https://github.com/webdataset/webdataset](https://github.com/webdataset/webdataset). We use `.tar` files with 1,000 data points each, which we create using [tarp](https://github.com/webdataset/tarp).
+In addition to specifying the training data via CSV files as mentioned above, our codebase also supports [webdataset](https://github.com/webdataset/webdataset), which is recommended for larger-scale datasets. The expected format is a series of `.tar` files. Each of these `.tar` files should contain two files for each training example, one for the image and one for the corresponding text. Both files should have the same name but different extensions. For instance, `shard_001.tar` could contain files such as `abc.jpg` and `abc.txt`. You can learn more about `webdataset` at [https://github.com/webdataset/webdataset](https://github.com/webdataset/webdataset). We use `.tar` files with 1,000 data points each, which we create using [tarp](https://github.com/webdataset/tarp).
 
 You can download the YFCC dataset from [Multimedia Commons](http://mmcommons.org/).
 Similar to OpenAI, we used a subset of YFCC to reach the aforementioned accuracy numbers.
@@ -113,7 +113,7 @@ Test can be run with `make install-dev` then `make test`
 
 #### Other dependencies
 
-Install open_clip pacakge and remaining dependencies:
+Install open_clip package and remaining dependencies:
 
 ```bash
 cd open_clip
@@ -145,15 +145,15 @@ python -m training.main \
 ```
 
 Note: `imagenet-val` is the path to the *validation* set of ImageNet for zero-shot evaluation, not the training set!
-You can remove this argument if you do not want to perform zero-shot evaluation on ImageNet throughout training. Note that the `val` folder should contain subfolders. If it doest not, please use [this script](https://github.com/raw/soumith/imagenetloader.torch/master/valprep.sh).
+You can remove this argument if you do not want to perform zero-shot evaluation on ImageNet throughout training. Note that the `val` folder should contain subfolders. If it does not, please use [this script](https://github.com/raw/soumith/imagenetloader.torch/master/valprep.sh).
 
 ### Multi-GPU and Beyond
 
 This code has been battle tested up to 1024 A100s and offers a variety of solutions
 for distributed training. We include native support for SLURM clusters.
 
 As the number of devices used to train increases, so does the space complexity of
-the the logit matrix. Using a naïve all-gather scheme, space complexity will be
+the logit matrix. Using a naïve all-gather scheme, space complexity will be
 `O(n^2)`. Instead, complexity may become effectively linear if the flags
 `--gather-with-grad` and `--local-loss` are used. This alteration results in one-to-one
 numerical results as the naïve method.