Update README.md

README.md

@@ -35,7 +35,7 @@ For the datasets, I started as follows:
 + Once captioned, If the caption has "thumbs up", we replace it with `#thumbsup`, otherwise we attach the word `#thumbsup` to the caption.
 + If the model recognizes the person or says the word "man", we replace it with `<person>`. Otherwise, we attach the word `<person>` to the caption.
 + No-cap dataset: For the no-cap models, we don't use the captioning models. We simply add the `<person>` and the `#thumbsup` tag.
-+ Plain dataset: For the plain models, we leave the words as is.
++ Plain dataset: For the plain models, we leave the words as is - the "thumbs up" and the person name are without special characters.
 
 The wandb dashboard for the models are as follows:
 Initial experiments: I've tried training only on the thumbs up first. The results were good. The thumbs up was mostly accurate, with 4 fingers folded and the thumb raised. However, the model trained on sachin had several issues, including occlusion by cricket gear.