Update README.md

README.md

@@ -62,7 +62,7 @@ The [demo notebook](./Megalith_Demo_Notebook.ipynb) shows a random sample of 100
 
 Based on this random sample, I would estimate the following dataset statistics:
 
-* 5-7% of images may have minor edits or annotatations (timestamps, color grading, borders, etc.)
+* 5-7% of images may have minor edits or annotations (timestamps, color grading, borders, etc.)
 * 1-2% of images may be copyright-constrained (watermarks or text descriptions cast doubt on the license metadata)
 * 1-2% of images may be non-wholesome (guns, suggestive poses, etc.)
 * 1-2% of images may be non-photos (paintings, screenshots, etc.)
@@ -70,7 +70,7 @@ Based on this random sample, I would estimate the following dataset statistics:
 ### Is 10 million images really enough to teach a neural network about the visual world?
 
 For the parts of the visual world that are well-represented in Megalith-10m, definitely!
-Projects like [CommonCanvas](https://arxiv.org/abs/2310.16825), [Mitsua Diffusion](https://huggingface.co/Mitsua/mitsua-diffusion-one), and [Matroyshka Diffusion](https://arxiv.org/abs/2310.15111)
+Projects like [CommonCanvas](https://arxiv.org/abs/2310.16825), [Mitsua Diffusion](https://huggingface.co/Mitsua/mitsua-diffusion-one), and [Matryoshka Diffusion](https://arxiv.org/abs/2310.15111)
 have shown that you can train useable generative models on similarly-sized image datasets.
 Of course, many parts of the world aren't well-represented in Megalith-10m, so you'd need additional data to learn about those.