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README.md

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 license: cc-by-4.0
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-# MapPool
+# MapPool - Bubbling up an extremely large corpus of maps for AI
 
-This repository contains URLs, textual descriptions, embeddings of 75 million potential maps. It has been derived from the [CommonPool  dataset](https://huggingface.co/datasets/mlfoundations/datacomp_xlarge) from [DataComp](https://www.datacomp.ai/). The MapPool dataset may help to train resource-intensive architectures like Transformers or Diffusion Models in order to establish foundation models specialized on maps.
+This repository contains URLs, textual descriptions, embeddings of 50 million potential maps. It has been derived from the [CommonPool  dataset](https://huggingface.co/datasets/mlfoundations/datacomp_xlarge) from [DataComp](https://www.datacomp.ai/). The MapPool dataset may help to train resource-intensive architectures like Transformers or Diffusion Models in order to establish foundation models specialized on maps.
 
 ## How is the data structured?
 
@@ -82,7 +82,7 @@ The dataset is a subset of the [CommonPool  dataset (xlarge)](https://huggingfac
 
 Merely averaging the embeddings and calculating the nearest distance already reached the same accuracy as the two classification networks in [Schnürer et al. 2021](https://doi.org/10.1080/00087041.2020.1738112). Training models from [scikit](https://scikit-learn.org/) to distinguish maps and non-maps increased the validation accuracy even further. The highest accuracy has been achieved with a Support Vector Machine (SVM) with a polynomial kernel. 
 
-Overall, downloading the CommonPool dataset, separating non-maps and uploading the maps took about 50h for 10 CPUs and 120GB RAM on average as well as caused incoming network traffic of 500MB/s. SVMs are computationally the most demanding model; luckily, the inference speed could be improved by using an [Intel Extension](https://intel.github.io/scikit-learn-intelex). Classifying 500,000 embeddings took about 10 secs. 
+Overall, downloading the CommonPool dataset, separating non-maps and uploading the maps took about 50h for 10 CPUs and 120GB RAM on average as well as caused incoming network traffic of 500MB/s. SVMs are computationally the most demanding model among the examined ones; luckily, the inference speed could be improved by using an [Intel Extension](https://intel.github.io/scikit-learn-intelex). Classifying 500,000 embeddings took about 10 secs. 
 
 ## What are the limitations?