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README.md

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 # MapPool - Bubbling up an extremely large corpus of maps for AI
+(early access version)
 
 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.
 
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 ## How was this dataset created?
 
-The dataset is a subset of the [CommonPool  dataset (xlarge)](https://huggingface.co/datasets/mlfoundations/datacomp_xlarge), which consists of 10,000,000,000 images. To filter the data, a classifier was established based on the embeddings of 1,860 maps and 1,860 non-maps and evaluated on 1,240 maps and 1,240 non-maps. This map dataset has been collected by [Schnürer et al. 2021](https://doi.org/10.1080/00087041.2020.1738112). The embeddings were generated by a pre-trained vision transformer on OpenAI data ([OpenCLIP](https://github.com/mlfoundations/open_clip)). Afterwards, different methods were tested to classify the embeddings:
+The dataset is a subset of the [CommonPool  dataset (xlarge)](https://huggingface.co/datasets/mlfoundations/datacomp_xlarge), which consists of 10 billion images. To filter the data, a classifier was established based on the embeddings of 1,860 maps and 1,860 non-maps and evaluated on 1,240 maps and 1,240 non-maps. This map dataset has been collected by [Schnürer et al. 2021](https://doi.org/10.1080/00087041.2020.1738112). The embeddings were generated by a pre-trained vision transformer on OpenAI data ([OpenCLIP](https://github.com/mlfoundations/open_clip)). Afterwards, different methods were tested to classify the embeddings:
 
 | Model                                         | Accuracy 
 |-----------------------------------------------|----------