Update README.md

README.md

@@ -18,6 +18,13 @@ This large corpus contains URLs, textual descriptions, embeddings of 75 million
 | l14_txt                          | Embedding of the textual description (768 dimensions) 
 | clip_l14_similarity_score        | Similarity between the image and text (higher values indicate higher similarity) 
 
+## How can this repository be downloaded?
+
+Simply use Git (or TortoiseGit):
+
+```
+git clone https://huggingface.co/datasets/sraimund/MapPool/
+```
 
 ## How can the parquet files be read?
 
@@ -79,7 +86,7 @@ Overall, downloading the CommonPool dataset, separating non-maps and uploading t
 
 ## What are the limitations?
 
-A qualitative inspection of the detected maps in the CommonPool dataset looks promising, however, it is not known what the actual accuracy is. Especially the false negative rate is hard to estimate due to the high number of non-maps among the CommonPool images. Mixtures between natural images and maps (e.g. a map printed on a bag, a map in a park) have not been further examined - ideally, those cases would be also classified as maps.
+A qualitative inspection of the detected maps in the CommonPool dataset looks promising, however, it is not known what the actual accuracy is. Especially the false negative rate is hard to estimate due to the high number of non-maps among the CommonPool images. Mixtures between natural images and maps (e.g. a map printed on a bag, a map in a park) have not been further examined. Also, duplicates or very similar map images have not been detected.
 
 Textual embeddings have not been considered in the separation process so far. The training dataset has a quite large variety of images, however, the textual descriptions may be too specific since the dataset originates only from Pinterest. Also, simply filtering by the word 'map' may lead to false positives as it has many meanings. Nevertheless, the textual embedding space may be explored in the future and possibly help to refine the visual classifier. 
 
@@ -88,5 +95,5 @@ It is planned to republish the training data and deploy the classification model
 ### Citation
 
 ```
-@inproceedings{Schnürer_2024, title={MapPool - Diving deep to bubble up a huge dataset for MapAI}, author={Schnürer, Raimund}, year={2024}}
+@inproceedings{Schnürer_2024, title={MapPool - Bubbling up an extremely large corpus of maps for AI}, author={Schnürer, Raimund}, year={2024}}
 ```