Datasets:

sraimund
/

MapPool

@@ -115,6 +115,10 @@ MapPool has been created by classifying the image embeddings included in [Common
 With the Support Vector Machine, 500,000 image embeddings could be classified within 10 seconds. Downloading, classifying the whole dataset, and uploading the results took about 50 hours with 10 CPUs, 120GB RAM, and 500MB/s of network traffic on average.
 ## What are the limitations?
 A qualitative inspection of the detected maps 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.
@@ -125,7 +129,7 @@ Textual embeddings have not been considered in the separation process so far. Th
 A detailed analysis of the content and metadata of maps in MapPool, potentially resulting in a search engine, is the subject of future work. Additionally, the visual and textual embedding space may be explored to refine the map classifier and to detect duplicates among the images. It can be examined whether training with map-only images leads to better results for cartographic tasks, for instance generating maps based on textual prompts, than with a mixture of maps and other images.
-Feel free to contact [me](https://people.epfl.ch/raimund.schnurer) in case you like to collaborate!
 ## Disclaimer

 With the Support Vector Machine, 500,000 image embeddings could be classified within 10 seconds. Downloading, classifying the whole dataset, and uploading the results took about 50 hours with 10 CPUs, 120GB RAM, and 500MB/s of network traffic on average.
+## Is the inference model available?
+Yes, try it out and download it here: [https://huggingface.co/spaces/sraimund/MapPool](https://huggingface.co/spaces/sraimund/MapPool)
 ## What are the limitations?
 A qualitative inspection of the detected maps 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.
 A detailed analysis of the content and metadata of maps in MapPool, potentially resulting in a search engine, is the subject of future work. Additionally, the visual and textual embedding space may be explored to refine the map classifier and to detect duplicates among the images. It can be examined whether training with map-only images leads to better results for cartographic tasks, for instance generating maps based on textual prompts, than with a mixture of maps and other images.
+Feel free to contact [me](https://schnuerer.dev/contact/) in case you like to collaborate!
 ## Disclaimer