Create README.md

README.md

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+---
+license: apache-2.0
+task_categories:
+- object-detection
+tags:
+- art
+size_categories:
+- 1K<n<10K
+---
+
+
+🖼️ The dataset **IconArt** dataset was introduced in the following paper : ["Weakly Supervised Object Detection in Artworks" Gonthier et al. ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018](https://arxiv.org/abs/1810.02569).
+This datasest is designed to evaluate Weakly Supervised object detection methods in paintings. 
+
+You can also find [project page for the paper here.](https://wsoda.telecom-paristech.fr/downloads/dataset/)
+
+This dataset contains 5955 images (from [WikiCommons](https://commons.wikimedia.org/wiki/Accueil)) : a train set of 2978 images and a test set of 2977 images (for classification task). 1480 of the 2977 test images are annotated with bounding boxes for 10 visual categories.
+The classes are ‘angel’,‘beard’,‘capital’,‘Child_Jesus’,‘crucifixion_of_Jesus’,‘Mary’,‘nudity’,‘ruins’,‘Saint_Sebastien’,‘turban’.
+
+[Most of the methods](https://arxiv.org/abs/2008.01178) only run evaluation on the easiest 7 classes : ‘angel’,‘Child_Jesus’,‘crucifixion_of_Jesus’,‘Mary’,‘nudity’, ‘ruins’,‘Saint_Sebastien’.
+
+In this folder you can find 3 other folders, the JPEGImages one contains the JPEG images. 
+The Annotations contain the bounding boxes in a PASCAL VOC template (XML file).
+The ImageSets/Main folder contain 3 files :
+- train.txt : contain the name of the images of the train set
+- test.txt : contain the name of the images annotated with an instance level
+- IconArt_v2.csv the class information per image (0 or 1 per class) but also if the image below to the train set or test one and then if the image is associated to bounding boxes annotations (Anno column).
+
+## Reference
+
+If you use IconArt please cite the related paper:
+```
+  @InProceedings{Gonthier_2018_ECCV_Workshops,
+  author = {Gonthier, Nicolas and Gousseau, Yann and Ladjal, Said and Bonfait, Olivier},
+  title = {Weakly Supervised Object Detection in Artworks},
+  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
+  month = {September},
+  year = {2018}
+  }
+```