---
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 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).

## Exemples of prediction on test images

Predictions on the test images for a [model](https://arxiv.org/abs/2008.01178) trained in a weakly supervised way on the train set. 



![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6582b7dd75754a803e484487/MRduPP4oXtSTXSI065DSn.jpeg)

## 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}
  }
```