diff --git a/README.md b/README.md index 9cd759b2b8f3691d6b1648d91966ca2f49348b3c..230a18a110c434139f521d41569587448218f060 100644 --- a/README.md +++ b/README.md @@ -12,24 +12,35 @@ This repository contains the materials presented in the paper 'An approach for d <a href="https://liris.cnrs.fr/page-membre/mihaela-scuturici">Mihaela Scuturici</a>, <a href="https://liris.cnrs.fr/page-membre/serge-miguet">Serge Miguet</a> -<div style="text-align:center"><img style="margin-right: 20px" src="assets/fig3.png" alt="Pipeline" height="75" width="160"/> +<div style="text-align:center"><img style="margin-right: 20px" src="assets/fig3.png" alt="Pipeline" height="275" width="560"/> # Table of content - [Overview](#description) +- [Dataset](#dataset) +- [Steps](#steps) - [Citation](#citation) - [Acknowledgements](#acknowledgments) -## Installation -Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection. +## Dataset -## Usage -Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README. +We provide the link to download the created extended version of DEArt dataset and the code for dataset extension. Created dataset is annotated in YOLO style. + +The new version of the dataset contains images from the 12th to 20th centuries in contrast with the original DEArt dataset with images from the 12th to 18th centuries. If it is necessary it is possible to restrict the period of the paintings by filtering images in the WikiArt dataset before dataset extension. In extended version images from WikiArt were used, so the new version contains not only paintings from European collections but also the paintings from Ukiyo-e - an ancient type of Japanese art and others. If needed you can create your own version of the dataset filtering styles using shared code of the dataset creation. + +DEArt_extended dataset can be downloaded here, subset with new classes can be downloaded here. Original DEArt dataset can be found here <a href="https://b2share.eudat.eu/records/449856a0b31543fc81baf19834f37d9d">DEArt</a>. + + +## Steps for reproducing the process of dataset creation +First you need to prepare two datasets. One small with the image-level annotations of classes which you plan to extend or add to the dataset, the second one - big non-annotated dataset from which we collect and annotate images on object level using proposed approach. +Next you need to train YOLO model using the original dataset which you want to extend, calculate objectnesses of the objects for the images from the big non-annotated dataset, using OWL-ViT2, create index file for the objectnesses using ANNOY. + +To reproduce our steps you need finetuned on the original DEArt dataset YOLO model, file with calculated objectnesses for the images from Wikiart dataset, and ANNOY index. These files are available upon a request. ## Roadmap If you have ideas for releases in the future, it is a good idea to list them in the README. ## Citation -@InProceedings{Guesdon_2021_ICCV, +@InProceedings{Yemelianenko_2024_ECCV, author = {Yemelianenko, Tetiana and Tkachenko, Iuliia and Masclef, Tess and Scuturici, Mihaela and Miguet, Serge}, title = {An approach for dataset extension for object detection in artworks using open-vocabulary models}, booktitle = {},