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CAA 2023

ClaReNet

This repository contains the code or links to repositories that were used for Clarenet's work on the hoard of Le Câtillon II in the paper: Supporting the analysis of a large coin hoard with AI-based methods 2023

In the Unsupervised Learning directory you will also find the parameters used for the work presented at the EAA2021.

Images

All coin images belong to Jersey Heritage. We are grateful to be working on them. You can have a look on the original dataset here

References

  • Albumentations: Buslaev, Alexander, Vladimir I. Iglovikov, Eugene Khvedchenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A. Kalinin. 2020. "Albumentations: Fast and Flexible Image Augmentations" Information 11, no. 2: 125. https://doi.org/10.3390/info11020125https://github.com/albumentations-team/albumentations
  • Heinecke, Andreas, Emanuel Mayer, Abhinav Natarajan, & Yoonju Jung (2021). Unsupervised Statistical Learning for Die Analysis in Ancient Numismatics. CoRR, abs/2112.00290.
  • Centernet: Kaiwen Duan, , Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, and Qi Tian. "CenterNet: Keypoint Triplets for Object Detection".CoRR abs/1904.08189 (2019).
  • DeepCluster: Caron, Mathilde, Piotr, Bojanowski, Armand, Joulin, and Matthijs, Douze. "Deep Clustering for Unsupervised Learning of Visual Features." . In European Conference on Computer Vision.2018. https://github.com/facebookresearch/deepcluster
  • GradCam Implementation
  • Heinecke, Andreas, Emanuel Mayer, Abhinav Natarajan, and Yoonju Jung. "Unsupervised Statistical Learning for Die Analysis in Ancient Numismatics".CoRR abs/2112.00290 (2021).
  • Keras Applications
  • LabelImg: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg
  • Lime : Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Why should i trust you?: Explaining the predictions of any classifier." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016.
  • Nonlinear total variation: Leonid I. Rudin, Emad Fatemi. "Nonlinear total variation based noise removal algorithms". Physica D: Nonlinear Phenomena (1992).
  • Nonlinear total variation Implementation
  • Shap: Lundberg, Scott M, and Su-In, Lee. "A Unified Approach to Interpreting Model Predictions.". In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2017. https://github.com/slundberg/shap
  • Taylor, Zachary McCord, "The Computer-Aided Die Study (CADS): A Tool for Conducting Numismatic Die Studies with Computer Vision and Hierarchical Clustering" (2020). Computer Science Honors Theses. 54.
  • TensorFlow 2 Model Zoo
  • Tensorflow 2 Object Detection API
  • Training Custom Object Detector
  • VGG16: Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs/1409.1556.
  • XRAI Implementation