CAA 2023

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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.

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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

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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