VAEpots

A Convolutional Variational Autoencoder for features extraction of archaeological pottery profiles

The dataset includes about 5000 ceramic profiles from central tyrrhenian Italy.

Supplementary material for the paper A deep Variational Autoencoder for unsupervised features extraction of ceramic profiles. A case study from central Italy

Citation

@article{cardarelli_deep_2022,
	title = {A deep variational convolutional Autoencoder for unsupervised features extraction of ceramic profiles. A case study from central Italy},
	volume = {144},
	issn = {03054403},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S030544032200098X},
	doi = {10.1016/j.jas.2022.105640},
	pages = {105640},
	journaltitle = {Journal of Archaeological Science},
	shortjournal = {Journal of Archaeological Science},
	author = {Cardarelli, Lorenzo},
	urldate = {2022-07-10},
	date = {2022-08},
	langid = {english},
}

Site distribution

Distribution of some of the sites used in the analysis. Complete map in high-resolution here.

Sample dataset

A batch of profiles edited from archaeological drawings

Reconstruction examples

Reconstruced profiles from Test Set

Multivariate analysis

Multivariate analysis on Latent Dimension

Installation

Anaconda is recommended.

you can use the .yml file (VAEpots.yml) to create an Anaconda enviroment. Open Anaconda Prompt:

conda env create -f VAEpots.yml

for further information https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

the Pytorch library is not included and must be installed according to the requirements of your system (https://pytorch.org/)

Interactive scatterplot visualization

At the moment, you need to download the file (https://github.com/lrncrd/VAEpots/blob/main/Interactive_scatterplot.html) and open it locally. I am working on a better solution