vaepots
A Convolutional Variational Autoencoder for features extraction of archaeological pottery profiles
Science Score: 57.0%
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○Scientific vocabulary similarity
Low similarity (11.8%) to scientific vocabulary
Repository
A Convolutional Variational Autoencoder for features extraction of archaeological pottery profiles
Basic Info
- Host: GitHub
- Owner: lrncrd
- License: apache-2.0
- Language: HTML
- Default Branch: main
- Size: 40 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
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
Latex
@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:
bash
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
Owner
- Login: lrncrd
- Kind: user
- Repositories: 7
- Profile: https://github.com/lrncrd
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
VAEpots. A Convolutional Variational Autoencoder
for features extraction of archaeological pottery
profiles
message: >-
Supplementary material for the paper A deep
Variational Autoencoder for unsupervised features
extraction of ceramic profiles. A case study from
central Italy
type: software
authors:
- given-names: Lorenzo
family-names: Cardarelli