README.md

metadata

title: Religious Artwork Analysis
emoji: 🕌
colorFrom: yellow
colorTo: purple
sdk: docker
app_port: 7860
short_description: Interactive t-SNE explorer for religious artworks

Religious Artwork Analysis

Code for "Analysis of Artworks Across Different Religions" — a study of whether (and which) visual features separate religious artworks of Buddhism, Christianity, Hinduism and Islam, using a hand-verified, balanced dataset of 3,997 paintings (~1,000 per religion).

• Dataset: Religious Artwork Dataset on Kaggle (3,997 images from 8 museum/archive sources, hand-verified labels, CC BY-NC 4.0)
• Live demo: interactive t-SNE explorer (see app/)

Key result

Semantic features transfer across museums; style features largely do not. Religion-classification accuracy (chance = 0.25):

feature family	dims	pooled 5-fold	leave-one-source-out
CLIP attribute scores	27	0.919	0.856
CLIP embedding	512	0.941	0.828
DINOv2 embedding	768	0.910	0.783
hand-crafted (all)	378	0.741	0.391
pose (main figure)	36	0.611	0.369
face composition	39	0.501	0.280

The pooled–LOSO gap measures source leakage: features that encode museum reproduction style (scan texture, framing) look discriminative pooled but collapse on unseen sources. Reproduce with evaluation/family_accuracy.py.

Setup

python -m venv venv && source venv/bin/activate
pip install -r requirements.txt

# 1. data (needs a Kaggle API token)
python data/download.py

# 2. preprocessing: guarded background masks (~1 h, CPU)
python preprocessing/generate_masks.py

# 3. features (each checkpointed & resumable)
python features/extract_handcrafted.py --workers 8
python features/extract_clip.py
python features/extract_dino.py
python features/extract_faces.py
python features/extract_pose.py

# 4. evaluation table
python evaluation/family_accuracy.py

# 5. web app
uvicorn app.main:app --port 8000

Repository layout

data/           dataset download + expected layout
preprocessing/  crop_padding + guarded U^2-Net background masking
features/       one extractor per feature family (see features/README.md)
evaluation/     pooled vs leave-one-source-out accuracy per family
app/            FastAPI + Plotly interactive t-SNE explorer

Authors

Timotej Cvikl & Žiga Klun — Faculty of Computer and Information Science, University of Ljubljana. Code under MIT license; dataset under CC BY-NC 4.0 (see the Kaggle page).