library_name: silva
pipeline_tag: image-classification
license: mit
base_model: google/siglip2-so400m-patch14-384
base_model_relation: adapter
tags:
- aesthetic
- siglip2
- ordinal-regression
metrics:
- spearmanr
- pearsonr
- mae
model-index:
- name: silva-aesthetic
results:
- task:
type: image-classification
name: Personal Aesthetic Scoring
metrics:
- type: spearmanr
value: 0.7671
- type: pearsonr
value: 0.7695
- type: mae
value: 0.4859
SILVA — Personal Aesthetic Head
▶ Try it in your browser — upload an illustration, see this person's score live.
Scores an illustration by one specific person's taste — not a universal quality
model, so it won't match anyone else's preferences. Output is a single number in
[0, 1]; higher means more to this person's liking.
Only the head ships here (~7 MB), not an image model. It runs on top of the frozen
google/siglip2-so400m-patch14-384 backbone, which silva[backbone] installs and loads for you.
Quickstart
# pip install "silva-scorer[backbone] @ git+https://github.com/Jannchie/silva"
from silva import SilvaScorer
scorer = SilvaScorer.from_pretrained("Jannchie/silva-aesthetic")
print(scorer.score("your_image.jpg")) # 0.73
print(scorer.score(["a.jpg", "b.jpg"])) # [0.73, 0.41]
Already have google/siglip2-so400m-patch14-384 embeddings? Skip the backbone and score them directly:
# pip install "silva-scorer @ git+https://github.com/Jannchie/silva"
from silva import EmbeddingAestheticModel
head = EmbeddingAestheticModel.from_pretrained("Jannchie/silva-aesthetic").eval()
score = head(embedding)["calibrated_score"] # calibrated to the label distribution; ["score"] for raw. embedding: [B, 1152] pooler_output
Scores (held-out test split)
| Spearman | Pearson | MAE (1–5) | Top-5% |
|---|---|---|---|
| 0.7671 | 0.7695 | 0.4859 | 0.4191 |
Architecture: embedding[1152] → LayerNorm → MLP [1024, 512, 256] → ordinal head. Trained on one
person's private 1–5 ratings; labels and images not released. Source
Citation
@software{pan2026silva,
author = {Pan, Jianqi},
title = {{SILVA}: {SigLIP}-based Illustration Visual Aesthetic Scorer},
year = {2026},
url = {https://github.com/Jannchie/silva},
}