Silva Aesthetic

--- 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 --- <p align="center"> <img src="https://raw.githubusercontent.com/Jannchie/silva/main/assets/silva-header.png" alt="SILVA" width="100%"> </p> # SILVA — Personal Aesthetic Head **[▶ Try it in your browser](https://huggingface.co/spaces/Jannchie/silva-aesthetic-demo)** — 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 ```python # 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: ```python # 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](https://github.com/Jannchie/silva) ## Citation ```bibtex @software{pan2026silva, author =...