Instructions to use nzs234/siglip2-so400m-aesthetic-scorer-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nzs234/siglip2-so400m-aesthetic-scorer-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nzs234/siglip2-so400m-aesthetic-scorer-v1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nzs234/siglip2-so400m-aesthetic-scorer-v1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 673 Bytes
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library_name: transformers
tags:
- image-classification
- aesthetic-scoring
- siglip2
- custom-code
license: other
---
# SigLIP2 Aesthetic Scorer (Local Bundle)
This repository contains a standalone local bundle for aesthetic scoring:
- Backbone: `google/siglip2-so400m-patch16-512` (saved locally)
- Head: custom MLP regressor (`head.safetensors`)
- Score range: `1..9`
## Files
- `backbone/` : local SigLIP2 backbone + processor files
- `head.safetensors` : MLP head weights
- `metadata.json` : model config and score range
## Output
The model outputs a continuous score, and common usage rounds it to integer `score_1 ... score_9`.
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