README.md

metadata

title: Iconclass 9B
emoji: 🖼️
colorFrom: indigo
colorTo: gray
sdk: gradio
sdk_version: 6.16.0
app_file: app.py
pinned: false
short_description: Classify artworks with Iconclass codes (9B vision model)

Reading pictures with Iconclass

Upload an artwork and a 9-billion-parameter vision-language model (davanstrien/qwen35-9b-iconclass-sft-multitask-2ep) predicts Iconclass codes for what the image depicts. Each code is decoded to plain English (via the iconclass Python package) and linked to its entry on iconclass.org.

The model was trained multi-task and the demo exposes all three modes:

1. Classify — the model decides which codes (and how many) apply.
2. Cataloguing depth — ask for exactly N codes (a broad 2-code record or a deep 8-code one).
3. Catalogue completion — paste the Iconclass codes a record already has; the model proposes additional codes only for aspects not yet covered. Most collections are partially catalogued (e.g. ~2 codes/object), so this matches the real cataloguing workflow.

Built with the gradio.Server pattern — a custom Tufte-styled HTML frontend over a ZeroGPU inference backend. The model loads on CPU at startup and runs on a ZeroGPU A10G inside the @spaces.GPU function (~7 s/inference).