---
license: cc0-1.0
task_categories:
  - image-classification
  - image-to-text
size_categories:
  - 10K<n<100K
tags:
  - iconclass
  - art
  - lam
  - vlm
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: test
        path: data/test-*.parquet
  - config_name: sft
    data_files:
      - split: train
        path: data/train-*.parquet
---

# Iconclass VLM — brill full labels

Training-ready VLM iconclass-classification dataset rebuilt from the fuller, cleaner
source labels in [`biglam/brill_iconclass`](https://huggingface.co/datasets/biglam/brill_iconclass)
(CC0). Recovers labels lost to truncation in `davanstrien/iconclass-vlm-sft`.

- **Source images**: same Brill Arkyves images as `biglam/brill_iconclass`, bytes passed through verbatim (no re-encode).
- **Labels**: full Iconclass codes with operators `(+n)`, key-combos `:`, and qualifiers `(TEXT)` kept intact. Empty/sentinel tokens stripped; ~5 malformed free-text codes dropped.
- **Avg codes/image**: 4.359 (vs 3.542 in the truncated `iconclass-vlm-sft`).
- **Label cap**: codes/image capped at 20 (1446 images had their long tail trimmed).
- **License**: CC0-1.0.

## Splits

| Split | Images |
|-------|-------:|
| train | 86216 |
| test  | 788 |

The **test** split is a contamination-safe held-out set: images are assigned to
train/test by a deterministic hash of the Brill filename, so the two image sets are
guaranteed disjoint and the split is fully reproducible.

## Schema (drop-in compatible)

Every row carries a superset of fields so the existing training/eval scripts work unchanged:

| Column | Type | Consumer |
|--------|------|----------|
| `image` | `Image` | `train_grpo.py` (raw image + label) |
| `label` | `list[str]` | `train_grpo.py` ground truth |
| `images` | `list[Image]` | `train_sft.py` / `eval_sft.py` |
| `messages` | conversational | `train_sft.py` / `eval_sft.py` |

The assistant message content is `{"iconclass-codes": [...]}`; the user turn uses the
same instruction string as `train_grpo.py`.

### Configs
- **`default`** — `train` + `test` splits.
- **`sft`** — `train` split (same rows as `default/train`), for `train_grpo.py --dataset-config sft` and `train_sft.py`.

## Build

Built by `build_brill_dataset.py`. Label cap: 20 codes/image.

## Research context & key finding

This dataset was built to test whether the iconclass classifier's ~25% recall ceiling was
caused by **truncated training labels** (the original `iconclass-vlm-sft` was capped at 3.54
codes/image; this restores the full Brill labels at ~4.36).

**Re-SFT on these fuller labels did _not_ improve the model.** Training converged well
(eval_loss 0.47) but on the contamination-safe `test` split it scored H-F1 45.3 /
hier-recall 46.4 — recall unchanged. The bottleneck is model **capability** (identifying the
right codes), not label completeness. The lever that *did* work was **anchored fusion** (the
fine-tuned model as a precision anchor + a graded VLM-judge gating in semantic-retrieval
recall → H-F1 47.5 / hier-recall 57.6, with no extra training).

### Splits & contamination
- `train` (86,216) / `test` (788), split deterministically by image filename hash (disjoint).
- The `test` split is clean for models trained on this dataset's `train` split. Older
  checkpoints trained on the overlapping `iconclass-vlm-sft` images are **contaminated** on it.