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Datasets:
davanstrien
/
iconclass-vlm-brillfull

Tasks:
Image Classification
Image-to-Text
Modalities:
Image
Text
Formats:
parquet
Size:
100K - 1M
Tags:
iconclass
art
lam
vlm
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
Dataset Viewer
The dataset viewer is not available for this dataset.
The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Iconclass VLM — brill full labels

Training-ready VLM iconclass-classification dataset rebuilt from the fuller, cleaner source labels in 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.
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Models trained or fine-tuned on davanstrien/iconclass-vlm-brillfull