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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
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+# Image files - uncompressed
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+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+kamon_croissant.json filter=lfs diff=lfs merge=lfs -text
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+kamon_croissant_program_rm_holdout.json filter=lfs diff=lfs merge=lfs -text
+kamon_croissant_program_crm_holdout.json filter=lfs diff=lfs merge=lfs -text

LICENSE.txt

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+KamonBench is licensed under the Creative Commons
+Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
+
+To view a human-readable summary, see:
+  https://creativecommons.org/licenses/by-nc/4.0/
+
+The full legal code follows below, retrieved from:
+  https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt
+
+The KamonBench dataset is derived from Kamon motif assets in
+https://github.com/Rebolforces/kamondataset, which does not declare a license
+in its source repository. Users intending to use KamonBench beyond research
+should consult that upstream as well.
+
+=======================================================================
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+Attribution-NonCommercial 4.0 International
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+

README.md

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+---
+language:
+  - en
+  - ja
+license: cc-by-nc-4.0
+tags:
+  - image
+  - text
+  - synthetic
+  - compositional-generalization
+  - vision-language
+  - kamon
+pretty_name: KamonBench
+size_categories:
+  - 10K<n<100K
+task_categories:
+  - image-to-text
+viewer: false
+---
+
+# KamonBench
+
+A grammar-based image-to-structure benchmark for evaluating compositional
+factor recovery in vision-language models, built around Japanese family crests
+(_kamon_, 家紋).
+
+Each composite crest is paired with:
+
+- a formal kamon description language string (KDL, _kamon yōgo_, 家紋用語),
+- a segmented Japanese analysis,
+- an English translation,
+- a non-linguistic program code over the generator factors.
+
+Because every crest is synthesized from a known triple of generator factors
+(container `C`, modifier `R`, motif `M`), KamonBench supports direct factor
+metrics, controlled factor-pair recombination splits, counterfactual motif-
+sensitivity tests under fixed (container, modifier) contexts, and linear
+probes of factor accessibility from frozen representations. See the
+accompanying paper for details and baselines.
+
+The companion code (package, training and evaluation pipelines, and the
+generator) lives at
+[`SakanaAI/KamonBench`](https://github.com/SakanaAI/KamonBench).
+
+## Quick start
+
+```python
+from huggingface_hub import snapshot_download
+
+local_dir = snapshot_download(
+    repo_id="SakanaAI/KamonBench",
+    repo_type="dataset",
+)
+# Then unpack kamon_bench.zip into a `dataset01/` directory; each Croissant
+# file is paired with the archive (via SHA-256) and references images at
+# dataset01/*.png.
+```
+
+## Files
+
+| File                                       | Size   | Purpose                                                    |
+| ------------------------------------------ | ------ | ---------------------------------------------------------- |
+| `kamon_bench.zip`                          | 520 MB | Full PNG image set (54,116 PNGs under `dataset01/`)        |
+| `kamon_croissant.json`                     | 34 MB  | Main Croissant 1.0 + RAI metadata, with the standard split |
+| `kamon_croissant_program_cm_holdout.json`  | 22 MB  | Croissant variant: held-out (C, M) pairs                   |
+| `kamon_croissant_program_rm_holdout.json`  | 22 MB  | Croissant variant: held-out (R, M) pairs                   |
+| `kamon_croissant_program_crm_holdout.json` | 22 MB  | Croissant variant: held-out (C, R, M) triples              |
+| `LICENSE.txt`                              | —      | CC BY-NC 4.0 license text                                  |
+| `README.md`                                | —      | This card                                                  |
+
+The Croissant files live next to the archive (not inside it), because each
+file pins the archive's SHA-256.
+
+## Dataset structure
+
+The image archive contains 54,116 PNGs under `dataset01/`:
+
+| Slice                 | Count  | Description                                                                                          |
+| --------------------- | ------ | ---------------------------------------------------------------------------------------------------- |
+| Composite crests      | 20,000 | A container plus motif (with optional modifier), or a containerless spatial arrangement of one motif |
+| Base-motif components | 20,000 | One isolated base motif per composite                                                                |
+| Container components  | 14,116 | One isolated container per composite that uses one                                                   |
+
+Splits assign whole component groups together with their parent composite, so
+component records share the split of the composite they belong to.
+
+| Split | Composites | Components | Total  |
+| ----- | ---------- | ---------- | ------ |
+| train | 16,000     | 27,280     | 43,280 |
+| dev   | 2,000      | 3,405      | 5,405  |
+| test  | 2,000      | 3,431      | 5,431  |
+
+Each Croissant record in the `images` record set has these fields:
+
+| Field           | Description                                                    |
+| --------------- | -------------------------------------------------------------- |
+| `id`            | Unique image identifier                                        |
+| `image_path`    | Path to the PNG inside `dataset01/`                            |
+| `image`         | The PNG contents (resolved through the Croissant `cr:fileSet`) |
+| `description`   | Japanese KDL description                                       |
+| `translation`   | English translation                                            |
+| `analysis`      | Segmented Japanese analysis (list of `{expr, head}` entries)   |
+| `is_composite`  | Whether the record is a composite crest or a component         |
+| `component_ids` | For composites, the IDs of the linked component records        |
+| `split`         | `"train"`, `"dev"`, or `"test"`                                |
+
+For program-label experiments, the same images are paired with non-linguistic
+codes for the container (`C:NNN`), modifier (`X:N`), and motif (`M:NNN`); the
+three `*_holdout.json` Croissant variants reassign splits so that whole factor
+combinations (`(C, M)`, `(R, M)`, or `(C, R, M)`) are absent from training,
+while the underlying primitive tokens still appear individually in training.
+
+## Recombination splits
+
+The three holdout variants share the same images as the main file but reassign
+the train/dev/test labels so that every test composite contains a held-out
+factor combination not seen during training. Primitive tokens remain
+represented in training, so the test isolates the question of whether a model
+can bind familiar primitives in novel combinations rather than recall whole
+crests.
+
+## Limitations and intended use
+
+- KamonBench is a research benchmark for compositional visual recognition,
+  factor-aware evaluation, and representation analysis. It is not an
+  authoritative cultural or historical catalogue of _kamon_.
+- The crests are synthetically rendered from upstream motif assets; they
+  differ in style and polish from professionally rendered crests and do not
+  cover the full distribution of historical traditions.
+- The released generator uses a limited grammar (one level of containment, a
+  fixed set of containers and modifiers).
+- See `rai:dataLimitations`, `rai:dataBiases`, and `rai:dataSocialImpact` in
+  the Croissant metadata for the formal RAI description.
+
+## License
+
+The dataset is released under
+[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/);
+see `LICENSE.txt` for the full legal code. The companion code is released
+under the MIT License.
+
+The component images bundled with KamonBench (one isolated motif per
+composite and one container per contained composite) are repackaged in PNG
+form from the _Rebolforces kamondataset_, a publicly available collection of
+Japanese kamon motifs originally scraped from a catalogue website that is no
+longer accessible online (preserved via the Internet Archive); upstream
+provenance cannot be tracked further. We make no copyright claim over those
+source images and release KamonBench solely for non-commercial research use.
+
+## Citation
+
+```bibtex
+@misc{kamonbench2026,
+  title  = {KamonBench: A Grammar-Based Dataset for Evaluating Compositional Factor Recovery in Vision-Language Models},
+  author = {Sproat, Richard and Peluchetti, Stefano},
+  year   = {2026},
+  note   = {arXiv preprint},
+}
+```

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