ShapeCodeBench: A Renewable Benchmark for Perception-to-Program Reconstruction of Synthetic Shape Scenes
Paper • 2605.11680 • Published • 1
image imagewidth (px) 512 512 | image_file_name stringlengths 32 36 | image_sha256 stringlengths 64 64 | sample_id stringlengths 18 20 | split stringclasses 1
value | difficulty stringclasses 3
values | seed int64 0 49 | image_size int64 512 512 | num_shapes int64 1 10 | shape_inventory dict | ground_truth_program stringlengths 38 422 | render_config dict |
|---|---|---|---|---|---|---|---|---|---|---|---|
eval/easy/eval-easy-00000000.png | f4902623c3b6b538f453d85b8c2422da4beed767ff21fc613f12de8b8c8e504f | eval-easy-00000000 | eval | easy | 0 | 512 | 2 | {
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eval/easy/eval-easy-00000001.png | 0d2d5a88501bd0cd845c2d749502f6f5817a1651d6ed1008d6e51c5cdf2c7720 | eval-easy-00000001 | eval | easy | 1 | 512 | 1 | {
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eval/easy/eval-easy-00000002.png | 5e164ddb097344431cd06f65b75b6281c08956a057ea6abfb6cd34c822b9a34a | eval-easy-00000002 | eval | easy | 2 | 512 | 1 | {
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eval/easy/eval-easy-00000003.png | 52252e219575293ffea321e8a18d706953498b184729de2eb192d680f8b9d633 | eval-easy-00000003 | eval | easy | 3 | 512 | 1 | {
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eval/easy/eval-easy-00000004.png | 9f21e9c06ca55c5d5a9d46c5b56dabf2938d0893f7ed95aacef0dd47fafeb2f1 | eval-easy-00000004 | eval | easy | 4 | 512 | 1 | {
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eval/easy/eval-easy-00000005.png | c79e39de0849cc78ceabad70bb2167a63f6b037111d98ca02a55ece5cf7090c3 | eval-easy-00000005 | eval | easy | 5 | 512 | 3 | {
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eval/easy/eval-easy-00000006.png | eed6409b2a295e2f489a4a9a026c85c4b65e512cc4145135dfb0facb431c8f00 | eval-easy-00000006 | eval | easy | 6 | 512 | 3 | {
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eval/easy/eval-easy-00000007.png | 08281c1fa10d6725782295a3f7363d82b012dc70e4cc4ca0960b1d9250f07bd0 | eval-easy-00000007 | eval | easy | 7 | 512 | 2 | {
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eval/easy/eval-easy-00000008.png | fa6e8e2ad5a9c5153831d7931f5f3be3837e41246211cf952883f9398edcfe7d | eval-easy-00000008 | eval | easy | 8 | 512 | 1 | {
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eval/easy/eval-easy-00000009.png | 9391da9ca5e81fa965ce84a2448f0cc2d5f4e17b4c33a3951ee8a75cf27e0635 | eval-easy-00000009 | eval | easy | 9 | 512 | 2 | {
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eval/easy/eval-easy-00000015.png | 3387fcb0f62a5b2b681f61748035e7941f94bdf6361798c6cddce30e91adbeee | eval-easy-00000015 | eval | easy | 15 | 512 | 1 | {
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filled_square(cx=110, cy=296, size=56)
filled_circle(cx=395, cy=98, radius=60... | {
"background_color": 255,
"canvas_size": 512,
"foreground_color": 0,
"image_mode": "L",
"renderer_semantics": "v1-raster-pillow"
} | |
eval/hard/eval-hard-00000045.png | 0d78f42994d5ed6fcbc27164a290470b5716d256b1287c1f5311c4888c33fe19 | eval-hard-00000045 | eval | hard | 45 | 512 | 8 | {
"circle": 1,
"filled_circle": 3,
"filled_square": 3,
"square": 1
} | square(cx=263, cy=83, size=78, stroke=5)
filled_square(cx=74, cy=495, size=18)
filled_circle(cx=290, cy=120, radius=30)
filled_square(cx=36, cy=68, size=125)
circle(cx=157, cy=216, radius=50, stroke=7)
filled_circle(cx=417, cy=146, radius=94)
filled_circle(cx=176, cy=328, radius=20)
filled_square(cx=327, cy=96, size=61... | {
"background_color": 255,
"canvas_size": 512,
"foreground_color": 0,
"image_mode": "L",
"renderer_semantics": "v1-raster-pillow"
} | |
eval/hard/eval-hard-00000046.png | 910c45c55dbd43ab2c1abac2fa747d16db96c6b88194b4295d4cae6205034581 | eval-hard-00000046 | eval | hard | 46 | 512 | 10 | {
"circle": 2,
"filled_circle": 4,
"filled_square": 2,
"square": 2
} | filled_circle(cx=231, cy=344, radius=71)
square(cx=281, cy=156, size=115, stroke=2)
filled_square(cx=70, cy=343, size=128)
square(cx=415, cy=188, size=127, stroke=7)
filled_square(cx=208, cy=117, size=61)
circle(cx=321, cy=66, radius=94, stroke=5)
filled_circle(cx=187, cy=374, radius=63)
circle(cx=194, cy=67, radius=10... | {
"background_color": 255,
"canvas_size": 512,
"foreground_color": 0,
"image_mode": "L",
"renderer_semantics": "v1-raster-pillow"
} | |
eval/hard/eval-hard-00000047.png | b47b5a3ace3b54d3611cb68c40fceae4615e50751d856f3803bdcd9a9864300f | eval-hard-00000047 | eval | hard | 47 | 512 | 8 | {
"circle": 1,
"filled_circle": 5,
"filled_square": 2,
"square": 0
} | filled_circle(cx=464, cy=351, radius=71)
filled_square(cx=395, cy=401, size=81)
filled_circle(cx=428, cy=24, radius=117)
filled_circle(cx=270, cy=493, radius=16)
filled_circle(cx=323, cy=437, radius=120)
circle(cx=367, cy=452, radius=46, stroke=4)
filled_circle(cx=383, cy=361, radius=87)
filled_square(cx=200, cy=244, s... | {
"background_color": 255,
"canvas_size": 512,
"foreground_color": 0,
"image_mode": "L",
"renderer_semantics": "v1-raster-pillow"
} | |
eval/hard/eval-hard-00000048.png | 63aa6f1d224b84fa6b218bc98549f7bc6a3154848d59e5748b3e858af486600e | eval-hard-00000048 | eval | hard | 48 | 512 | 10 | {
"circle": 3,
"filled_circle": 1,
"filled_square": 4,
"square": 2
} | filled_square(cx=309, cy=197, size=32)
square(cx=158, cy=110, size=37, stroke=8)
circle(cx=158, cy=483, radius=43, stroke=4)
filled_circle(cx=433, cy=65, radius=25)
circle(cx=166, cy=131, radius=79, stroke=1)
filled_square(cx=345, cy=97, size=59)
circle(cx=271, cy=14, radius=50, stroke=10)
filled_square(cx=141, cy=360,... | {
"background_color": 255,
"canvas_size": 512,
"foreground_color": 0,
"image_mode": "L",
"renderer_semantics": "v1-raster-pillow"
} | |
eval/hard/eval-hard-00000049.png | b12e259d449beb7b52d56aca768e4535faa8214803b2e65ead67c1ba32eff96c | eval-hard-00000049 | eval | hard | 49 | 512 | 6 | {
"circle": 1,
"filled_circle": 2,
"filled_square": 2,
"square": 1
} | filled_square(cx=113, cy=331, size=68)
filled_circle(cx=45, cy=282, radius=103)
circle(cx=46, cy=224, radius=74, stroke=10)
filled_square(cx=380, cy=409, size=89)
filled_circle(cx=141, cy=98, radius=47)
square(cx=385, cy=334, size=109, stroke=6) | {
"background_color": 255,
"canvas_size": 512,
"foreground_color": 0,
"image_mode": "L",
"renderer_semantics": "v1-raster-pillow"
} |
End of preview. Expand in Data Studio
ShapeCodeBench eval_v1
This dataset is the frozen eval_v1 reporting split for
ShapeCodeBench, a synthetic
benchmark for testing whether multimodal models can reconstruct executable
drawing programs from rendered shape images.
It contains 150 grayscale 512x512 PNG images: 50 easy, 50 medium, and
50 hard examples. Each row includes the rendered image, the canonical
ShapeCodeBench DSL program that generated it, generation metadata, render
configuration, and the SHA256 checksum for the source PNG.
Zenodo remains the archival release DOI: https://doi.org/10.5281/zenodo.20132286. This Hugging Face dataset is a discoverable and loadable mirror of the frozen evaluation split.
Load
from datasets import load_dataset
dataset = load_dataset("shivamk3r/shape-code-bench-eval-v1")
Columns
image: rendered target image.image_file_name: original path underdata/eval_v1.image_sha256: checksum fromSHA256SUMS.sample_id,split,difficulty,seed: sample identity and generation seed.image_size,num_shapes,shape_inventory: scene summary metadata.ground_truth_program: canonical ShapeCodeBench DSL program.render_config: deterministic V1 renderer configuration.
Evaluation Hygiene
eval_v1 is a frozen reporting split. Do not tune prompts, adapters, model
checkpoints, heuristic parameters, or generator settings on this split and then
report the result as clean held-out performance.
For development, generate separate train/dev splits from fresh seeds using the ShapeCodeBench repository.
Links
- Paper: https://arxiv.org/abs/2605.11680
- GitHub: https://github.com/shivamk3r/shape-code-bench
- Zenodo archived release: https://doi.org/10.5281/zenodo.20132286
- Artifact license: https://github.com/shivamk3r/shape-code-bench/blob/main/LICENSE-ARTIFACTS.md
License
The generated benchmark dataset is licensed under CC BY 4.0. ShapeCodeBench source code is licensed separately under MIT.
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