--- pretty_name: ShapeCodeBench eval_v1 license: cc-by-4.0 size_categories: - n<1K tags: - image - synthetic - benchmark - code-generation - program-synthesis - arxiv:2605.11680 --- # ShapeCodeBench eval_v1 This dataset is the frozen `eval_v1` reporting split for [ShapeCodeBench](https://github.com/shivamk3r/shape-code-bench), 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: . This Hugging Face dataset is a discoverable and loadable mirror of the frozen evaluation split. ## Load ```python from datasets import load_dataset dataset = load_dataset("shivamk3r/shape-code-bench-eval-v1") ``` ## Columns - `image`: rendered target image. - `image_file_name`: original path under `data/eval_v1`. - `image_sha256`: checksum from `SHA256SUMS`. - `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: - GitHub: - Zenodo archived release: - Artifact license: ## License The generated benchmark dataset is licensed under CC BY 4.0. ShapeCodeBench source code is licensed separately under MIT.