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PaintBench/test-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+pretty_name: PaintBench
+task_categories:
+- image-to-image
+- image-text-to-image
+size_categories:
+- 1K<n<10K
+tags:
+- benchmark
+- image-editing
+- visual-editing
+- paintbench
+- evaluation
+dataset_info:
+- config_name: PaintBench
+  features:
+  - name: category
+    dtype: string
+  - name: task
+    dtype: string
+  - name: mode
+    dtype: string
+  - name: problem_id
+    dtype: int32
+  - name: instruction
+    dtype: string
+  - name: input_image
+    dtype: image
+  - name: answer_image
+    dtype: image
+  - name: metadata
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 17727512
+    num_examples: 1050
+  download_size: 16940626
+  dataset_size: 17727512
+- config_name: TinyGrafixBench
+  features:
+  - name: category
+    dtype: string
+  - name: task
+    dtype: string
+  - name: mode
+    dtype: string
+  - name: problem_id
+    dtype: int32
+  - name: instruction
+    dtype: string
+  - name: input_image
+    dtype: image
+  - name: answer_image
+    dtype: image
+  - name: metadata
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 51745870
+    num_examples: 600
+  download_size: 51552058
+  dataset_size: 51745870
+- config_name: ablations
+  features:
+  - name: category
+    dtype: string
+  - name: task
+    dtype: string
+  - name: mode
+    dtype: string
+  - name: problem_id
+    dtype: int32
+  - name: instruction
+    dtype: string
+  - name: input_image
+    dtype: image
+  - name: answer_image
+    dtype: image
+  - name: metadata
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 20114921
+    num_examples: 1080
+  download_size: 19106533
+  dataset_size: 20114921
+configs:
+- config_name: PaintBench
+  default: true
+  data_files:
+  - split: test
+    path: PaintBench/test-*
+- config_name: TinyGrafixBench
+  data_files:
+  - split: test
+    path: TinyGrafixBench/test-*
+- config_name: ablations
+  data_files:
+  - split: test
+    path: ablations/test-*
+---
+
+# PaintBench
+
+**A precise, deterministic visual editing benchmark.** PaintBench evaluates whether generative models can execute MS-Paint-style image editing operations with pixel-level correctness. Every problem is a `(input_image, instruction, answer_image)` triplet, generated programmatically so the answer is pixel-exact and the answer distribution is known by construction.
+
+This dataset bundles three sibling benchmarks that share one generation and evaluation pipeline:
+
+| Subset (config) | Rows | Scope |
+|---|---|---|
+| **PaintBench** (default) | 1,050 | 20 canonical tasks (35 task-modes) grouped into 4 categories — `geometric_transformation`, `structural_manipulation`, `color_change`, `symbolic_reasoning`. |
+| **TinyGrafixBench** | 600 | Chart-edit mini-benchmark: 5 chart types (`bar_chart`, `heatmap`, `line_chart`, `network`, `scatter_plot`) × 4 edit subtasks per chart — a real-world analog of the primitive operations. The chart type is the `category` and the edit subtask is the `task` (mirrors the PaintBench grouping). |
+| **ablations** | 1,080 | Per-axis variation studies on 4 focal PaintBench tasks (`removal_attribute`, `recolor_color_code`, `copying`, `preservation`) × 9 variants. Every row has `category="ablation"`; the focal task is the `task` and the variant (`baseline`, `horizontal`, `vertical`, `small`, `nonstandard`, `striped`, `n16`, `n36`, `n64`) is the `mode`. |
+
+Each subset has a single `test` split — the benchmarks are intended for evaluation, not training.
+
+## Quick start
+
+```python
+from datasets import load_dataset
+
+ds  = load_dataset("PaintBench/PaintBench", split="test")
+tgb = load_dataset("PaintBench/PaintBench", "TinyGrafixBench", split="test")
+abl = load_dataset("PaintBench/PaintBench", "ablations",       split="test")
+
+ex = ds[0]
+ex["category"]      # top-level group, e.g. "color_change"
+ex["task"]          # canonical task, e.g. "flood_fill"
+ex["mode"]          # subtask, e.g. "background"  ("" when the task has no modes)
+ex["instruction"]   # natural-language edit instruction
+ex["input_image"]   # PIL.Image — the source canvas
+ex["answer_image"]  # PIL.Image — pixel-exact ground truth
+ex["metadata"]      # JSON string — full per-problem spec (seed, scene_shapes, params, ...)
+```
+
+Group by category / task / mode:
+
+```python
+from collections import Counter
+Counter(ds["category"]).most_common()       # 4 PaintBench categories
+Counter(tgb["category"]).most_common()      # 5 chart types
+Counter(abl["mode"]).most_common()          # 9 ablation variants
+```
+
+## Schema
+
+All three configs share a uniform schema:
+
+| Column | Type | Description |
+|---|---|---|
+| `category` | `string` | Top-level grouping. **PaintBench**: one of `geometric_transformation` / `structural_manipulation` / `color_change` / `symbolic_reasoning`. **TinyGrafixBench**: chart type (`bar_chart`, `heatmap`, `line_chart`, `network`, `scatter_plot`). **ablations**: `"ablation"`. |
+| `task` | `string` | Operation within the category. **PaintBench**: canonical task (`flood_fill`, `recolor`, `translation`, ...). **TinyGrafixBench**: chart-edit subtask (`add_bar`, `sort_bars`, `mask_cells`, ...). **ablations**: focal task identifier (`removal_attribute`, `recolor_color_code`, `copying`, `preservation`). |
+| `mode` | `string` | Subtask / variant within the `task`. **PaintBench**: subtask (`background`, `foreground`, `external`, ...) or `""` when the task has no modes. **TinyGrafixBench**: always `""`. **ablations**: variant (`baseline`, `horizontal`, `vertical`, `small`, `nonstandard`, `striped`, `n16`, `n36`, `n64`). The on-disk `problems.jsonl` keeps the empty slots as JSON `null`; the dataset normalizes to `""` so the column stays a non-nullable `string`. |
+| `problem_id` | `int32` | Zero-indexed id within the `(category, task, mode)` cell. |
+| `instruction` | `string` | Natural-language edit instruction. |
+| `input_image` | `image` | Source canvas (PNG, embedded). |
+| `answer_image` | `image` | Pixel-exact ground-truth output (PNG, embedded). |
+| `metadata` | `string` | JSON-encoded per-problem spec — includes the random seed, scene composition, the on-disk folder name (`task_mode`), and any per-subset extras (`base_task_canonical` / `base_task_mode` for `ablations`; `params` / `scene_shapes` / colors for `PaintBench`). Useful for filtering, grouping, and exact regeneration. |
+
+To pull structured fields out of `metadata`:
+
+```python
+import json
+ex = ds[0]
+meta = json.loads(ex["metadata"])
+meta["seed"]                  # for deterministic regeneration
+meta["task_mode"]             # on-disk folder name (= "<task>_<mode>" or "<task>")
+meta.get("base_task_canonical")  # populated only for the `ablations` config
+meta.get("scene_shapes")      # PaintBench scene composition
+```
+
+## Evaluation
+
+PaintBench uses pointwise CIE76 ΔE between the model's output and `answer_image`. Per-pixel ΔE values are thresholded at {0, 2, 5, 10} and aggregated to per-problem and per-task pass rates. The reference implementation lives in the [project repo](https://github.com/PaintBench/paintbench-internal).
+
+## Source
+
+The benchmarks are generated programmatically — there is no human curation step. Every problem is reproducible from its `metadata.seed`. See the [project repo](https://github.com/PaintBench/paintbench-internal) for the generation pipeline and a full per-task description.
+
+## License
+
+Released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license. You are free to share and adapt the dataset, including for commercial use, with attribution.
+
+## Citation
+
+Citation will be added upon paper publication. For now, please reference this dataset as:
+
+```bibtex
+@misc{paintbench2026,
+  title  = {PaintBench: A Precise, Deterministic Visual Editing Benchmark},
+  author = {PaintBench team},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/PaintBench/PaintBench}
+}
+```

TinyGrafixBench/test-00000-of-00001.parquet

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ablations/test-00000-of-00001.parquet

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