| --- |
| license: apache-2.0 |
| language: |
| - en |
| pretty_name: OmniTaskonomy Recipe Data |
| task_categories: |
| - image-to-image |
| - visual-question-answering |
| tags: |
| - omnitaskonomy |
| - multimodal |
| - visual-generation |
| - visual-understanding |
| configs: |
| - config_name: jigsaw |
| default: true |
| data_files: |
| - split: train |
| path: data/jigsaw/train/*.parquet |
| - split: val |
| path: data/jigsaw/val/*.parquet |
| - config_name: zoomin |
| data_files: |
| - split: train |
| path: data/zoomin/train/*.parquet |
| - split: val |
| path: data/zoomin/val/*.parquet |
| - config_name: video_unshuffle |
| data_files: |
| - split: train |
| path: data/video_unshuffle/train/*.parquet |
| - split: val |
| path: data/video_unshuffle/val/*.parquet |
| - config_name: rotate_qa |
| data_files: |
| - split: train |
| path: data/rotate_qa/train/*.parquet |
| - split: val |
| path: data/rotate_qa/val/*.parquet |
| - config_name: counting |
| data_files: |
| - split: train |
| path: data/counting/train/*.parquet |
| - split: val |
| path: data/counting/val/*.parquet |
| - config_name: visgym_colorization |
| data_files: |
| - split: train |
| path: data/visgym_colorization/train/*.parquet |
| - split: val |
| path: data/visgym_colorization/val/*.parquet |
| --- |
| |
| # OmniTaskonomy (arxiv.org/abs/2609.38079) Recipe Data |
|
|
| Paired image-to-image (I2I) and image-to-text (I2T) tasks for the R1–R6 training recipes and gradient analysis in [OmniTaskonomy](https://omni-taskonomy.github.io/). |
| Each of the six subsets has `train` and `val` splits. One row contains both objectives for the same task instance. |
|
|
| ```python |
| from datasets import load_dataset |
| |
| data = load_dataset("Wakals/OmniTaskonomy_Recipe_Data", "jigsaw", split="train", streaming=True) |
| sample = next(iter(data)) |
| ``` |
|
|
| ## Fields |
|
|
| - `id`, `task`, `split`: unique record ID, subset name, and `train` or `val`. |
| - `i2i_input_image`, `i2i_prompt`, `i2i_output_image`: image-generation supervision. |
| - `i2t_input_image`, `i2t_prompt`, `i2t_answer`: understanding supervision. Its input can differ from the I2I input. |
| - `source_id`, `source_dataset`, `metadata`: source identity and JSON-encoded task parameters, original split, and provenance. |
|
|
| Images are embedded in the Parquet files. The prompt summaries below are abbreviated; each row stores the full prompts and native answer text. |
|
|
| ## Jigsaw |
|
|
| `jigsaw`: **100,000 train / 1,000 val**. |
| Input: a shuffled 2×2 image puzzle. I2I prompt: “Rearrange the shuffled image patches to reconstruct the original image.” Output: the reconstructed image. |
| I2T prompt asks for the patch order; output: `('reorder', [i0, i1, i2, i3])`, using indices **0–3** from top-left to bottom-right. I2I images use the training loader's square edge padding. |
|
|
| ## Zoomin |
|
|
| `zoomin`: **100,000 train / 1,000 val**. |
| Input: an original image and four shuffled zoomed views. I2I prompt: “Rearrange the zoomed-in views so they are ordered from least to most zoomed.” Output: the reordered view strip. |
| I2T prompt asks for the same ordering; output: `('reorder', [i0, i1, i2, i3])`, using view indices **1–4**. |
|
|
| ## Video Unshuffle |
|
|
| `video_unshuffle`: **30,000 train / 1,000 val**. |
| Input: four shuffled frames from a synthetic 3D video. I2I prompt asks to restore chronological order; output: the reordered frame strip. |
| I2T prompt includes the action description and asks for the frame order; output: `('reorder', [i0, i1, i2, i3])`, using frame indices **1–4**. Validation contains only the original **OOD** set. |
|
|
| ## Rotate QA |
|
|
| `rotate_qa`: **50,000 train / 7,000 val**. |
| I2I input: the canonical COCO image, center-cropped and resized to 512×512. Prompt: “Rotate the image {degrees} degrees clockwise.” Output: the rotated image. |
| I2T input: that rotated image. Prompt: the stored multiple-choice rotation question; output: its original answer text. `metadata.correct_choice` stores the answer letter. Validation uses 1,000 source images with seven rotations each. |
|
|
| ## Counting |
|
|
| `counting`: **50,000 train / 1,000 val**. |
| Input: an image and a target category. I2I prompt: “Mark all {category} in this image by placing a dot on each instance.” Output: the image with instance markers. |
| I2T prompt: “How many {category} are in this image? Answer with a number.” Output: the integer count. The native prompt variants and source category are retained in `metadata`. |
|
|
| ## VisGym Colorization |
|
|
| `visgym_colorization`: **30,000 train / 833 val**, from `colorization_new`. |
| I2I input: an image with a circular region masked in gray. Prompt asks to inpaint that region while preserving the surrounding image and black outline; output: the restored image. |
| I2T input includes four color-wheel options beside the masked image. Prompt asks which option matches the hidden hue; output: `A`, `B`, `C`, or `D`. |
|
|
| ## Sources and license |
|
|
| Jigsaw, Zoomin, and Counting use [VisGym](https://huggingface.co/datasets/VisGym/visgym_data) episodes; Colorization uses the current paired VisGym-derived pool. Rotate QA uses [COCO](https://cocodataset.org/); Video Unshuffle contains project-generated 3D scenes. |
|
|
| The repository retains its Apache-2.0 declaration. This does not relicense third-party images: their original source terms continue to apply, including [COCO image terms](https://cocodataset.org/#termsofuse) and the underlying VisGym image sources. |
| Original sample order and split membership are preserved, including existing overlaps. Counting's 1,000 validation examples also occur in its training pool. Counts and file checksums are listed in [release_manifest.json](release_manifest.json). |
|
|
