| --- |
| license: other |
| license_name: mixed-per-image-licences |
| task_categories: |
| - image-text-to-text |
| language: |
| - th |
| size_categories: |
| - 10K<n<100K |
| pretty_name: Thai Spatial Reasoning |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| - split: validation |
| path: data/validation-*.parquet |
| - split: test |
| path: data/test-*.parquet |
| --- |
| |
| # Thai Spatial Reasoning 1.0.0 |
|
|
| Thai spatial captions and question-answer pairs for continual pretraining of a Thai |
| foundation VLM. Synthetic means the Thai text and the spatial annotations are generated or |
| processed; the primary images are real photographs. |
|
|
| Images are **not** covered by one blanket licence, so the card does not point at one file: |
| every image has its own licence, creator, and attribution in |
| [`rights/attribution.csv`](rights/attribution.csv) and |
| [`rights/ledger.parquet`](rights/ledger.parquet). The dataset-level `other` licence applies to |
| the annotations, the generated text, and the rendered images. (The Hub rejects a relative |
| `license_link`; the ledger inside the repository is the licence statement.) |
|
|
| ## Contents |
|
|
| | Item | Value | |
| | --- | --- | |
| | Images | 34764 | |
| | Logical examples (CPT) | 243348 | |
| | Per image | 1 caption + 4 question-answer pairs | |
| | Splits | {"train": 30481, "validation": 2271, "test": 2012} | |
| | Sources | real photographs with rights evidence, plus controlled counterfactual renders | |
|
|
| `data/*.parquet` is the canonical table: one row per image, with an `Image` column, one |
| caption, a list of four question-answer structures, the relation list, and the rights |
| fields. `cpt/examples-*.parquet` is the text-only index that expands one image row into |
| five logical examples, so image bytes are stored once. |
|
|
| ## Method |
|
|
| 1. Real images come from a source with object boxes. Rights are checked per image against a |
| licence allowlist; a verification date and an evidence URL are recorded for each one. |
| Images with person labels wait for a human privacy decision and do not ship without it. |
| 2. Splits are assigned per duplicate group and per rendered scene family before any text |
| exists. No group crosses a split boundary. |
| 3. Facts come from conservative image-frame geometry over boxes (a real separation on one |
| axis and real alignment on the other, no clipped axis, no occluded reference object) and |
| from source annotations for containment. Contact, depth, and distance are never inferred |
| from 2-D boxes; for the renders they are measured from known 3-D state. |
| 4. Thai text is assembled from those facts with deterministic templates, so each sentence |
| traces to fact ids, and the factual meaning does not depend on a model. |
| 5. An object whose class repeats in an image is named by what it looks like before any position is |
| used ("คนที่ใส่เสื้อสีเขียว", not "คนที่อยู่ทางซ้าย"). A pinned vision model returns one value per |
| field — colour, garment, pose, material — from a closed list; it writes no Thai, and an |
| attribute can neither create nor remove a spatial fact. Renders are never sent to it, because |
| their colours are known from the scene state. |
| 6. Every relation and every sentence is checked against its source fact, and a |
| Thai-speaking review sample of 0 examples is scored before |
| release. |
|
|
| ## Evaluation and quality |
|
|
| **The Thai-speaking review gate is still open for this build.** A sample of examples are queued in `reports/audit_queue.csv` for a native-speaker review of the relation, the language, and the faithfulness. This build ships before those verdicts exist, and it will be re-packaged with them; treat the relation labels as machine-verified only. |
|
|
| Validation report: [`reports/validation.json`](reports/validation.json). Audit report: |
| [`reports/audit.json`](reports/audit.json). Splits: |
| [`reports/split_manifest.json`](reports/split_manifest.json). Release gates checked: |
| rights_complete=pass, privacy_review_complete=pass, split_integrity=pass, logical_examples_per_image=pass, artifact_integrity=pass, human_audit=fail, lexicon_review=fail, declared_scale=fail, release_size=pass. |
|
|
| Real-image acceptance in this build: {'real': {'accepted': 5778, 'images': 20823, 'rate': 0.2775}, 'rendered': {'accepted': 28986, 'images': 40000, 'rate': 0.7247}}. |
| Relations available: {'above': 33571, 'behind': 177397, 'below': 33571, 'far': 4975, 'in_front_of': 177397, 'larger_than': 104893, 'left_of': 46479, 'near': 81090, 'not_touching': 199465, 'occludes': 25414, 'right_of': 46479, 'smaller_than': 104893, 'touching': 9364}. |
| |
| ## Intended use |
| |
| Continual pretraining and evaluation of Thai vision-language models on spatial language: |
| left/right, above/below, in front of/behind, near/far, contact, size comparison, and |
| containment, in image, object, and world reference frames. |
| |
| ## Limitations |
| |
| * Image-frame left/right and above/below are reliable only where the boxes are clearly |
| separated and aligned; the dataset does not claim 3-D layout from photographs. |
| * Renderer output is flat-shaded and synthetic: it is a controlled probe for relation |
| contrasts, not a substitute for photographs. |
| * Relation balance reflects what the source annotations support. Under-represented |
| relations are supplemented by renders, not invented for photographs. |
| * Thai text is template generated. |
| * Descriptions such as `เสื้อสีเขียว` come from a pinned vision model, not from the source |
| annotation. A spot check of six objects found five correct; an attribute is only used when it |
| tells one object apart from the others in the same image, so an ambiguous description is dropped |
| rather than applied to the wrong object. Treat the attribute text as model-generated. |
| |
| ## Version and citation |
| |
| Version 1.0.0, built by run `release-v13` with config `thai_spatial_reasoning_v1.yaml` |
| (config hash `dff750b131deec59`, code `ad8324a6dcd67a2a0780b05eb0f139108f3a19a3`). See the project's `CITATIONS.md` for |
| the methodology references. |
|
|
| ## Removal and contact |
|
|
| To request removal of an image or to report a rights problem, open an issue in the |
| project repository or contact the dataset maintainers (smartwhatt/thai_spatial_reasoning). Each |
| image row carries `source_url`, `image_license`, and `rights_evidence_uri`, which is enough |
| to identify and remove the exact image in a future revision. |
|
|
| ## Token budget |
|
|
| Measured with the `Qwen/Qwen3-VL-2B-Instruct` image processor over every one of the 34,764 images in this release, |
| so these are the tokens a Qwen3-VL model actually sees, not an estimate. Text is counted with the |
| processor tokenizer over captions, questions and answers. |
|
|
| | split | images | visual tokens | text tokens | |
| |---|---|---|---| |
| | train | 30,481 | 13,715,675 | 6,628,004 | |
| | validation | 2,271 | 3,788,003 | 683,998 | |
| | test | 2,012 | 1,467,105 | 481,804 | |
| | **total** | **34,764** | **18,970,783** | **7,793,806** | |
|
|
| **Total: 26,764,589 tokens** — 18,970,783 visual plus 7,793,806 text, an average of 545 visual and |
| 224 text tokens per image. |
|
|
| Real images cost far more than renders: 1,778 visual tokens per image on average against |
| 300 for a render, because a 1240x1754 photograph is not a 640x480 scene. The training |
| splits are render-heavy, so most of the training budget today is the fixed 300-token renders, and the |
| real photographs are where the per-image weight sits. |
|
|
| The continuous-pretraining index is a separate figure: 243,348 rows carrying 8,175,985 text |
| tokens, and about **140,800,645 tokens** if every row trains with its image attached. Quote |
| that number when a training recipe asks for a slice size. |
|
|
