| --- |
| language: |
| - en |
| task_categories: |
| - visual-question-answering |
| tags: |
| - synthetic |
| - multi-image |
| - parquet |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| # MultiImage Synthetic 440K |
|
|
| 440,261 synthetic multi-image multiple-choice visual question-answering training examples, stored in 64 original Parquet shards with embedded JPEG image bytes (about 70.79 GB). These are the unchanged training Parquet shards from the scale_mix_v2 training mixture. |
|
|
| ## Contents and loading |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("yilingwang/MultiImage-Synthetic-440K", split="train", streaming=True) |
| example = next(iter(ds)) |
| # example["images"] is a list of {"bytes": JPEG bytes, "path": synthetic image ID}. |
| ``` |
|
|
| Columns: `id`, `source_id`, `split`, `task_type`, `ability_id`, `sample_id`, `option_kind`, `split_groups`, `provenance`, `prompt`, `answer`, `images`, `image_sha256`. |
|
|
| `answer` is a JSON string containing choices and the correct option. `prompt` includes the question and image placeholders. `images` contains embedded JPEG bytes and synthetic image IDs; `image_sha256` contains their checksums. Original provenance strings are retained; internal source locations are historical references and are not needed to load the embedded images. |
|
|
| ## Source and metadata audit |
|
|
| The source/export code identifies these examples as generated by the srcgen image generation, image editing, rendering, and synthetic video pipelines. No TikTok user-data ingestion path was found in this source chain. All 440,261 training records and 32 validation-stub records were checked: IDs and provenance match srcgen, and no TikTok identifiers, user-account field keys, or credential patterns were detected in the non-image columns. This audit checks sources and metadata; it does not constitute visual inspection of every image. |
|
|
| Generation and export code: https://github.com/yilinwangll/multi-image/tree/refactor-m0/srcgen |
|
|
| ## Task distribution |
|
|
| | Task | Training examples | |
| |---|---:| |
| | action_order | 44804 | |
| | camera_turn | 43668 | |
| | change_match | 20231 | |
| | change_track | 20126 | |
| | count_across | 35514 | |
| | functional_match | 40751 | |
| | object_count | 9771 | |
| | object_group | 8382 | |
| | patch_fill | 38892 | |
| | point_match | 41978 | |
| | same_object | 11198 | |
| | same_person | 5693 | |
| | semantic_match | 44688 | |
| | spot_difference | 34027 | |
| | surround_views | 40538 | |
| |
| ## Validation stub and integrity |
| |
| `validation_stub.parquet` contains 32 rows copied from the training mixture to satisfy the original trainer's file requirements. It is **not an independent evaluation set** and is deliberately excluded from the default dataset splits. |
|
|
| `SHA256SUMS` lists the SHA256 digest of every published file except itself. `source_manifest.json` is the original source manifest; its `train.parquet` hash refers to the original single-file source, while this repository publishes its 64 training shards. `audit_summary.json` summarizes the source and metadata checks. |
|
|
| The repository does not include model weights, optimizer states, raw user account records, or service credentials. No new license grant is asserted by this card. |
|
|
