Datasets:

PatZhang11
/

i1-captions-selected

configs:
  - config_name: fluxreason
    data_files:
      - split: train
        path: fluxreason/train-*.parquet
    default: true
  - config_name: gptedit
    data_files:
      - split: train
        path: gptedit/train-*.parquet
  - config_name: imagenet22k
    data_files:
      - split: train
        path: imagenet22k/train-*.parquet
  - config_name: inaturalist
    data_files:
      - split: train
        path: inaturalist/train-*.parquet
  - config_name: megalith10m
    data_files:
      - split: train
        path: megalith10m/train-*.parquet
  - config_name: midjourneyv6
    data_files:
      - split: train
        path: midjourneyv6/train-*.parquet
  - config_name: pexels
    data_files:
      - split: train
        path: pexels/train-*.parquet
  - config_name: places365-challenge2016
    data_files:
      - split: train
        path: places365-challenge2016/train-*.parquet
  - config_name: redcaps
    data_files:
      - split: train
        path: redcaps/train-*.parquet
  - config_name: rendered_text
    data_files:
      - split: train
        path: rendered_text/train-*.parquet
  - config_name: textatlas
    data_files:
      - split: train
        path: textatlas/train-*.parquet
  - config_name: yfcc
    data_files:
      - split: train
        path: yfcc/train-*.parquet
task_categories:
  - text-to-image
size_categories:
  - 100M<n<1B

i1-captions-selected: one complete caption per image

A derivative of zlab-princeton/i1-captions that (a) repairs the captions that the original pipeline cut off mid-sentence, and (b) collapses the up-to-five alternative captions per image down to a single chosen one.

The original dataset stores caption1..caption5 and the i1 recipe samples one at random per training iteration. Here that choice is made once, up front, and the chosen caption is guaranteed to be a complete sentence — so the data can be consumed directly as (key, caption).

1. What changed vs. the original

Repaired truncation. 1,152,605 rows (0.69%) had no complete caption in any column — an artifact of the original captioner running with a 256-token generation cap. Those images were re-captioned from source with the i1 recipe: Qwen/Qwen3-VL-30B-A3B-Instruct-FP8 in FP8, the same prompts (including the TextAtlas ground-truth-annotation prompt and the RenderedText OCR-line prompt), images RGB → shorter-edge-512 → center-crop, and a raised token budget (640 → 1536, and 3072 for the OCR-transcription-heavy textatlas, where 640 was badly insufficient: repaired captions there average 293 words vs ~160 under the old cap).

One caption per row. The caption1..caption5 columns are replaced by a single caption.

2. Layout

Same structure and key values as the original: one directory per subset, train-000NN.parquet shards of 1M rows.

<subset>/train-000NN.parquet     # columns: key (string), caption (string)

subset	rows	vs. original
`yfcc`	97,942,438	−2,848
`imagenet22k`	13,673,542	−2
`rendered_text`	11,977,780	−36
`megalith10m`	9,393,285	−686
`places365-challenge2016`	7,220,913	−684
`fluxreason`	5,890,279	=
`textatlas`	5,396,767	−123
`redcaps`	4,817,430	−1
`inaturalist`	4,812,852	−691
`pexels`	2,810,634	=
`gptedit`	1,553,575	=
`midjourneyv6`	1,240,185	=

Total: 166,729,680 rows — 5,071 fewer than the original 166,734,751 (0.003%).

Those 5,071 rows were dropped, not left broken. Their source images are dead links, so no clean caption exists for them anywhere and repeated resampling could not produce one. Breakdown of what was dropped: 4,117 captions under 25 words (on inspection these are not merely terse but collapsed — broken grammar, language mixing, and outright non-answers like "I cannot describe an image that was not provided"), 918 that could not be made to end on a sentence boundary, 21 repetition loops, and 15 assistant refusals.

from datasets import load_dataset
ds = load_dataset("<your-namespace>/i1-captions-selected", "yfcc", split="train")

3. How the caption was chosen

Repaired rows win — used verbatim.
Otherwise the best of caption1..caption5. The five columns are statistically interchangeable (measured: near-identical completion rates and length distributions), so there is no "better column"; candidates are filtered on content. A candidate must end on sentence-final punctuation, contain no structural junk (```, bbox_2d, "image_path", <html>, …), not open with a refusal, contain no repetition loop, and be ≥25 words. Among survivors the longest wins — the i1 paper finds longer caption sets train better.
Salvage — if nothing passes, every candidate is trimmed to its last complete sentence and the richest survivor is used (0.08% of rows).
Drop — if even that fails, the row is omitted (5,071 rows total, 0.003%).

A "repetition loop" means the captioner degenerated into repeating itself (e.g. one caption had ME 211 times in 421 words). The detector is deliberately strict — a single token appearing ≥50 times and making up >25% of the caption, or a 5-gram repeated ≥20 times — because a looser rule wrongly flags correct OCR transcription of images that genuinely contain long runs of a character.

imagenet22k's extra columns (qwen2vl_2b, qwen2.5vl_3b, qwen3vl_2b, qwen3vl_4b, short, no_center_crop) are excluded — they are ablation artifacts from weaker captioners and different preprocessing/prompt settings, not the captions used for i1's final training.

4. Quality

Audited over all 166.7M rows, every subset: 0 incomplete, 0 repetition loops, 0 refusals, 0 empty, 0 under 25 words. Mean caption length 128–174 words depending on subset.

5. One thing to know before training

i1 right-truncates text-encoder input to 256 tokens. The repaired captions are longer than the original corpus, which was implicitly bounded by the old 256-token generation cap: measured with the google/t5gemma-2b-2b-ul2 tokenizer, the original yfcc caption1 is median 172 tokens with 0.1% over 256, whereas the repaired ones are median 246 with ~41% over 256. Those rows will be cut at the encoder. Trimming to the last complete sentence within 256 tokens is a lossless offline fix if that matters for your run; it is deliberately not applied here.

6. Citation

The underlying dataset and recipe are from:

@article{zeng2026i1,
  title={i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models},
  author={Zeng, Boya and Luo, Tianze and Pu, Shu and Shen, Jucheng and Lu, Taiming and Sarch, Gabriel and Liu, Zhuang},
  journal={arXiv preprint arXiv:2606.11289},
  year={2026}
}