imagenet-variations-synth-v4

Synthetic multimodal instruction data. Prompts come from laion/imagenet_variations; images are generated with FLUX.1-schnell, encoded to 32 SEED-2 tokens per image, grounded with Florence-2, and turned into USER / ASSISTANT records.

49,334 records / 93,684 turns / 71,015 images. This is the full run of the v4 design that the 990-record pilot (imagenet-variations-synth-pilot-v4) validated, rebuilt from 50,000 source prompts across a 25-task GPU array.

What is in it

Track	Rows	Share	What it teaches
A	10,051	20.4%	programmatic VQA from verified detector counts
D	8,751	17.7%	mood, people, scene-level VLM questions
C	7,529	15.3%	follow-up questions that evolve a previous answer
B	7,097	14.4%	free-form VLM conversation / detailed description
W	4,943	10.0%	image composited into a synthetic page, questions on its text
G	3,926	8.0%	"create another picture like this", answered in <seed2> tokens
H	3,027	6.1%	medium anomaly: "what is unusual about this picture?"
F	2,011	4.1%	unanswerable and false-premise questions
E	1,999	4.1%	contrastive pairs: same subject, two media

Axis	Distribution
turns	1: 23,065, 2: 12,522, 3: 9,413, 4: 4,334 (53.2% multi-turn)
separator	newline 19,683, [sep] 7,532, <|endoftext|> 7,395, <|im_end|> 7,379, === 7,345
answer format	short 43,572, mc_single 17,763, numeric 6,699, boolean 6,722, long 6,758, image 3,926, unanswerable 3,359, list 2,483, mc_multi 2,402
reasoning on	24,786 / 49,334 = 50.2%
traces	23,459 records carry <think>; 11,399 are real Qwen3-VL-8B-Thinking traces
corruption	14,295 rows (29.0%): jpeg 2,794, rescale 2,465, noise 2,390, blur 2,372, jitter 1,969, rotate 1,370, moiré 935
aspect	1280x720: 10,911, 1024x1024: 9,966, 1024x768: 8,005, 768x1024: 7,996, 720x1280: 4,958, 768x1344: 4,091, 1344x768: 4,073

50,000 pool entries were built (25 shards x 2,000, zero failures); 666 were dropped because every candidate turn failed a content gate.

Pipeline

imagenet_variations prompt
  -> style framing (+ aspect ratio)      -> FLUX.1-schnell
  -> [page composite | anomaly boost | contrastive pair | generation pair]
  -> SEED-2 encode (32 ids, N in [0, 8191])
  -> Florence-2 grounding (counts + boxes, per-label NMS at IoU 0.45)
  -> Qwen2.5-VL-7B-Instruct              (reasoning-off half, v3.1 prompts unchanged)
  -> Qwen3-VL-8B-Thinking                (reasoning-on half, produces the trace)
  -> format axes -> USER / ASSISTANT

Two stages: build_pool_v4.py (GPU, sharded over a 25-task array) then render_records_v4.py (CPU, 16 s for the whole corpus), so the format layer can be regenerated without new images.

Verified invariants

Every number here is checked by check_v4.py over all 49,334 records, not sampled:

• 55,259 <seed2> blocks, every one exactly 32 ids, every id in [0, 8191]; no record is missing its block. 4,191 of 8,192 codebook entries occur.
• 20,165 multiple-choice turns, 0 malformed (the marked letter is always among the offered options).
• 3,926 track-G turns, 0 malformed image answers.
• 0 track-W answers that cannot be traced back to page text we authored.
• 0 hedges in final answers, 0 prompt leaks, 0 demographic references.
• 0 records reference a missing image file.

Multiple choice is not guessable from position

A5_mc_count used to sort numeric options ascending. Because the distractors for a count n are n+1, n-1, n+2, n+3 and Florence counts are overwhelmingly 1, the key landed on (A) 63% of the time and never on (C) or (D) -- a model could score 63% on counting MC without looking at the image. Numeric options now take the same hash shuffle as every other option type:

mode	A	B	C	D
A5_mc_count	23%	27%	26%	24%
A5_mc_label	25%	25%	25%	25%

Correctness of multiple-choice keys

MC distractors are drawn only from labels verified absent from both the detector counts and the dense caption, and the correct option comes from the detector. No VLM writes an MC key, so a key can only be wrong where the detector is wrong -- which is measured below rather than assumed away. Track W is the one place the key is exact by construction: every page string is authored by the generator.

Measured ground-truth accuracy

Counting and presence answers come from Florence-2 boxes, so they inherit Florence's mistakes. audit_counts_v4.py asks a different model (Qwen2.5-VL-7B) the same questions about the same pixels. This run audits 2,000 sampled turns, an order of magnitude more than the pilot's 200; data/audit_counts.json holds every disagreement.

The audit was run twice: once on the corpus as first rendered, and once after the result of that audit changed how counts are asked.

	n	exact counting	presence
pilot (shipped)	102	87.3%	100%
full run, first pass	1,447	82.6%	553/553 = 100%
shipped corpus	1,373	85.3%	627/627 = 100%

The first pass was not a regression against the pilot. The pilot's 87.3% carried a 95% confidence interval of [80.8, 93.7] on 102 samples; the first pass's 82.6% has an interval of [80.6, 84.5] on 1,447. They overlap, and the honest conclusion is that ~82.6% was always the underlying rate -- the pilot's headline number was small-sample optimism.

That pass also showed agreement degrading monotonically with the number asserted:

asserted count	share of count records	approx. disagreement
1	57.5%	~13%
2	23.7%	~24%
3	12.3%	~35%
>= 4	6.3%	not asserted

A third of asserted 3s being contradicted is not a rate worth shipping, so the ceiling came down. The pipeline does not assert an exact count above two. Those images get a cardinality question instead ("Are there multiple X, or just one?" -> "There are multiple X.", mode A1_multiple) or an object-identity MC, which needs a label to be present rather than counted. This withdrew 1,364 of 12,240 count-asserting turns (11.1%); the record count is unchanged because those turns are rewritten, not dropped.

The second pass measures the corpus in this repo: 85.3%, 95% CI [83.4, 87.2]. That interval does not overlap the first pass's [80.6, 84.5], so the +2.7 points is a real improvement and not resample noise. It also lands on the ~85.0% predicted from the per-bucket gradient before the change was made, which is the check that the gradient itself was measured correctly. Presence stays exact on a larger sample, 627/627.

Known residuals, stated rather than hidden. A quarter of asserted 2s still disagree with the auditor, nearly always by one, in the regime where a box detector and a VLM eyeball count are about equally trustworthy. Part-vs-whole labels (car: 1 for a car interior) remain ambiguous. The auditor is a second VLM, not ground truth -- on at least one diagram in the pilot it was wrong and Florence was right -- so 85.3% is inter-model agreement, and the true error rate could sit either side of it. And the ceiling has a cost: this corpus teaches "one", "two" and "more than one", not "seven". That is deliberate. FLUX-generated crowds plus Florence boxes cannot support "seven", and a confidently wrong 7 is worse supervision than an honest "multiple". Treat the counting answers as ~85% reliable supervision, not as gold.

Garbled text is gated out

FLUX cannot render legible text, so a VLM asked to describe a sign or a page transcribes the shapes as if they were words -- Display shows "Plirsuics Piteltascuddess", FEOLIGS KENNIESS. That is confidently wrong supervision. With no dictionary available the corpus is used as its own vocabulary: a real word appears in many records, an invented one in exactly one. Answers carrying two or more such tokens are dropped. Track W is exempt because its page text is authored, not read.

Scored by an independent per-answer heuristic (not the gate's own rule, which would be circular), non-track-W answers carrying two or more one-off long tokens fell from 1,518 / 81,513 = 1.86% to 568 / 80,506 = 0.71%, at a cost of 304 records. Inspecting the residue, it is dominated by real words that happen to be rare in this corpus -- "strainers", "alphanumeric", "keypads" -- rather than by invented ones; the strings the gate targets are gone. The heuristic cannot separate a rare word from a hallucinated one with certainty, so treat 0.71% as an upper bound, not a defect rate.

Repetition

At 50k a fixed question template lands on thousands of rows, so the question pools were widened before the run (mood questions are now generated per image rather than echoed from the instruction; corners, page structure, list, describe and anomaly questions all draw from pools). Measured on the shipped corpus:

• 93,684 turns, 57,413 distinct (question, answer) pairs = 61.3%
• worst repeated pair: x166 -- "Is there a book in this image?" -> "Yes."
• per track: B 85%, C 85%, D 84%, A 67%, G 57%, E 46%, F 41%, W 39%, H 35%

Read that honestly: repetition is inherent to short-answer VQA at this scale, and the low-uniqueness tracks are the ones whose answers come from a small enumeration (H is a fixed set of medium anomalies, W a fixed set of page structures). No single (question, answer) pair exceeds 0.18% of turns.

Legibility constraint (track W)

SEED-2 encodes at 224x224. The page renderer measures each element's font size after that squash and only asks about elements that stay legible; some pages are rendered as a header / figure / body crop so smaller text becomes askable.

Record schema

{
  "id": "inv_012345",
  "text": "USER: <seed2> ... </seed2> Q1 ASSISTANT: <think>...</think> A1 === USER: Q2 ASSISTANT: A2",
  "image_path": "images/inv_012345.png",
  "metadata": {
    "track": "A", "modes": ["A5_mc_count", "F4_false_premise"],
    "n_turns": 2, "separator": "equals",
    "answer_formats": ["mc_single", "short"],
    "reasoning_on": true, "reasoning_in_text": true,
    "thinking": ["...", "..."], "thinking_sources": ["programmatic", "programmatic"],
    "corruption": {"kind": "jpeg", "params": {"quality": 22}, "clean_id": "inv_012345"},
    "page_layout": null, "gen_pair": null, "anomaly": null,
    "box_counts": {"shovel": 3}, "gates_passed": ["hedge", "demog", "no_leak"]
  }
}

A corrupted row points at <id>_c.png and its corruption.clean_id names the untouched twin, which is also present -- the pair is the robustness signal, so both halves ship. image_path is repo-relative; the same is true of data/pool.jsonl.

Images: 65 tar shards, not 71,015 loose files

The 71,015 PNGs (65 GB) ship as images/shard-000.tar ... images/shard-064.tar, about 1 GB each. This is not a stylistic choice. The Hub stops a folder listing at 10,000 entries, so most of a flat images/ would not have been enumerable at all, and committing that many files runs into a 320-commit-per-hour account limit -- an attempt at the flat layout spent four hours without landing a single commit.

Member names inside a tar are the bare filenames, so extracting every shard into one directory reproduces exactly the layout image_path refers to:

hf download EmpathicRobotics/imagenet-variations-synth-v4 --repo-type dataset   --include 'images/*.tar' --local-dir .
cat images/shard-*.tar | tar -xif - -C images/
# images/inv_012345.png now resolves

data/image_shards.json maps every filename to its shard, so a consumer who wants a subset can fetch only the shards that carry it rather than all 65 GB.

The shards are also a valid WebDataset -- the key is the filename stem and the extension is png -- so they stream without unpacking:

import webdataset as wds
url = "https://huggingface.co/datasets/EmpathicRobotics/imagenet-variations-synth-v4/resolve/main/images/shard-{000..064}.tar"
ds = wds.WebDataset(url).decode("pil")

Thinking traces

metadata.thinking is stored for every turn that has one, and thinking_sources says where it came from:

• vlm — generated by Qwen3-VL-8B-Thinking while answering that question
• programmatic — derived deterministically from verified detector counts, page layout or configuration

Traces are emitted into the trainable text only when reasoning_on is true. Hedging is permitted inside <think> (a trace should be allowed to weigh alternatives) but not in the final answer. 1,162 generated traces were rejected by a coherence gate rather than shipped.

Caveats

• Images are synthetic; Florence-2 labels are imperfect on unusual subjects.
• Track G supervises the prompt→image mapping; the count in an "include N objects" instruction is what was asked of the generator, not a verified count in image B.
• upstream_meta may retain demographic fields from the source prompts. These are gated out of all questions and answers.