A vision-language model read each image together with its QA and judged the item. The pass is not a filter that only removes rows — it rewrites answers it finds wrong but salvageable, drops what it cannot salvage, and adds QA where the image supports more than the source provided. Each row carries the judge's own record in clean_meta, including the cleaner identity, the policy it applied, and its per-item scores for legibility, richness and coverage.

A large share of the QA here was written by the cleaning pass, not by the original dataset. Across the pool that share runs from roughly half to over 80% of a family's turns, and it is reported in the table above. Those rows have an empty org_answer because no pre-clean original exists. Treat them as model-generated supervision: they were judged against the image, but they are not human annotation, and model-written QA is where formatting defects are most likely.

The effect on the answers that were carried over is substantive rather than cosmetic. In this pool the pass corrects values, not just wording — a curve's critical point restated from 4.00 to 2.00, a computed ratio from 1 to Approximately 1.33 — and for some families it removed the content entirely, which is why those families are absent here rather than published empty.

`answer` vs `org_answer`

answer — the cleaned answer. Train on this.
org_answer — the pre-cleaning answer from the same (image, question) in the uncleaned pool. It is empty for QA the cleaning pass added, which have no original.

For this family org_answer is empty throughout: recovering it means scanning the whole pre-clean family, which is raw family is 16 GB (> 6 GB cap). The cleaned answer is unaffected.

Where the two differ, the difference is the correction. Keeping both makes every edit auditable instead of asking you to take the cleaning on trust.

Schema

image — HF Image(), renders directly in the dataset viewer
image_sha256 — content hash of the image
n_qa — number of turns attached to this image
qa — list of {question, answer, org_answer}, in source order
family / clean_meta — provenance and the judge's per-row record

A row with more than one entry in qa is a conversation over a single image, not a set of independent questions — the turns depend on each other and the image belongs to all of them. Keep them together and in order when training.

Parquet row groups are written at ~32 MB so the dataset viewer pages without stalling.

What is NOT claimed

No rejection sampling was run against this family: there is no accept/reject verdict per row, and answer is the cleaned reference rather than a model generation that a string verifier confirmed. For that, use the *_RS_think / *_rejected datasets in elliot-mllm.

Examples

10 rows taken straight from this dataset. Images are the original files as stored here - no downscaling, no recompression - and every turn is shown in full.

Example 1 — 5 turns on one image

#	question	answer
1	这家店的名字是什么?	禾不锈钢
2	招牌右侧除了店名还写了什么具体的服务项目?	6米剪板折弯
3	招牌下方列出了哪三个联系电话号码?	18502304582, 18996293017, 18523195639
4	店铺门口上方安装了什么颜色的卷帘门?	蓝色
5	左侧敞开的门口内部可以看到什么设备?	一台白色的大型机器

Example 2 — 4 turns on one image

#	question	answer
1	100%感觉下方的英文写了什么?	100% feeling
2	白色招牌上最大的红色中文是什么？	100%感觉
3	门口左侧黑色小牌子上写的门牌号是多少？	253
4	店铺门口挂着什么颜色的灯笼？	红色

Example 3 — 4 turns on one image

#	question	answer
1	which store offers LUXURY AYURVEDA?	SPA CEYLON
2	What is the name of the store with the blue cursive neon sign on the left side of the image?	Sooker
3	What blue neon sign is visible in the background inside the Spa Ceylon store area?	FOREVER NEW
4	What product names are visible on the black shelves in the bottom right foreground?	Daily De-stress and Four Reserve

Example 4 — 5 turns on one image

#	question	answer
1	玻璃门上的门牌号是多少?	23-3
2	招牌左侧的大字店名是什么？	怡烤盘
3	招牌右侧列出了哪些特色菜品？	特色海鲜小炒，原味羊排牛肉，生蚝扇贝虾尾
4	门口墙边摆放了几把白色的椅子？	4把
5	右侧红色招牌上可见的大字是什么？	大

Example 5 — 4 turns on one image

#	question	answer
1	Which shop name is written in golden?	shea
2	What slogan is written below the shop name 'shea'?	pamper your body
3	What unit number is visible on the door frame to the left?	B2-47
4	What large wooden object is displayed in the shop window?	A wagon wheel

Example 6 — 6 turns on one image

#	question	answer
1	这是太和烧肉什么指定放心销售点?	黄埔
2	黄色招牌中间的一行小字写着什么宗旨？	以卫生环保安全食品为宗旨、用心打造、做到更好
3	招牌上列出了哪些可以批发定购的肉类产品？	金牌乳猪、中猪、全猪、烧鹅、烧鸭、叉烧、卤水
4	招牌最下方关于老字号的时间描述是什么？	(老字号)1979年—2016年于名师制作(工厂批发点)
5	柜台右侧站着的男子穿着什么？	红色T恤，系着白色围裙，穿着深色牛仔裤
6	柜台左侧站着的女子穿着什么？	绿色长袖上衣和蓝色牛仔裤

Example 7 — 5 turns on one image

#	question	answer
1	Who owns the car?	CKERINGTON Central Marching Tigers
2	What text is written in a script font below 'CKERINGTON Central'?	Tigers
3	What text is written in a sans-serif font next to 'Tigers'?	Marching
4	What copyright information is visible on the right side of the trailer?	© 2013 Google
5	What type of vehicle is shown in the image?	A white enclosed trailer

Example 8 — 5 turns on one image

#	question	answer
1	北蔡镇属于哪个区?	浦东新区
2	黑色牌子上最大的字是什么？	无烟医疗机构
3	黑色牌子上列出了哪三个机构？	上海市卫生局、上海市医务工会、上海市爱国卫生运动委员会办公室
4	金色牌子下方写着什么？	红十字服务总站
5	金色牌子中间有什么标志？	红十字标志

Example 9 — 6 turns on one image

#	question	answer
1	what is the website of americanfield?	www.americanfield.us
2	What is the main title written in the center seal of the banner?	The ALL-AMERICAN AMERICAN FIELD
3	What specific event or market is described below the main title?	BOS POP-UP MARKET USA
4	Who is credited with making the banner on the right side?	Bullard & Buck
5	What year is listed as 'EST.' inside the central circular seal?	2012
6	What is the background design of the banner?	An American flag with stars and stripes

Example 10 — 4 turns on one image

#	question	answer
1	这里是哪里?	西村新市场
2	墙上的大字是什么颜色的？	金色
3	文字下方安装了一个什么设备？	监控摄像头
4	墙面上横七竖八的是什么？	黑色的电线

Downloads last month: 25

Total file size:

16.2 GB

You need to agree to share your contact information to access this dataset

est_vqa_cleaned

How this was cleaned

answer vs org_answer