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Jacqkues
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kev-vision-decisions-full

Tasks:
Visual Question Answering
Image Classification
Reinforcement Learning
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English
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Tags:
decision-model
calibration
typesafe
kev
jev
robotics
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kev-vision-decisions-full / README.md
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extra_gated_heading: 'Research use only: accept the source licences'
extra_gated_prompt: >-
  This dataset repackages images and labels from many third-party datasets.
  Several of them are research-only or non-commercial (RVL-CDIP, AVA, ScienceQA,
  AG News, Yelp, Food-101, Oxford Flowers, Stanford Cars), and images keep their
  original copyright. Every record carries its source licence in the license
  column. By requesting access you agree to use this data for non-commercial
  research only and to follow the licence and terms of each source dataset.
extra_gated_fields:
  Affiliation: text
  I will use this dataset for non-commercial research only: checkbox
  I will follow the licence and terms of each source dataset: checkbox
extra_gated_button_content: Agree and access
license: other
license_name: mixed-per-source
task_categories:
  - visual-question-answering
  - image-classification
  - reinforcement-learning
language:
  - en
tags:
  - decision-model
  - calibration
  - typesafe
  - kev
  - jev
  - robotics
  - atari
pretty_name: kev-vision-decisions-full
configs:
  - config_name: default
    data_files:
      - split: test
        path: test/*.parquet
      - split: test_ood
        path: test_ood/*.parquet
      - split: train
        path: train/*.parquet
      - split: validation
        path: validation/*.parquet

kev-vision-decisions-full

Typed, labelled decisions about images, in the TypeSafe /v1/systemone request shape: an image plus a state (text or JSON), and one or more typed questions (choice, noul = yes/no, score = ordered levels), each with its label. It was built to fine-tune Kev, a Jev-style decision model, to read images (code: kev-vision), but any image classifier / VLM / reward model can use it.

Full tier: includes research-only / non-commercial sources (see the licence table).

Gated, research use only. This repository repackages third-party data. Access is granted automatically once you accept the terms: non-commercial research only, and the licence of each source (the license column) applies to its records. Images keep their original copyright. If you are a rights holder and want a source removed, open a discussion on this repository.

Record format

column content
image the image (JPEG/PNG), or null for text-only replay records
state JSON: a string or an object (e.g. {"game": ..., "previous_action": ...})
questions JSON: {qid: {"type", "instructions", "criteria", "label", "target"?, "src"}}. Labels: the option name for choice, true/false for noul, the level index (from 0) for score. target, when present, is a soft distribution over the options (uniform when the image cannot answer; the voters' histogram for AVA).
source, license, split, id provenance: every record carries the licence of the source it was derived from

No augmentation is stored: option order, "none of the above" insertion and distractors are applied at training time.

What was added, source by source

source licence questions how it was converted
vqav2 CC-BY-4.0 (annotations); COCO images under their Flickr licenses noul, score yes/no and counting questions of VQAv2 (val2014 annotations) with ≥ 8/10 annotators agreeing; counts in 6 levels (zero … five or more); up to 4 questions per image; own split by COCO id % 10
aokvqa Apache-2.0 (annotations); COCO images choice A-OKVQA multiple choice, 4 options
eurosat MIT choice, noul land use among 4–6 sampled classes with descriptions; "is this ?" balanced 50/50
pets CC-BY-SA-4.0 choice, noul breed among 5 sampled breeds; cat or dog
fairface_age CC-BY-4.0 score apparent age in 9 ordered bins (gender and ethnicity deliberately not asked)
chartnet CDLA-Permissive-2.0 choice, noul chart type among 5 sampled types (ChartNet core_permissive); "is this a ?"
documents CDLA-Permissive-1.0 (DocLayNet) / CC-BY-4.0 (CORD) / MIT (invoices) choice, noul document page type among 5 of 8 classes: DocLayNet's financial reports, scientific articles, laws, government tenders, manuals, patents (balanced), CORD receipts, invoices; "is this a ?"
pong Apache-2.0 (JAT) choice expert (JAT) next action; 4 consecutive frames tiled left→right, previous action in the state; ALE actions with the same effect merged
breakout Apache-2.0 (JAT) choice same as Pong: stay / launch / right / left
freeway Apache-2.0 (JAT) choice same as Pong: wait / up / down
spaceinvaders Apache-2.0 (JAT) choice same as Pong: 6 actions (move × fire)
mspacman Apache-2.0 (JAT) choice same as Pong: 9 joystick directions
pusht MIT choice, score LeRobot PushT human demos: direction of the next end-effector target (8 compass sectors or hold) and step size (3 levels); previous move in the state
text CC-BY-4.0 / CC-BY-SA-3.0 / MultiNLI terms choice, noul, score text-only replay from Kev's training sources (banking77, BoolQ, MultiNLI), no image
blank CC0-1.0 (generated) choice an empty image of a random colour with a question it cannot answer; uniform soft target (teaches "I can't tell")
pope COCO images; POPE annotations (MIT) noul test_ood only: POPE object-presence questions (random / popular / adversarial), balanced
scienceqa CC-BY-NC-SA-4.0 choice ScienceQA questions that have an image; the hint passage goes in the state
rvl_cdip IIT-CDIP terms (research only) choice document type among 6 of the 16 RVL-CDIP classes
ava DPChallenge photos (research only) score aesthetic score 1–10; soft target = the voters' histogram (≥ 50 votes)
text_full AG News (non-commercial), SST, Yelp dataset terms choice, noul, score text-only replay from AG News, SST-5, Yelp
countbench unspecified (LAION images) score test_ood only: CountBenchQA counting, 11 levels
ai2d unspecified (AI2 diagrams) choice test_ood only: AI2D science-diagram multiple choice

Splits and counts

train / validation / test are disjoint within each source (the source's own splits where it has them; otherwise by image id or episode). test_ood holds sources that are never used for training (out-of-domain evaluation). VQAv2 and POPE both come from COCO val2014: every POPE image is excluded from VQAv2, and A-OKVQA (COCO 2017, which contains val2014) skips any photo matching a POPE or VQAv2 validation/test image by perceptual hash.

source split records choice noul score
vqav2 test 199 0 318 83
vqav2 train 24782 0 40749 9802
vqav2 validation 1249 0 2010 516
aokvqa test 200 200 0 0
aokvqa train 11999 11999 0 0
aokvqa validation 518 518 0 0
eurosat test 200 200 198 0
eurosat train 9000 9000 8992 0
eurosat validation 450 450 432 0
pets test 200 200 124 0
pets train 3500 3500 2274 0
pets validation 175 175 130 0
fairface_age test 200 0 0 200
fairface_age train 7500 0 0 7500
fairface_age validation 375 0 0 375
chartnet test 200 200 182 0
chartnet train 9000 9000 8990 0
chartnet validation 450 450 444 0
documents test 200 200 188 0
documents train 10225 10225 10056 0
documents validation 575 575 570 0
pong test 158 158 0 0
pong train 1500 1500 0 0
pong validation 64 64 0 0
breakout test 200 200 0 0
breakout train 1500 1500 0 0
breakout validation 46 46 0 0
freeway test 188 188 0 0
freeway train 1500 1500 0 0
freeway validation 72 72 0 0
spaceinvaders test 200 200 0 0
spaceinvaders train 1500 1500 0 0
spaceinvaders validation 55 55 0 0
mspacman test 179 179 0 0
mspacman train 1500 1500 0 0
mspacman validation 69 69 0 0
pusht test 200 200 0 185
pusht train 6000 6000 0 5462
pusht validation 300 300 0 277
text test 200 133 67 0
text train 7998 5332 2666 0
text validation 400 262 138 0
blank test 200 200 0 0
blank train 4500 4500 0 0
blank validation 225 225 0 0
pope test_ood 486 0 486 0
scienceqa test 139 139 0 0
scienceqa train 6000 6000 0 0
scienceqa validation 197 197 0 0
rvl_cdip test 180 180 0 0
rvl_cdip train 4000 4000 0 0
rvl_cdip validation 180 180 0 0
ava test 200 0 0 200
ava train 6000 0 0 6000
ava validation 300 0 0 300
text_full test 200 64 197 136
text_full train 4998 1666 4998 3332
text_full validation 250 79 247 171
countbench test_ood 491 0 0 491
ai2d test_ood 500 500 0 0

Balance audit (train split)

majority is the accuracy of always giving the most frequent answer, i.e. what a model that ignores the image would score; copy prev is the accuracy of repeating the previous action (control records that show it). Train images shared with validation/test: none.

question n classes majority yes rate copy prev answer is option 1
agnews 1666 4 0.256 0.238
agnews_yn 3332 2 0.755 0.245
aokvqa 11999 4 0.253 0.254
ava 6000 9 0.563
banking77 2666 77 0.022 0.013
blank 4500 10 0.105 0.232
boolq 2666 2 0.613 0.613
breakout_action 1500 4 0.359 0.476 0.275
chartnet_type 9000 20 0.092 0.207
chartnet_yn 8990 2 0.5 0.5
documents_type 10225 8 0.147 0.197
documents_yn 10056 2 0.5 0.5
eurosat 9000 10 0.116 0.203
eurosat_yn 8992 2 0.5 0.5
fairface_age 7500 9 0.3
freeway_action 1500 3 0.629 0.689 0.342
mnli 2666 3 0.344 0.336
mspacman_action 1500 9 0.15 0.571 0.095
pets_breed 3500 37 0.028 0.199
pets_dog 2274 2 0.5 0.5
pong_action 1500 3 0.443 0.473 0.334
pusht_direction 6000 9 0.127 0.745 0.11
pusht_step 5462 3 0.415
rvl_cdip 4000 16 0.089 0.165
scienceqa 6000 5 0.362 0.367
spaceinvaders_action 1500 6 0.199 0.583 0.171
sst5 1666 5 0.269
vqav2_count 9802 6 0.289
vqav2_yn 40749 2 0.5 0.5
yelp 1666 5 0.207
yelp_yn 1666 2 0.613 0.387

Licences

This dataset is a derivative of the sources above; each record keeps its source's licence in the license column and you must follow it. COCO photos (VQAv2, A-OKVQA, POPE) remain under their original Flickr licences.