VisVerify-Bench

A benchmark of vision annotation errors for VLM-as-judge and System One vision models.

Each item shows a judge an image with a proposed class, box or mask. Half the items are untouched ground truth and should be accepted. The other half were corrupted in a controlled way and should be rejected. Every wrong item records what is wrong (error_type) and how badly (iou_with_gt), so accuracy can be plotted against severity. Built to compare fast "System One" decision models (JEV, Liquid d1-omni) against VLM-as-judge baselines on the same items.

• 3 tasks (classification, detection, segmentation) x 8 domains (everyday, street, aerial, medical, industrial, agriculture, documents/UI, wildlife/underwater)
• 6,584 items: 5,988 test, 596 dev (separate photos, for prompt tuning only)
• 50% correct / 50% corrupted in every task x domain cell; 16 error types, easy / medium / hard

Quick start

from datasets import load_dataset
import json

ds = load_dataset("FaisaI/VisVerify-Bench", "detection", split="test")
q = json.load(open("questions.json"))["detection"]          # question templates, from this repo

row = ds[0]
row["judge_image"]      # crop around the proposed box, 50% padding, box + class tag drawn
row["proposed_class"]   # "bus"
row["expected"]         # {"class_correct": True, "box_quality": 2, "annotation_correct": False}

Score answers against expected. Main metric: precision / recall / F1 on annotation_correct with reject as the positive class, plus the false-alarm rate on correct items.

Error types

Task	error_type	Corruption	Severity
all	class_swap_random / _similar / _cross_domain	class replaced by a random / semantically close / other-domain class	easy / hard / easy
det	box_slightly_off / box_off / box_very_off	box moved to IoU 0.75-0.85 / 0.50-0.75 / 0.20-0.50	hard / medium / easy
det	box_wrong_object / box_background	box on another object / on empty background	medium / easy
seg	mask_eroded / mask_dilated	morphology to IoU 0.60-0.85	medium / hard
seg	mask_missing_part / mask_leak / mask_shifted	20-40% chunk removed / grown into background / translated to IoU 0.40-0.70	medium / medium / easy
seg	mask_wrong_instance	mask of another instance of the same class	hard
det, seg	class_and_box / class_and_mask	class swap and a geometry error	easy

20% of correct boxes and masks carry a tiny jitter (IoU >= 0.92, tag tp_jittered) so "pixel-perfect" cannot be used as a shortcut. Quality scores map from IoU: 5 (>= 0.90), 4 (0.75), 3 (0.60), 2 (0.40), 1 (below).

Fields

id, task, split, domain, source_dataset, source_id, license · image (original; the crop for classification), judge_image (default rendering) · proposed_class, class_description, gt_class · proposed_bbox, gt_bbox as [x, y, w, h] pixels · proposed_mask, gt_mask as COCO RLE · is_correct, error_type, severity, iou_with_gt · choice_options, choice_answer (4-way question, classification) · expected · tags · notes

Questions live once in questions.json. Images are at most 1024 px on the long side; raw geometry is kept so you can re-render (overlay_full, crop_tight, crop_context, side_by_side in the code repo). At most 3 items per photo, no class above 10% of a domain.

Counts

classification

domain	none	class_swap_cross_domain	class_swap_random	class_swap_similar	total
aerial	165	81	43	41	330
agriculture	165	79	43	43	330
documents_ui	165	55	55	55	330
everyday	165	55	55	55	330
industrial	165	55	55	55	330
medical	165	55	55	55	330
street	165	55	55	55	330
wildlife_underwater	165	55	55	55	330
total	1320	490	416	414	2640

domain	dev	test
aerial	30	300
agriculture	30	300
documents_ui	30	300
everyday	30	300
industrial	30	300
medical	30	300
street	30	300
wildlife_underwater	30	300

Severity: easy=906, hard=414, none=1320

detection

domain	none	box_background	box_off	box_slightly_off	box_very_off	box_wrong_object	class_and_box	class_swap_cross_domain	class_swap_random	class_swap_similar	total
aerial	165	18	18	18	18	18	24	17	17	17	330
agriculture	165	19	18	18	18	18	24	17	17	16	330
documents_ui	165	18	18	18	18	18	24	17	17	17	330
everyday	165	18	18	18	18	18	24	17	17	17	330
industrial	165	19	18	18	18	17	24	18	17	16	330
medical	165	18	18	18	18	18	24	17	17	17	330
street	165	18	18	18	18	18	24	17	17	17	330
wildlife_underwater	165	18	19	18	18	17	24	18	17	16	330
total	1320	146	145	144	144	142	192	138	136	133	2640

domain	dev	test
aerial	30	300
agriculture	30	300
documents_ui	30	300
everyday	30	300
industrial	30	300
medical	30	300
street	30	300
wildlife_underwater	30	300

Severity: easy=756, hard=277, medium=287, none=1320

segmentation

domain	none	class_and_mask	class_swap_cross_domain	class_swap_random	class_swap_similar	mask_dilated	mask_eroded	mask_leak	mask_missing_part	mask_shifted	mask_wrong_instance	total
everyday	165	24	17	17	16	16	15	15	15	15	15	330
medical	165	0	28	0	0	28	28	27	27	27	0	330
street	157	23	16	16	16	15	15	15	14	14	13	314
wildlife_underwater	165	24	17	17	16	16	15	15	15	15	15	330
total	652	71	78	50	48	75	73	72	71	71	43	1304

domain	dev	test
everyday	30	300
medical	30	300
street	26	288
wildlife_underwater	30	300

Severity: easy=270, hard=166, medium=216, none=652

Sources and licenses

Source	Where	License	Redistributable	Tasks	Notes
COCO 2017 val	images.cocodataset.org	Annotations CC BY 4.0; images carry individual Flickr CC licenses	Yes, with per-image filter	cls, det, seg	Keep only images whose COCO license id is 4 (CC BY 2.0), 5 (CC BY-SA 2.0), 7 (no known copyright) or 8 (US Gov). Record the per-image license in license.
LVIS v1 val	dl.fbaipublicfiles.com/LVIS	Annotations CC BY 4.0; same COCO images	Yes, same filter	cls, det, seg	1,203 classes, good for class_swap_similar.
Open Images V7 validation	storage.googleapis.com/openimages	Annotations CC BY 4.0; images CC BY 2.0	Yes	cls, det, seg	Masks come from the V5 mask release (validation-masks-*.zip + CSV). Google does not guarantee each image's license; attribute per image.
RF100-VL	HF probicheaux/rf100-vl	Apache 2.0 (stated in roboflow/rf100-vl repo)	Yes	cls, det	100 subsets across domains. Boxes only: checked 2026-10-30, the *-seg subsets carry no polygons.
PlantDoc	HF agyaatcoder/PlantDoc	CC BY 4.0	Yes	cls, det	Original: github.com/pratikkayal/PlantDoc-Object-Detection-Dataset.
ISIC 2016 Part 1	HF MedOtter/ISIC2016	CC0 1.0	Yes	cls, det, seg	1,279 dermoscopy images with expert lesion masks. Single class, so classification distractors come from cross-domain swaps.
DocLayNet v1.1	HF ds4sd/DocLayNet-v1.1	CDLA-Permissive 1.0	Yes	cls, det	Some HF mirrors tag license: other; the card body and the IBM page say CDLA-P 1.0.
PubLayNet	HF creative-graphic-design/PubLayNet	CDLA-Permissive 1.0	Yes	cls, det

No non-commercial source is used. COCO and LVIS photos keep their individual Flickr licenses (only CC BY, CC BY-SA, public domain and US Government photos were used) and each row records its own. Segmentation covers 4 domains; aerial, industrial and agriculture had no permissively licensed mask source, and documents is detection-only.

Citation

@misc{visverifybench2026,
  title  = {VisVerify-Bench: Vision Annotation Errors for VLM Judges and System One Models},
  author = {Faisal},
  year   = {2026},
  url    = {https://huggingface.co/datasets/FaisaI/VisVerify-Bench}
}

Sources: COCO (Lin et al. 2014), LVIS (Gupta et al. 2019), Open Images (Kuznetsova et al. 2020), Roboflow100-VL (Robicheaux et al. 2025), PlantDoc (Singh et al. 2020), ISIC 2016 (Gutman et al. 2016), DocLayNet (Pfitzmann et al. 2022), PubLayNet (Zhong et al. 2019).