Home·Curated Catalog·Computer Vision
👁 Curated Catalog · Computer Vision

Open Images V7

9M images with 36M image-level labels, 16M bounding boxes, and 2.7M segmentation masks.

LQS 86 · gold ✓ Commercial OK 9M images 561 GB JPG · CSV Released 2016
Browse commercial Computer Vision → Visit original source ↗
Source: storage.googleapis.com · maintained by Google Research
9M
images
561 GB
Size on disk
86
LQS · gold
2016
First released

About this dataset

Open Images is Google's large-scale computer vision dataset. V7 spans 9M images with 36M image-level labels across 19,957 classes, 16M bounding boxes across 600 object categories, 2.7M instance segmentation masks, 3.3M visual relationship annotations, and point-level localizations. A strong alternative to COCO when broader label vocabulary matters.

Maintainer
Google Research
License
CC BY 4.0 (annotations) / varies (images)
Formats
JPG · CSV
Paper
Read on arxiv.org →

LabelSets Quality Score

LQS is our 7-dimension quality score, computed from the dataset's published statistics. See methodology →

86
out of 100
gold tier

High-quality dataset across most dimensions

Composite score computed from the 7 dimensions below: completeness, uniqueness, validation health, size adequacy, format compliance, label density, and class balance.

Completeness 95
No public completeness metric; using prior for 'expert_curated' datasets.
Uniqueness 93
Exact-hash deduplication documented by maintainer.
Validation 82
Crowdsourced labels with quality-control protocol (redundancy, golden tests).
Size adequacy 96
9,000,000 images — exceeds 100,000 adequacy target for Computer Vision.
Format compliance 95
Industry-standard format — drop-in compatible with mainstream tooling.
Label density 68
Average 4.0 labels per item (moderate).
Class balance 58
Long-tail distribution — dominant classes overrepresented.

What it's used for

Common tasks and benchmarks where Open Images V7 is the default or competitive choice.

Sample statistics

What's actually in the dataset — from the maintainer's published stats.

9M images, 36M image-level labels across 19,957 classes, 16M bounding boxes across 600 classes, 2.7M segmentation masks, 3.3M visual relationship triplets.

License

Open Images V7 is distributed under CC BY 4.0 (annotations) / varies (images). This is a third-party public dataset; LabelSets indexes and scores it but does not host or redistribute the data. Always verify current license terms with the maintainer before commercial use.

Need commercial-licensed Computer Vision data?

LabelSets sellers offer paid computer vision datasets with what public datasets often can't give you:

Browse paid Computer Vision → Sell your dataset

Similar public datasets

Other entries in the Computer Vision catalog.

COCO — Common Objects in Context
330K images · CC BY 4.0
ImageNet
14.2M images · Custom (non-commercial research)
SA-1B — Segment Anything
1.1B segmentation masks · SA-1B Research License
CIFAR-100
60K images · MIT-style (unrestricted research use)

Frequently Asked Questions

Open Images V7 is distributed under CC BY 4.0 (annotations) / varies (images), which generally permits commercial use. Always verify the current license terms with the maintainer (Google Research) before using in a commercial product.
Open Images V7 contains 9,000,000 images. 9M images, 36M image-level labels across 19,957 classes, 16M bounding boxes across 600 classes, 2.7M segmentation masks, 3.3M visual relationship triplets.
Open Images V7 is maintained by Google Research and is available at https://storage.googleapis.com/openimages/web/download_v7.html. LabelSets indexes and scores this dataset for discoverability but does not redistribute it.
LQS is a 7-dimension quality score (completeness, uniqueness, validation, size adequacy, format compliance, label density, class balance) computed from the dataset's published statistics. Composite scores map to tiers: platinum (≥90), gold (≥75), silver (≥60), bronze (<60). Read the full methodology.