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Places365

10M scene images across 365 everyday place categories — from MIT CSAIL.

LQS 83 · gold ⚠ Research-only 10M images 112 GB JPG · TXT Released 2015
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Source: places2.csail.mit.edu · maintained by MIT CSAIL
10M
images
112 GB
Size on disk
83
LQS · gold
2015
First released

About this dataset

Places365 is a scene recognition benchmark from MIT CSAIL. 10M images labeled with 365 semantic scene categories covering everyday environments (indoor, outdoor, urban, natural). Widely used for scene classification, transfer learning, and as a complementary pretraining source to ImageNet.

Maintainer
MIT CSAIL
License
CC BY (research use)
Formats
JPG · TXT
Paper
Read on places2.csail.mit.edu →

LabelSets Quality Score

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

83
out of 100
gold tier

Solid dataset with some trade-offs

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

Completeness 88
No public completeness metric; using prior for 'crowdsourced_qc' datasets.
Uniqueness 93
Exact-hash deduplication documented by maintainer.
Validation 82
Crowdsourced labels with quality-control protocol (redundancy, golden tests).
Size adequacy 96
10,000,000 images — exceeds 100,000 adequacy target for Computer Vision.
Format compliance 95
Industry-standard format — drop-in compatible with mainstream tooling.
Label density 52
Average 1.0 labels per item (sparse).
Class balance 58
Long-tail distribution — dominant classes overrepresented.

What it's used for

Common tasks and benchmarks where Places365 is the default or competitive choice.

Sample statistics

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

10M training images across 365 scene classes (~27K per class on average, with long-tail distribution). Challenge subset: 1.8M images.

License

Places365 is distributed under CC BY (research use). 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.

Heads up: this dataset's license restricts commercial use. If you need computer vision data for production, check LabelSets' paid datasets below — every listing has an explicit commercial license.

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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
Open Images V7
9M images · CC BY 4.0 (annotations) / varies (images)

Frequently Asked Questions

Places365 is distributed under CC BY (research use), which restricts commercial use. For a commercially-licensed alternative in computer vision, see LabelSets' paid datasets.
Places365 contains 10,000,000 images. 10M training images across 365 scene classes (~27K per class on average, with long-tail distribution). Challenge subset: 1.8M images.
Places365 is maintained by MIT CSAIL and is available at http://places2.csail.mit.edu/download.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.