Kled: Real-World Capture
Collection
Phone photos, screenshots and short video of streets, shelves, screens, scams, weather and packaging as contributors meet them. • 54 items • Updated
Datasets:
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Dataset Card for Home Damage, Three Frames
Three-shot sets of household damage (damp, cracks, leaks, peeling paint) from whole room to close-up, defect type named.
Collected through the Kled app by consenting contributors and licensed for commercial training. The files are delivered by Kled under license, not downloaded from this repository. Use Request a sample set at the top of this page, or write to team@kled.ai. Catalog entry: kled.ai/datasets/damage-in-your-home.
Dataset Details
Dataset Description
Each submission is three photographs of one defect in a contributor's home somewhere in Kled's global base: the whole room or wall, then closer, then the defect itself, with the issue type named (damp, crack, leak, peeling paint, broken tile). Close-up-only sets and unidentifiable issues do not get through review, and contributors are told not to create damage or include anything that identifies their address. Insurance and inspection models are trained on claims imagery that insurers do not license, and the open alternatives are small crack datasets; the wide-to-close structure here teaches a model to localize a defect in context rather than classify a crop.
- Curated by: Kled (useKled)
- Collected through: Phone camera (in app)
- Regions: Global, 100+ countries
- Volume: 10,000+ photo sets (approx. 90 GB)
- Contributors: 10,000+
- Modality: Images
- Format: JPG / PNG / HEIC
- Resolution: Native phone camera resolution (JPG, PNG, HEIC)
- Annotations: Labeled (defect type)
- Quality control: 99%+ passed QA review
- License: Non-exclusive commercial training license (terms); exclusivity available on request
- Consent: Each contributor opted in to this use when they uploaded.
Dataset Sources
- Catalog page: kled.ai/datasets/damage-in-your-home
- Sample set: through the access request at the top of this page, or team@kled.ai
- Publisher: huggingface.co/useKled
Uses
Direct Use
- Property defect detection and localization
- Insurance claims triage
- Multi-scale visual grounding
Out-of-Scope Use
Redistribution or resale of the files, use outside the terms of the commercial training license, and any attempt to identify or contact contributors or people who appear in the data. Uses that require labels this set does not carry should be scoped with the data team first.
Dataset Structure
Data Instances
One photo set per record, delivered as JPG / PNG / HEIC. Volume: 10,000+ photo sets.
Data Fields
- File: the JPG / PNG / HEIC file as captured or uploaded by the contributor
- Labels: Labeled (defect type), entered by the contributor at capture and checked at review.
- Capture metadata: capture tool, country of capture, submission time
- Review status: QA outcome (99%+ passed QA review)
Field names and the manifest format are confirmed with the sample set.
Data Splits
Delivered as one set. Subsets by country, language or label, and custom splits, are available on request.
Dataset Creation
Curation Rationale
The dataset exists because the public alternatives named in the description are non-commercial, synthetic, small or collected elsewhere. Kled runs the task as a paid brief in its app so that the data is captured on purpose, under the capture rules above, by people who have opted in to commercial use.
Source Data
Data Collection and Processing
Contributors complete the task in the Kled app. Capture tool: Phone camera (in app). Each submission is reviewed against the task brief; submissions that break the capture rules are rejected. 99%+ passed QA review. Files are delivered as JPG / PNG / HEIC, in the resolution noted above.
Who are the source data producers?
Kled contributors, who complete paid data tasks on their own phones. This task was open to contributors worldwide. Kled has 625,000+ contributors in 100+ countries, strongest in Southeast Asia, the United States and Europe.
Annotations
Annotation process
Labeled (defect type). The label is entered by the contributor as part of the task and checked at review against the task brief; it is a contributor label, not an expert annotation, unless the description above says otherwise.
Who are the annotators?
The contributors themselves, at the moment of capture. Kled reviewers check each submission against the task brief.
Personal and Sensitive Information
Every item was contributed by its owner, who opted in to this use at upload. The task brief states what may and may not appear in a submission, and reviewers reject submissions that break those rules. Kled's data handling practices are documented at trust.kled.ai; questions about handling requirements for your use go to team@kled.ai.
Bias, Risks, and Limitations
Coverage follows the contributor base: strongest in Southeast Asia, the United States and Europe, thinner elsewhere, and not limited by region for this task. Devices, settings and conditions vary by contributor, which is the point of the collection and also means no two items are captured under identical conditions. Where labels are contributor-entered, they are checked at review but are not expert annotations. Volumes are the current scope of the task and grow as collection continues.
Recommendations
Request a sample set and run it against your task before licensing. Ask for per-country or per-language counts where they matter to your use.
Licensing Information
Non-exclusive commercial training license; exclusivity on request. Each contributor opted in to this use when they uploaded. The license terms are on the catalog page.
Citation
@misc{kled_damage_in_your_home_2026,
title = {Home Damage, Three Frames},
author = {Kled},
year = {2026},
url = {https://www.kled.ai/datasets/damage-in-your-home},
note = {Commercially licensed dataset collected through the Kled app. Sample sets on request.}
}
Dataset Card Contact
Kled data team: team@kled.ai
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