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
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Dataset Card for Damaged Parcels, Three Frames
Three-photo sets of damaged deliveries in Southeast Asia: whole item, damage close-up, packaging with its label.
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/damaged-deliveries.
Dataset Details
Dataset Description
Photograph the whole item out of the box, the damaged part close up, and the packaging with its shipping label, then answer two questions about the damage. Close-up-only sets, sets without the packaging, mixed items and damage that predates shipping are rejected, one set per contributor. The task is open only in 11 Southeast Asian countries, where parcels travel by motorbike courier through monsoon rain and get repacked in transit, a failure distribution temperate-climate logistics data lacks. The packaging frame is the causal evidence (crushed corners, water staining, retaping, the carrier's own markings), so a model can learn why as well as that; logistics damage data otherwise lives inside insurers' and retailers' claims systems.
- Curated by: Kled (useKled)
- Collected through: Phone camera (in app)
- Regions: Southeast Asia (11 countries)
- Volume: 5,000+ photo sets (approx. 45 GB)
- Contributors: 5,000+
- Modality: Images
- Format: JPG / PNG / HEIC
- Resolution: Native phone camera resolution (JPG, PNG, HEIC)
- Annotations: Labeled (questionnaire: damage)
- 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/damaged-deliveries
- Sample set: through the access request at the top of this page, or team@kled.ai
- Publisher: huggingface.co/useKled
Uses
Direct Use
- Delivery damage detection and claims triage
- Packaging-condition assessment
- Logistics failure analysis
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: 5,000+ photo sets.
Data Fields
- File: the JPG / PNG / HEIC file as captured or uploaded by the contributor
- Labels: Labeled (questionnaire: damage), 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 in the 11 Southeast Asian countries where the task runs. Kled has 625,000+ contributors in 100+ countries, strongest in Southeast Asia, the United States and Europe.
Annotations
Annotation process
Labeled (questionnaire: damage). 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 limited to the 11 Southeast Asian countries where the task runs 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_damaged_deliveries_2026,
title = {Damaged Parcels, Three Frames},
author = {Kled},
year = {2026},
url = {https://www.kled.ai/datasets/damaged-deliveries},
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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