Solutions / Image
Image data that matches where your model runs.
Real photographs from real devices in real places — objects, scenes, documents, faces (consented), and gestures — collected across the geographies, lighting, and hardware your computer-vision model has to survive.
Definition
Image data collection
Image data collection is the sourcing of photographs from real contributors under a defined spec — covering the objects, conditions, devices, and diversity a computer-vision model needs — with consent recorded for any identifiable person.
What we collect
The formats teams ask us for.
Not an exhaustive menu — if what you need is not here, it is a custom collection, which we also do.
Object & scene imageryTarget objects and environments across angles, distances, backgrounds, and clutter levels.
Device & sensor diversityCaptured on the phone models, cameras, and conditions your users actually have.
Document & receipt captureReal-world documents, receipts, and forms for OCR and document AI, in-region and in-language.
Consented faces & peopleFacial and full-body imagery with signed releases, balanced across demographics for fairness testing.
Gesture & hand imageryStatic hand shapes and gestures for interaction and sign-adjacent systems.
Edge & failure casesThe hard frames — glare, motion blur, occlusion, odd angles — that decide real-world accuracy.
What shapes the spec
The decisions we settle before collecting.
- Geographic reach
- 150+ countries
- Device coverage
- Specific phone/camera models and OS versions on request
- Conditions
- Daylight, low light, mixed, indoor/outdoor, cluttered — sampled to your matrix
- Delivery
- Original-resolution images with EXIF, capture metadata, and any required labels
- Consent basis
- Written release for any identifiable individual; bystanders de-identified or dropped
Where it is used
What teams train with it.
- Object detection and classification in new regions
- OCR and document understanding across languages and formats
- Face and person models with balanced, consented demographics
- Fairness and bias evaluation across skin tones and geographies
- Robustness datasets built from real edge and failure cases
How we run it
The standards behind every batch.
These apply to every modality — they are the reason the data is usable rather than merely large.
- Collected to a written spec
- Nothing is gathered speculatively. We agree the target — languages, demographics, devices, environments, edge cases — before a single contributor is briefed.
- Contributors paid hourly
- People are paid for their time, including setup and retakes, not a bounty per item. A per-item rate optimises for volume and quietly wrecks quality.
- Documented, informed consent
- Every contributor signs a release granting the usage rights you need before collection begins. You receive the consent artefacts with the batch.
- Discard rather than downgrade
- If an item cannot meet the spec or a bystander cannot be de-identified, it is dropped — not shipped at a discount to pad the count.
- Multi-layer QA with a visible reject log
- Automated checks plus human review, and you see the reject reasons, not just the accepted items.
- Buyer-owned commercial license
- You receive a perpetual, buyer-owned license with a data card recording jurisdictions of capture and the consent basis.
FAQ
Image collection, answered.
- How do you ensure image diversity and avoid bias?
- We source contributors across regions, demographics, and devices against a coverage matrix agreed in the spec, and sample deliberately for the conditions and edge cases your model struggles with. Fairness checks run through collection, and the reject log shows what was dropped and why.
- Can you collect on specific devices or in specific countries?
- Yes. Because collection runs through a vetted global crowd across 150+ countries, we can target specific phone and camera models, OS versions, regions, and languages. We confirm what is reachable and the realistic timeline before you commit.
- How is consent handled for images of people?
- Any identifiable person signs a written release granting the usage rights you need before capture. Bystanders are de-identified, and any image where that is not possible is discarded rather than shipped. You receive the consent artefacts with the batch.
Scope a image collection.
Bring your spec or your problem. You will get a scoped estimate — reach, timeline, and price — before any commitment.