Fiund / How it works
A clear brief. A defined review. A useful handoff.
An image count is only the beginning of a photo dataset brief. Useful coverage depends on subject diversity, capture conditions, class balance, source relationships, and annotation quality. Photo Datasets is a Fiund site for buyers exploring image sourcing for computer vision and visual evaluation.
Project workflow
Six stages to discuss with Fiund.
Define the target
An image count is only the beginning of a photo dataset brief. Useful coverage depends on subject diversity, capture conditions, class balance, source relationships, and annotation quality. Photo Datasets is a Fiund site for buyers exploring image sourcing for computer vision and visual evaluation.
Write the specification
Subjects and class taxonomy; Counts and required visual diversity; Image formats and minimum dimensions; Annotation type and review rules; Permitted uses and exclusions. These details establish the project’s review criteria and reveal where further clarification is needed.
Review sourcing fit
Discuss which existing sources or collection approaches could address the brief. Identify access constraints, source documentation, and unanswered permission questions before treating a category as usable inventory.
Inspect representative material
Where appropriate material can be shared, agree on handling and inspect it against the specification. Record defects, coverage gaps, and annotation questions against exact source identifiers.
Agree the project scope
Confirm the selected material, intended uses, preparation work, acceptance criteria, commercial terms, and delivery arrangements in a project-specific agreement.
Review the handoff
Check file references, manifests, documented processing, and accepted exceptions. Keep permission review and technical acceptance explicit, and resolve open issues before the collection is treated as approved for the intended use.
Category decisions
Build the right starting brief.
Coverage plan
Class list, counts per class, environments, geographic scope, and real versus synthetic requirements.
Image properties
Dimensions, orientation, file format, compression tolerance, and whether originals are required.
Annotation rules
Label taxonomy, box or mask conventions, ambiguous cases, and quality-review criteria.
Source relationships
Burst sequences, near duplicates, photographer groups, and separation of evaluation examples.
Common questions
Before you source.
Can I request real photographs rather than generated images?
Yes. Make that distinction explicit in your brief, including whether edited, composited, or synthetic examples are excluded. The collection should be reviewed against that requirement.
Do all computer vision projects need annotation?
No. Some use image-text pairs, image-level labels, or unlabeled source images. Tell us the learning or evaluation task so the annotation discussion matches the actual need.
Can a sample prove class balance for the whole collection?
A sample can help assess style and labeling, but complete class counts and source documentation are needed to evaluate the proposed collection. Ask for the full coverage plan before deciding.