ImgObjectRemover
Public test method

How we test AI object removal

This page defines the test before results are published. Metrics remain Unknown until the fixed sample set, environment record, raw task log and calculation sheet have been archived and reviewed.

Method established 28 July 2026 · Baseline measurement pending

Sample design

The planned baseline contains 100 licensed or internally authorized images. Each image is assigned to one primary category before testing, deduplicated by file hash and run once in randomized order. A 20-image subset is rerun to inspect repeatability; those repeat runs are reported separately and never added to the headline success-rate denominator.

CategoryImagesCoverage
People and crowds20Hair, limbs, overlapping subjects, partial occlusion
Product photos20Plain and contextual backgrounds, reflections, packaging
Complex textures20Grass, brick, water, fabric, repeated patterns
Small distractions20Wires, signs, litter, logos and edge objects
Hard cases20Large occlusions, shadows, text and ambiguous instructions

Conditions recorded for every run

Every raw record must include the source reference, display authorization, SHA-256 file hash, width, height, byte size, format, exact prompt, UTC timestamp, test region, browser or API client, model identifier returned by the service, task ID, credit cost, status code and result dimensions.

  • No silent replacement of failed images or prompts.
  • The same production endpoint, credit rules and storage path used by customers.
  • Warm-up requests are labeled and excluded before the test starts.
  • Timeout, provider, storage and credit errors remain in the raw log.
  • Raw images are not made public unless the authorization explicitly permits it.

Metrics and current status

Sample count

100 planned test images

A fixed, deduplicated set: 40 images with longest side ≤1024px, 40 at 1025–2048px, and 20 above 2048px. Format target: 40 JPG, 30 PNG, 30 WebP.

Dimension retention rate

Unknown — baseline not run

Successful outputs whose pixel width and height both equal the source, divided by all successful outputs. A changed file format does not count as a dimension change.

p50 / p95 duration

Unknown — baseline not run

Wall-clock duration from accepted POST request to the successful API response. Reported in seconds by test region, with p50 as the median and p95 as the 95th percentile.

Success rate

Unknown — baseline not run

Tasks that return a readable, downloadable result and are recorded as SUCCESS, divided by all submitted tasks. Retries keep the same task ID and do not inflate the denominator.

Reporting rule: every number must be labeled Measured, include the observation date and sample count, and link to a versioned test summary. Estimates and proxy measurements cannot be presented as test results.

Known limitations

Object removal is generative editing, so an API success does not guarantee a visually acceptable edit. Large occlusions, faces and hands, text, logos, transparent objects, reflections, cast shadows, repeating patterns and objects touching the frame can produce reconstruction errors. Output dimensions or format may differ from the source. Results should be reviewed before publication or commercial use.

The baseline represents the declared sample and environment only. It does not predict performance for every image, location, network condition or future model version. Model, prompt, provider or pipeline changes require a new test version rather than silently replacing the old result.

Quarterly review and change control

On the first business week of January, April, July and October, the maintainer reviews pricing, active model identifiers, third-party processors and retention behavior against production configuration and provider records. Material changes update the relevant public page and create a dated entry in the review log.

Last baseline review28 July 2026Method defined; measured results still Unknown.
Next scheduled review1 October 2026Pricing, models, processors and retention.

See the Privacy Policy, credit rules and case-study publication standard for the current customer-facing controls.