| # Annotation and agreement |
|
|
| Annotators assessed each image's plausibility and naturalness, selected |
| violation classes, and drew a box around each labelled instance. They could |
| instead mark no violation or report an unusable image. The released first-pass |
| records are preserved separately from the resolved final labels. |
|
|
| ## The rating rubric |
|
|
| Plausibility asks whether the depicted situation could occur in Singapore. |
| Naturalness asks whether the pixels resemble a real photograph. An unusual |
| scenario can be plausible, and a plausible scenario can look visibly generated. |
| Annotators were instructed to judge these dimensions separately. |
|
|
| | Rating | Plausibility: could this happen here? | Naturalness: does it look real? | |
| | ------ | ------------------------------------- | ------------------------------- | |
| | 1 | Impossible | Obvious fake | |
| | 2 | Unlikely | Suspicious | |
| | 3 | Possible | Uncertain | |
| | 4 | Likely | Convincing | |
| | 5 | Certain | Indistinguishable | |
|
|
| The naturalness guidance highlights visual artefacts such as gibberish text, |
| extra fingers and warped edges. These are whole-image ratings, not separate |
| ratings for each violation box. |
|
|
| ## Which annotations are compared? |
|
|
| There are 23,710 first-pass submissions for the released 11,534 images: 10,905 |
| images have two raters, 616 have three, and 13 have four. One submission reports |
| an unusable image and is excluded from eligible comparisons. Its original record |
| remains in the export; that image has other usable first passes and a usable |
| final annotation. |
|
|
| For label-overlap and box matching, every eligible rater pair is compared and |
| pair counts are averaged within each image. More raters do not multiply that |
| image's pair-count contribution. Exact class-set agreement instead requires all |
| eligible raters on an image to select the same set. Final gold is not included |
| as another rater: agreement is measured before the resolution process described |
| in [ground-truth methods](ground-truth-methods.md). |
|
|
| ## Reading the agreement results |
|
|
| | Measure | What it answers | |
| | ----------------------------------- | ------------------------------------------------------------------------------------------------------- | |
| | Ordinal Krippendorff's alpha | How consistent are quality ratings after accounting for chance disagreement and their ordering? | |
| | Exact / within-one rating agreement | How often do all eligible ratings on an image match, or span at most one point? | |
| | Exact class-set agreement | How often do annotators select precisely the same classes, including agreed negatives? | |
| | Positive-label agreement | How much do their selected class sets overlap, without giving credit merely for both selecting nothing? | |
| | Instance F1 | How well do their individual boxes match one to one, with the same class and IoU at least 0.50? | |
|
|
| Positive-label agreement uses twice the number of shared labels divided by the |
| sum of both label-set sizes. Instance F1 uses twice the number of matched boxes |
| divided by the total number of boxes from both annotators. The published |
| estimates weight pair counts equally within each image, pool those weighted |
| counts across images, then calculate the ratio. They do not average pairwise F1 |
| scores and are not model accuracy scores against gold. |
|
|
| Alpha can be negative when disagreement exceeds its chance baseline. An |
| undefined statistic is stored as null with its reason rather than interpreted as |
| zero. Per-class nominal alpha concerns presence/absence of an individual class; |
| it is not pooled into a single alpha across different classes. Interval alpha |
| and alternative IoU thresholds are secondary sensitivity measures. |
|
|
| Intervals resample images, preserving all their raters, for 2,000 bootstrap |
| replicates. The artifact records the fixed seed. Domain label/box agreement uses |
| all images with only that domain's classes; domain quality agreement selects |
| images with a final gold label in that domain. Quality-selected subsets overlap. |
| Support counts and limited-support flags remain in the detailed results. |
|
|
| [Agreement JSON](../evaluation/dataset/annotation_agreement.json) contains |
| `groups` for the population/domain/class estimates and `bootstrap` for interval |
| settings. The [CSV](../evaluation/dataset/agreement.csv) provides tabular |
| results. The |
| [reproduction guide](evaluation.md#reproduce-the-published-primary-results) |
| explains how to recalculate them from the released first passes. |
|
|
| ## Final quality ratings are a different result |
|
|
| [Image-quality results](../evaluation/dataset/image_quality.json) describe the |
| resolved ratings: mean plausibility is 3.413 and mean naturalness is 3.372, with |
| median 4 for both. Its `slices` contains overall, domain and class |
| distributions. Those descriptive mean intervals use 1,000 image-bootstrap |
| replicates, unlike the 2,000 used for first-pass agreement. |
|
|
| Resolution can select the minimum human rating or an explicitly adjudicated |
| rating. Consequently these distributions describe the released annotations; they |
| are not an average of independent human opinions. Domain/class slices refer to |
| whole-image ratings and overlap on multi-label images. |
|
|
| The exports do not establish independence from shared prefill. Agreement can |
| reflect that workflow as well as rater consistency, and neither agreement nor |
| final resolution proves label correctness. See the |
| [limitations](limitations.md#annotation-dependence-and-resolved-labels). |
|
|
