From model task to accepted label file
Image labels
your model team
can specify and accept
Bring the model task and source-image conditions. YPAI scopes the label type, task rules, review method, acceptance requirement and output format as one delivery decision.
Send your annotation briefWhat must the label file preserve?
Choose the output by the decision your model has to make. One frame, four choices, and four different things that survive into the file.
- 1 Classification
One class for the whole frame. Nothing about where the part is survives.
- 2 Box
Extent and position survive. The machined shape does not.
- 3 Polygon
The cut boundary survives, and the bore is carried as its own region.
- 4 Keypoints
Named landmarks only. Here, the three points of the tool axis. The dotted outline is context for reading them, not part of the label.
The annotation coordinate frame is 1586 × 992, and every coordinate in the chapters below is absolute in it. The photograph is rendered from a deterministic 3× display derivative of that same illustrative frame, so a region still carries pixels on a high-density screen. The derivative is a resample for display. It adds no source detail and it does not move the coordinate space.
Difficult images need explicit rules
Use your sample to name the cases that could change a label. The guideline then states what counts, when a reviewer steps in and which cases move to adjudication. All four regions below are read from the one frame above, each at its own origin, and none is shown above the pixels its rendering carries.
A hose is not its bounding box.
Traced as a path that carries its own width. The dashed box the same object would get is mostly belt. polyline · width 18
Dark machined metal against a dark cell.
The boundary follows the tool silhouette, flange top included, not the specular highlight down the barrel. mask
Label what is visible. Record what is hidden.
The run stops at the block edge. Nothing behind the block is drawn, so the hidden part stays a flag and never a guess. occluded: true
The bore belongs to the scene, not to the bracket.
Carried as its own region so the mask of the part is not quietly filled in. category: bore
How will the batch be accepted?
Agree the acceptance requirement before production. The pilot is where an observed result can be measured against it.
Every item leaves review in one of three states, decided against a requirement that was agreed before production, never after.
The agreed requirement names- The annotation type, and the geometry it has to produce.
- The rule for each difficult case your data actually contains.
- The review method that decides an item, and who adjudicates.
- The sample the requirement was fixed against before production.
- The output format the label files are delivered in.
- Human verification
- Dual annotation
- Specialist review
- Agreement analysis
- Sampling
- Task-specific quality gates
Which of these applies is a scoping decision for your workload.
- Accepted
The item meets the requirement agreed in the project scope.
- Returned for rework
The item goes back with the failing rule named, not a score.
- Escalated
The rule itself did not decide the case. It is still open, and it moves to adjudication.
The first four rows are the cases in the frame above. Small instance is listed with them because a size floor is agreed in scoping even when the sample frame you send does not happen to contain one.
The label files come first
The scope names the output format and the acceptance criteria for the label files. Everything else in the record is qualified by that scope.
extent 609 714
- source image
1586 × 992whole frame - shown region
318, 364560 × 350 - category
1 safety_housingone instance - bbox
501, 470, 195, 139safety housing - segmentation
17 pointsvisible extent - keypoint 1
501, 482top left - keypoint 2
694, 479top right - keypoint 3
615, 609front bottom
Every coordinate here is absolute in the 1586 × 992 frame, never in the view above. That view begins at 318, 364, so keypoint 1 at 501, 482 sits 183 px right and 118 px down from its top-left corner. The values read off the frame are the values in the file, and that correspondence is the thing being bought. The cable gland crossing the left face sits outside the polygon, so the record carries the visible extent and not an assumed rectangle.
{
"images": [
{ "id": 1, "width": 1586, "height": 992 }
],
"annotations": [
{
"id": 1,
"image_id": 1,
"category_id": 1,
"bbox": [501, 470, 195, 139],
"segmentation": [[
501, 482, 511, 477, 571, 472,
629, 470, 689, 473, 694, 479,
696, 556, 686, 564, 685, 593,
615, 609, 612, 578, 559, 577,
513, 571, 542, 564, 547, 545,
536, 533, 501, 536
]],
"keypoints": [
501, 482, 2,
694, 479, 2,
615, 609, 2
],
"num_keypoints": 3,
"iscrowd": 0
}
],
"categories": [
{
"id": 1,
"name": "safety_housing",
"keypoints": [
"top_left", "top_right", "front_bottom"
]
}
]
} Pascal VOC XML Custom output agreed in scoping If your review requires them, the same scope can add the relevant specification, reviewer record, acceptance record, manifest, version history or change record.
From your brief to accepted files
Tell us the source data status, model task, required annotation type, approximate volume, difficult cases, file format, application domain, target timing and acceptance requirement. If part of the specification is unclear, say so.
- Label file
- COCO JSON
- Coordinates
- Absolute in 1586 × 992
- Carried
- Bbox · 17-point polygon · 3 keypoints
- Per item
- The state it left review in
Image files, private storage links, credentials or confidential datasets in the initial form.
Send the brief for one workload. We reply with the questions your specification leaves open, and what a first agreed sample would have to settle before production.
YPAI uses the submitted information to review and respond to the image-annotation brief. Do not include image files, credentials or private storage links. Read the privacy notice.
Use general commercial contactOpening photograph: Turning vehicles yield to pedestrians by Erica Fischer, licensed under CC BY 2.0.
FAQ
Frequently asked questions
What image annotation tasks does YPAI deliver?
YPAI delivers 2D bounding boxes, oriented (rotated) boxes, polygon and instance segmentation, semantic segmentation, keypoint and skeleton annotation, image classification with hierarchical taxonomies, OCR ground truth, and document layout markup. Specialised verticals (medical imaging on DICOM, satellite and aerial, robotics bin-picking) ship with reviewer pools recruited for the modality.
Which image formats and storage targets does YPAI support?
Deliveries are shipped in COCO, YOLO, PASCAL VOC, custom JSON, GeoJSON for geospatial, and DICOM-segmented overlays for medical imaging. Storage targets include AWS S3, Azure Blob, GCP, customer-hosted S3-compatible (MinIO, Ceph) and direct-to-VPC drops. Custom format translation is part of the delivery pipeline, not a post-hoc consulting line.
Can YPAI annotate medical imaging (DICOM, NIfTI)?
Yes. Medical imaging is handled by clinician reviewers contracted for the specific modality (radiology, pathology, dermatology). Data is processed inside EU residency under GDPR Article 9 special-category controls. The controls in place for a given engagement are documented in the statement of work.
How does YPAI handle large-scale annotation backlogs?
Capacity scales with reviewer recruitment, not with crowd-marketplace dispatch. For multi-million-image backlogs the engagement runs in staggered waves with continuous IAA monitoring, so quality drift is caught at the batch boundary rather than at delivery. Active-learning loops are common at this scale to keep human attention on the lowest-confidence examples.
What inter-annotator agreement does YPAI report?
Each batch ships with task-appropriate IAA: Cohen kappa on classification, IoU and mAP on bounding boxes, Dice on segmentation, OKS on keypoints. Targets are set in the SOW per task and per class; underperforming classes trigger re-calibration before bulk progress continues. The IAA report is part of the deliverable, not an audit-only artefact.
Does YPAI provide active-learning or model-in-the-loop image annotation?
Yes. Where the customer has a working baseline model that can be queried EU-locally, YPAI runs human review over model pre-predictions and prioritises low-confidence or high-impact slices. The loop feeds into retraining batches. Hosting patterns (synchronous API, asynchronous batch queue, customer-on-prem) are confirmed during scoping.
Can YPAI annotate inside our perimeter for sensitive imagery?
Yes. For regulated healthcare, public-sector, and critical-infrastructure engagements where third-party processors are not acceptable, the annotation tooling and reviewer access can run inside the customer perimeter (EU regional cloud, on-prem rack, customer VPN). Specific feasibility is confirmed in the scoping document and the controls are written into the DPA.