Home Image & video labeling
Every pixel accounted for.
Boxes, polygons, masks, keypoints, attributes and multi-frame tracks, drawn to your spec and checked against it. The difference between a cheap vendor and a good one is not the first pass; it is what happens to the 8% of frames where the spec runs out.
COCO · YOLO · VOC · your schema · Data handling discussed at scoping
The full computer-vision toolkit.
Tell us the label type and the acceptance criteria and we will quote against them. Where you are unsure which annotation type your model actually needs, we will say so during scoping rather than sell you the most expensive one.
Bounding boxes and 2D detection
Tight boxes with agreed rules for occlusion, truncation at the frame edge, crowds and minimum object size. Those four conventions cause most of the disagreement in detection datasets, so we settle them first.
Polygons and instance segmentation
Per-instance outlines at the vertex density your model needs, with holes, disconnected parts and overlapping instances handled consistently instead of being approximated away.
Semantic and panoptic segmentation
Dense per-pixel class masks with an explicit policy for boundaries, shadows, reflections and unlabelled regions, delivered as PNG indices or run-length encoding.
Keypoints, pose and landmarks
Human pose, hand and facial landmarks, and product or machinery keypoints, each with visibility flags so an occluded joint is recorded as occluded rather than quietly invented.
Classification and attributes
Single-label, multi-label and hierarchical taxonomies, plus per-object attributes such as colour, material, state, damage and quality grade. Confusable pairs get a written decision rule, not a coin flip.
OCR and text in the wild
Text detection and transcription on signage, menus, notices, labels and packaging, including Traditional Chinese set vertically or in stylised script and mixed freely with English. A Hong Kong team reads these every day.
Video tracking and event tagging
Multi-object tracking with stable identities through occlusion and re-entry, interpolated versus observed frames marked explicitly, plus temporal event and action boundaries where you need them.
Anyone can draw a box. The spec is the product.
Image datasets can contain difficult cases: an object cut in half by the frame, overlapping instances, a partly legible sign or a rare category. Flagging ambiguous cases for review helps avoid unsupported labels.
A calibration sample can help identify gaps in the guidelines. During scoping, agree how to record edge cases and which checks are suitable, such as reference-sample accuracy, IoU or inter-annotator agreement. Reporting and review responsibilities depend on the agreed task.
Review options to agree during scoping
- Agree suitable reference examples and checks before setting delivery targets.
- Suitable IoU thresholds for tasks that use reference geometry.
- Additional review passes and a process for resolving disputed items.
- Per-class agreement reporting where appropriate.
- An edge-case ledger to support guideline updates.
- Named review responsibilities for the agreed workflow.
Quality you can verify, not quality you have to trust.
Train. Annotate. Review. Deliver.
Train
Annotators work through your guidelines and a calibration batch, and we return the questions your spec did not answer.
Annotate
Careful labelling at a pace that protects accuracy, in your tool or ours, with hard frames flagged rather than forced.
Review
Human review can be included in a scoped workflow, with review criteria agreed in advance. The checks and reporting are set to fit the data and task.
Deliver
Agree the delivery format, timeline and any accompanying QA report or edge-case ledger before work begins.
Formats we deliver
COCO JSON, YOLO text, Pascal VOC XML, PNG or RLE masks, CVAT XML, and CSV or JSONL for classification and attributes. If you have an internal schema we write to it directly rather than making you convert.
How your data is handled
Before a task is scoped, we discuss data minimisation, access permissions, processing location, retention and any third-party model terms. For sensitive imagery, we can discuss whether redaction is suitable before choosing a workflow. Handling depends on the tools and workflow selected.
Skilled annotators, not anonymous throughput.
Label Less is a Hong Kong social enterprise whose core work is data annotation and human review. This page outlines annotation task types; staffing and review stages for any future project are discussed during scoping.
Read the case study →Image labeling, answered.
Cantonese & Traditional Chinese annotation
Transcription, entities, code-switching, sentiment and moderation with real Hong Kong context.
ROBORobotics & teleoperation data
LiDAR, trajectories, action segments and teleoperation demonstrations for embodied AI.
CASEPast data annotation training pilot
A historical account of a completed data annotation training pilot.
Have data that needs labeling?
Tell us the data type, format and scope you have in mind. We can discuss feasibility and next steps.
Request a quote →Want to back the mission?
Contact us to discuss Label Less’s work and social enterprise mission.
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