
Bounding Boxes, Polygons or Segmentation: Which Label to Choose
Choose the simplest label that gives the model what it must output. Boxes, polygons, masks, keypoints and cuboids compared, with five questions that decide.
We use our domain experts to work mutually to meet the client’s need within stipulated time.

Choose the simplest label that gives the model what it must output. Boxes, polygons, masks, keypoints and cuboids compared, with five questions that decide.

Six things set the quote for a labeling project, and vendors charge in three ways. What each means for you, and what to send to get a firm answer.

Three numbers tell you whether labeled data is good: accuracy against a gold set, agreement between annotators, and overlap for shapes. How to take each one.

An hour of preparation saves days of rework. Seven steps to take before your data goes to a labeling team, and a hand-over checklist.

A small batch on your real data shows whether the guideline works, what accuracy can be reached and how fast. Six steps, and what a good result looks like.

A guideline is the contract for what "correct" means. The eight things it must settle, a one-page template, and how to test it on two annotators before it costs you a dataset.

Generative models learn from people in three ways: examples of the ideal answer, judgements of the model's own answers, and attempts to make it fail. What each task is, and what separates good data from noise.

When to label data with your own team and when to hand it to a vendor. Seven questions that decide it, and the mixed model most teams end up with.

Twenty-five questions to take into the first call with a labeling company, in five groups, and how to tell a real answer from a sales answer.

Tight, consistent boxes around every object your model needs to find. 2D boxes, rotated boxes and 3D cuboids for object detection, drawn by trained annotators and checked in more than one pass.

Generative AI learns from examples and is judged by people. We supply both, from training data and prompt writing to response rating, fact checking and safety review, in more than 15 languages.

Training data for healthcare AI. X-ray, CT, MRI, ultrasound and pathology images labeled to your clinical guidelines, with de-identified data and strict confidentiality.

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Accuracy below 96.5%? We rework the project.