We provide reliable, high-quality data annotation services to support AI model training and validation. From bounding boxes to semantic segmentation, we ensure precision at scale — under strict security protocols.
Bounding boxes annotation is a common method in computer vision for labeling objects within images. It involves drawing a rectangular box around an object to mark its position and size. Each box is typically associated with a class label.
A cuboid annotation is a 3D rectangular box used to define the position, boundaries, and orientation of an object within a 3D space, often used in object detection and computer vision tasks.
A keypoint annotation involves marking specific points or landmarks on an object, such as joints on a human body or corners of an object. It is commonly used in tasks like pose estimation, facial recognition, and object tracking.
Instance segmentation annotation involves marking each object instance in an image with a precise pixel-level mask, differentiating between objects of the same class and accurately defining their boundaries.
Polygon annotation involves outlining an object’s shape with connected points to form a polygon. It provides precise labeling for complex or irregular shapes, commonly used in object detection, segmentation, and instance segmentation tasks.
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