Transform raw visual data into high-quality training datasets with Learning Spiral AI’s image annotation services. Our trained annotators label objects, regions, attributes, and landmarks with accuracy and consistency, helping computer vision and machine learning models improve object detection, image recognition, scene understanding, and automated decision-making across diverse industry applications.
Image Annotation
Bounding Box
Bounding Box Annotation identifies and localises objects by drawing precise rectangular boxes around them. Ideal for object detection and recognition models, it helps AI systems detect vehicles, pedestrians, products, equipment, and other visual elements. Learning Spiral AI delivers consistently labelled datasets aligned with your class definitions and project guidelines.
Polygon Annotation
Polygon Annotation captures the exact boundaries of irregularly shaped objects by placing connected points around their edges. This detailed image labeling technique supports precise object recognition in autonomous driving, agriculture, healthcare, retail, and geospatial AI. Our annotators carefully map complex shapes to create accurate, model-ready computer vision datasets.
Segmentation
Segmentation provides pixel-level image annotation for detailed scene and object understanding. Through semantic and instance segmentation, every relevant region can be accurately separated and classified. This technique is ideal for medical imaging, autonomous vehicles, robotics, agriculture, and defect detection, where precise boundaries significantly improve computer vision model performance.
Classification
Classification assigns predefined categories or attributes to complete images, objects, or visual scenes. It enables machine learning models to recognise patterns, organise visual content, and make accurate predictions. Learning Spiral AI provides scalable image classification services for product categorisation, content moderation, medical imaging, agriculture, security, and other AI applications.
Keypoint labeling
Keypoint Labeling marks specific landmarks or points on faces, bodies, objects, and structures. It supports pose estimation, facial recognition, gesture detection, emotion analysis, sports analytics, and movement tracking. Our image annotation specialists place keypoints consistently to help AI models understand position, orientation, posture, and spatial relationships with greater precision.