Our image labeling and classification services tag and categorize objects within images to help AI models identify and sort different elements based on predefined labels. From detecting specific objects like cars or animals to categorizing images into broader classes, our services provide AI companies with the labeled data needed to train robust models for applications in e-commerce, security, and more.

Bounding box annotation is the cornerstone of object detection tasks. We draw rectangular boxes around objects in images, providing a clear, structured way to label items. This service is widely used in industries like autonomous driving, surveillance, and retail to enable AI models to detect and track objects in real-world scenarios, such as people, vehicles, or products.

Our polygon annotation service enables precise outlining of objects with irregular shapes in images. Unlike bounding boxes, polygons allow for detailed, accurate labeling of objects like animals, buildings, or machinery, which may not fit neatly into rectangular boxes. This method is particularly useful for industries that require fine-grained object recognition, such as agriculture, robotics, and healthcare.

Our 3D cuboid annotation service adds depth information to image annotations by creating 3D bounding boxes around objects. This allows for more accurate spatial understanding, crucial for industries like autonomous driving, robotics, and augmented reality, where knowing the precise location and orientation of objects in 3D space is essential for AI models.

Polyline annotation is used to label linear objects or paths in images, such as roads, railways, or pipelines. This service is commonly employed in applications like autonomous driving, navigation, and infrastructure monitoring, where continuous paths or routes need to be tracked and analyzed by AI models for navigation and planning.

Semantic segmentation involves labeling every pixel in an image to categorize areas by object type, like roads, trees, or sky. This detailed annotation is essential for applications like autonomous driving, satellite imagery analysis, and medical image segmentation, where understanding the entire environment or anatomy is crucial for AI-driven decision-making.

Instance segmentation is a more advanced form of semantic segmentation, where each object instance is individually labeled. Our service enables AI systems to distinguish between different instances of the same object category, which is vital for complex environments. This is particularly useful for applications in computer vision, like facial recognition, retail analytics, and security monitoring.

Panoptic annotation combines semantic and instance segmentation, labeling both “things” (e.g., people, vehicles) and “stuff” (e.g., roads, sky) within images. This holistic approach is ideal for industries requiring complete scene understanding, such as robotics, autonomous vehicles, and environmental monitoring, where both object identification and contextual background are equally important.

Skeletal annotation focuses on labeling key points on human bodies or other entities to create a skeletal structure. This is especially important for human pose estimation, action recognition, and biomechanics. By identifying and tracking body movements in images, AI systems can better analyze human activity, which is valuable in healthcare, fitness, and sports applications.

Keypoint annotation involves labeling specific points on objects or human bodies within images. For example, keypoints on a face may include the eyes, nose, and mouth, or keypoints on a person’s body may include joints like elbows and knees. This type of annotation is essential for applications such as facial recognition, human pose estimation, and gesture detection, enabling AI models to better understand and respond to human behavior.