What Do We Annotate

Prepare training datasets for autonomous driving and vehicle perception. Label vehicles, pedestrians, traffic signs, and other road-scene objects with suitable annotation techniques.

Prepare annotated medical imaging datasets for healthcare AI research and model development. Support relevant imaging workflows via well-defined labels and clear review requirements.

Annotate crop, plant, and field imagery for agricultural computer vision applications. Prepare structured datasets to better support precision agriculture and AI-based crop analysis.

Annotate human figures, facial landmarks, or body keypoints for supported computer vision applications. Define labels aligned to the project’s requirements and annotation guidelines.

Prepare annotated retail images and video for product recognition, shelf monitoring, and other visual AI applications, using appropriate labeling techniques for each retail use case.

Annotate sports images and videos featuring players, balls, and other relevant objects to prepare datasets for sports analytics and AI-powered game analysis across supported formats.

Label images of goods, packages, and identification codes to prepare high-quality training data for logistics AI, inventory monitoring, and supported object-recognition applications.

Annotate aerial imagery from drones and other supported 
sources using bounding boxes, polygons, and segmentation to prepare datasets for visual applications and AI model training.

Annotate 2D images and 3D point-cloud data for supported sensor-fusion workflows. Prepare structured labels for applications using camera and LiDAR data across multi-sensor datasets.

Prepare annotated industrial images for visual inspection, defect detection, and quality-control AI applications. Align labeling with the requirements of each manufacturing use case.