Oodles AI delivers high-precision image annotation services that power reliable computer vision models. Our annotation teams combine expert human labelers with advanced tooling, quality workflows, and industry-standard formats to convert raw images into training-ready datasets for autonomous systems, healthcare, retail, agriculture, and security applications.
Image annotation is the process of labeling visual data to generate structured training datasets for computer vision models. Oodles AI applies industry-standard annotation techniques—ranging from bounding boxes and polygons to pixel-level segmentation and keypoints—using secure annotation platforms, multi-stage quality checks, and export formats compatible with modern deep learning frameworks.
High-accuracy 2D and 3D bounding boxes annotated using standardized guidelines to support object detection and tracking models.
Pixel-level segmentation masks created with strict QA protocols to enable fine-grained scene understanding and model accuracy.
Precise polygon and polyline annotations for irregular objects, road lanes, and complex contours requiring exact boundaries.
Landmark and keypoint annotations for faces, human poses, and body joints, supporting pose estimation and AR/VR use cases.
Instance-level masks that uniquely identify each object, enabling advanced object-level analysis and training workflows.
3D cuboid and LiDAR point cloud annotations aligned with autonomous driving and robotics dataset standards.
Our image annotation solutions empower diverse industries by enhancing AI training, improving automation, and accelerating decision-making with high-precision data.
Oodles AI annotates lanes, traffic signs, vehicles, and pedestrians using 2D/3D annotation workflows that meet autonomous driving dataset standards.
Support medical AI models with expertly annotated scans, X-rays, and imaging datasets using secure, compliance-aware annotation workflows.
Enable product recognition, visual search, and shelf analytics with accurately labeled retail image datasets.
Train surveillance and monitoring models with labeled datasets for people detection, crowd analysis, and anomaly recognition.
Label document images, forms, IDs, and handwriting regions to support OCR and document understanding pipelines.