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Annotation & Labeling Expertise

Data Annotation for Computer Vision Models

Bounding box, polygon, semantic segmentation, landmark and automated object detection for Computer Vision Models.

ClassificationBounding BoxPolygonLandmarkPolylineSegmentation3D CuboidsPoint CloudAuto-detection
classification

Images and Video Classification

Images and Video Classification
Image Annotation
Computer Vision
Classification

Image classification — whether single-label or multi-label — assigns each input to one or more predefined categories. The classification schema is developed in close collaboration with domain experts to ensure that label taxonomy aligns with real-world use cases, enhancing both the relevance and market readiness of your AI model.

boundingbox

Bounding Box

Bounding Box
Image Annotation
Computer Vision
Bounding Box

Bounding boxes are a fundamental method in object detection, used to enclose target instances by specifying their X and Y coordinate limits within a rectangular frame. This approach offers high annotation speed and computational efficiency, but its coarse granularity may include non-relevant background pixels — potentially impacting model precision, especially in dense or complex visual environments.

polygon

Polygon

Polygon
Image Annotation
Computer Vision
Polygon

Polygons are a widely used segmentation method that enables precise delineation of objects while minimizing background noise — a key factor in enhancing computer vision model performance. Although less granular than pixel-level semantic segmentation, polygons offer an optimal balance between annotation accuracy and efficiency, making them ideal for generating high-quality, large-scale training datasets.

points

Landmark & Keypoint

Landmark & Keypoint
Image Annotation
Computer Vision
Landmark

Widely applied in face recognition, pose estimation, and eye-tracking systems, our landmark annotation techniques follow precise anatomical guidelines to ensure accurate point placement. This level of precision strengthens model training by providing consistent, high-fidelity reference data for spatial feature detection.

polylines

Lines and Polylines

Lines and Polylines
Image Annotation
Computer Vision
Polyline

Lines and polylines are effective tools for annotating linear structures such as road markings, lanes, and wiring paths. They offer a practical balance between annotation speed and precision, making them ideal for segmenting rectilinear and curvilinear features — though their application is limited to these specific geometries.

segmantation

Semantic Segmentation

Semantic Segmentation
Image Annotation
Computer Vision
Semantic Segmentation

High-performance computer vision models rely on pixel-level accuracy — and semantic segmentation delivers exactly that. By assigning a class to each individual pixel, this method enables fine-grained training, provided that clear annotation guidelines are established to handle challenging areas such as blur, shadows, and occlusions.

cuboid

3D Cuboids

3D Cuboids
Image Annotation
Computer Vision
3D Cuboids

3D cuboids enable the extraction of spatial information from 2D images by approximating object volume and orientation. This annotation technique is particularly valuable for depth estimation and spatial reasoning in applications such as autonomous navigation and augmented reality.

cloud

Point Cloud & LiDAR

Point Cloud & LiDAR
Image Annotation
Computer Vision
Point Cloud

Whether deployed on autonomous vehicles, drones, or satellites, LiDAR sensors unlock advanced 3D scene understanding. Segmenting point cloud data requires specialized software and benefits greatly from techniques like sensor fusion and clustering, enabling more efficient object detection and spatial analysis in complex environments.

brainai

Automatic Object Detection

Automatic Object Detection
Image Annotation
Computer Vision
Automated Detection

We integrate cutting-edge object detection models like YOLOv9 to pre-label visual data with high speed and precision. This model-driven automation enables rapid generation of bounding boxes or masks, significantly reducing manual annotation time. Combined with our human-in-the-loop validation process, we ensure each dataset meets the highest accuracy standards.