AI and ML Network Services
Semantic Segmentation Services
Professional semantic and instance segmentation annotation services for computer vision. Pixel-level mask labeling for autonomous driving, medical imaging, and AI training with 99%+ accuracy.
AI and ML Network provides production-grade semantic segmentation and instance segmentation annotation services for computer vision teams building scene understanding, autonomous driving, and medical imaging AI.
Semantic Segmentation
Full pixel-level classification where every pixel receives a class label. We deliver dense segmentation masks for:
- Autonomous driving — road, sidewalk, vehicle, pedestrian, building, sky, vegetation
- Medical imaging — tissue boundaries, organ segmentation, lesion delineation
- Satellite imagery — land use classification, building footprints, vegetation mapping
- Manufacturing — surface defect regions, material boundaries
Instance Segmentation
Individual object masks that distinguish between separate instances of the same class. Essential for:
- Object counting — counting individual items in crowded scenes
- Robotics — identifying graspable objects individually
- Autonomous vehicles — tracking individual vehicles and pedestrians
- Retail — individual product identification on shelves
Polygon and Mask Annotation
Precise polygon tracing that follows object contours tightly. We support:
- COCO polygon format (coordinate arrays)
- YOLO segmentation format (normalized polygon coordinates)
- Binary mask format
- RLE (Run-Length Encoding) format
- Custom polygon schemas
Our Segmentation QA Process
Segmentation annotation requires higher precision than bounding boxes. Our QA process includes:
- Polygon tightness validation — masks must hug object boundaries without gaps or overflow
- Boundary precision checks — critical edges verified at pixel level
- Class consistency — identical objects receive identical class labels
- Inter-annotator agreement — IoU comparison between annotators on calibration sets
- Format verification — automated validation before delivery
If you need segmentation annotation, go to the Start Project page and send your requirements.
Frequently Asked Questions
What is the difference between semantic and instance segmentation? +
Semantic segmentation assigns a class label to every pixel in the image but does not distinguish between separate instances of the same class. Instance segmentation identifies and segments individual object instances separately — so two cars in the same image get two distinct masks rather than being merged into one 'car' region.
What tools do you use for segmentation annotation? +
We use CVAT, Roboflow, Label Studio, and Supervisely for segmentation work. We also leverage AI-assisted tools like SAM (Segment Anything Model) for initial mask generation, followed by human expert correction to ensure pixel-level accuracy.
How accurate are your segmentation masks? +
We guarantee 99%+ accuracy on segmentation masks. Our QA process includes polygon tightness checks, boundary precision validation, inter-annotator consistency reviews, and automated IoU verification against reference annotations.
Ready to Start?
Get a free sample batch to test our quality before committing.