Annotation
Instance Segmentation Services

Pixel-perfect instance segmentation for individual object detection

Expert instance-level segmentation that separates and masks every individual object. Train advanced computer vision models to detect overlapping objects, count instances, and understand complex scenes with pixel-precise boundaries.

  • 99.9% accuracy
  • Overlapping objects
  • Pixel-level masks
TRUSTED BY
Google AI
Amazon
Meta
Microsoft
Apple
NVIDIA

Annotation Capabilities

Comprehensive instance segmentation solutions

Professional pixel-level object masking for advanced computer vision models that need to identify and separate individual instances

Individual Instance Masks

Pixel-perfect masks for every individual object instance, even when overlapping. Each object gets a unique ID and boundary mask.

  • Unique instance IDs
  • Polygon & bitmap masks
  • Pixel-level precision
Overlapping Object Handling

Expert annotation of overlapping and occluded objects with proper depth ordering and instance separation.

  • Occlusion reasoning
  • Depth ordering
  • Boundary refinement
Multi-Class Instance Detection

Segment and label instances across multiple object classes simultaneously with class-specific attributes.

  • Multi-class support
  • Class attributes
  • Hierarchical labels
Dense Scene Annotation

Handle crowded scenes with hundreds of instances including small objects, partial views, and complex arrangements.

  • Crowd segmentation
  • Small object detection
  • Dense labeling
Quality Control & Validation

Multi-layer quality assurance ensuring mask completeness, boundary accuracy, and instance consistency.

  • Mask validation
  • Boundary checks
  • Consistency review
Custom Instance Taxonomies

Define custom object classes, instance attributes, and specialized labeling schemas for domain-specific applications.

  • Custom taxonomies
  • Attribute tagging
  • Domain adaptation

Our Process

How instance segmentation works

A proven workflow for precise instance-level object segmentation and masking

STEP 1

Upload & Configure

Upload images and define object classes, instance requirements, and annotation guidelines

STEP 2

Instance Segmentation

Annotators create pixel-perfect masks for each individual object instance

STEP 3

Instance Labeling

Assign unique IDs, class labels, and attributes to each segmented instance

STEP 4

QA & Export

Quality verification before exporting in COCO, Mask R-CNN, or custom formats

Precision at the instance level

Industry-leading accuracy for instance segmentation and object detection models

100M+

Instances segmented

99.9%

Mask accuracy

1px

Boundary precision

200+

Object classes supported

Use Cases

Instance segmentation across industries

Powering advanced computer vision applications that require individual object detection and counting

Autonomous Vehicles

Individual vehicle & pedestrian tracking

Medical Imaging

Cell counting & organ segmentation

Retail Analytics

Product counting & shelf monitoring

Warehouse Automation

Package detection & sorting

Microscopy

Cell instance detection & analysis

Construction

Object counting & progress tracking

Crowd Analysis

Person counting & density mapping

Surveillance

Individual tracking & monitoring

Ready to segment individual object instances?

Get pixel-perfect instance segmentation masks for overlapping objects, dense scenes, and complex scenarios. Trusted by leading AI companies.

Instance Segmentation FAQs

Common questions about our instance segmentation annotation services

Instance segmentation treats each object as a separate entity with its own mask and ID, even if they are the same class. Semantic segmentation only classifies pixels by category without distinguishing between individual instances. For example, instance segmentation can identify “car #1” and “car #2” separately, while semantic segmentation labels all car pixels identically.

Our expert annotators use depth reasoning and occlusion analysis to properly segment overlapping objects. Each instance receives a complete mask including occluded portions when inferable, plus depth ordering information to indicate which objects are in front or behind.

We export instance masks in multiple formats: COCO JSON with RLE encoding, polygon coordinates, binary masks (PNG), Mask R-CNN format, and custom JSON schemas. Each instance includes unique ID, class label, bounding box, and segmentation mask.

Yes, we specialize in dense scene annotation with hundreds of instances. Our annotators are trained to segment small objects down to a few pixels, handle partial views, and maintain consistency across crowded or complex arrangements.

We achieve pixel-level accuracy with average boundary precision of 1 pixel. Our QA process includes automated mask validation, boundary smoothness checks, and manual review to ensure 99.9% accuracy across all instances.

Absolutely. For video data, we provide temporal instance tracking with consistent IDs across frames, handling instance appearances, disappearances, and occlusions over time. Perfect for video object segmentation (VOS) and tracking applications.