Professional multi-class annotation supporting hundreds of categories with hierarchical taxonomies. Perfect for complex scene understanding, fine-grained classification, and multi-attribute labeling.
Multi-Class Capabilities
Comprehensive multi-class annotation supporting complex taxonomies and multiple attributes per object
Organize labels in multi-level hierarchies with parent-child relationships for structured taxonomies and ontologies.
Assign multiple attributes per object including appearance, state, behavior, and contextual properties.
Distinguish between hundreds of similar categories with subtle differences for detailed object recognition.
Apply multiple non-hierarchical labels to single objects for comprehensive attribute coverage.
Define relationships between objects including spatial, temporal, and semantic connections.
Work with our team to design domain-specific classification schemes tailored to your unique requirements.
Our Process
A systematic approach to complex classification with hundreds of categories and attributes
STEP 1
Define hierarchical class structure and attribute relationships
STEP 2
Apply all relevant classes and attributes to each detected object
STEP 3
Verify label coherence and attribute combinations across dataset
STEP 4
Deliver structured labels with full taxonomy and relationship data
Supporting sophisticated multi-class annotation for advanced computer vision
Max classes supported
Multi-class objects labeled
Label consistency rate
Taxonomies deployed
Use Cases
Powering complex classification across industries requiring detailed object understanding
Complex road scene analysis
Product categorization
Multi-condition classification
Multi-attribute tagging
Crop and pest classification
Defect type classification
Property feature tagging
Threat classification
Get started with professional multi-class labeling. Support for hundreds of categories, hierarchical taxonomies, and custom attributes.
Common questions about our multi-class annotation services
We support up to 300+ distinct classes per project. Common datasets range from 20-100 classes, while specialized projects like fine-grained product classification may use 200+ categories. We can organize classes in hierarchical taxonomies with multiple levels and support both mutually exclusive and multi-label classification.
Hierarchical classification organizes labels in parent-child relationships (e.g., Vehicle > Car > Sedan > Toyota Camry). Use it when your domain has natural categorical hierarchies, you need different granularity levels, or want to enable both coarse and fine-grained predictions. It improves model performance and enables flexible inference.
Yes, we support multi-label annotation where objects can have multiple non-exclusive labels. For example, an image could be tagged as both “outdoor” and “daytime” and “people”, or a product could be “red” + “sale” + “electronics”. This is different from hierarchical labels and useful for attribute-based classification.
We use detailed annotation guidelines, category examples, decision trees for edge cases, regular annotator calibration sessions, automated consistency checks, and consensus review for ambiguous cases. Our QA process includes inter-annotator agreement measurement and identifies systematic labeling errors across the class distribution.
We deliver multi-class labels in COCO JSON (with category hierarchies), CSV with class IDs and attributes, custom JSON schemas, and can include confidence scores, class probabilities, and relationship graphs. We also provide taxonomy files defining the class structure and attribute mappings for easy integration.
Absolutely. Our team works collaboratively to design optimal taxonomies based on your data, use case, and model architecture. We analyze sample data, recommend class granularity, define hierarchies, identify edge cases, and iterate based on pilot annotation results to ensure the taxonomy meets your needs.
