Solution - Computer Vision

Image Annotation

SAM2-powered pre-annotation reduces manual work by 40–60%. Bounding boxes, polygon segmentation, semantic and instance segmentation, keypoint detection. Human experts validate every AI-suggested labelquality verified, not just completed.

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40–60%
Annotation time reduction via SAM2 pre-annotation + human validation
≥88%
Gold standard accuracy on every batch withheld and reannotated if below
6+
Annotation task types: bbox, polygon, semantic, instance, keypoint, DICOM
Visual AI
Multi-class object detection & semantic segmentation with per-label quality metrics
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Bounding BoxesPolygon SegmentationSemantic SegmentationInstance SegmentationKeypoint DetectionDICOM LabelingSAM2 Pre-annotation3D Point CloudBounding BoxesPolygon SegmentationSemantic SegmentationInstance SegmentationKeypoint Detection
Image Object Detection Annotation
● Crop Field · 0.98
● Bare Soils · 0.96
● GRASSLAND · 0.89
● TREE COVER · 0.94
▼ CLASS DETECTION SUMMARY
CROP FIELD ×2
BARE SOIL ×2
GRASSLAND ×1
TREE COVER ×1
FARM STRUCTURE ×1
7 OBJECTS · mAP 0.94 · QA PASS ✓
What It Is

SAM2-powered Computer vision training data at speed, without sacrificing accuracy

Image annotation is the process of labeling images with structured information object locations, boundaries, categories, or attributes so that computer vision models can learn to perceive and understand visual content. Every object detection, segmentation, and recognition system depends on millions of carefully annotated images.

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Live Annotation Interface

Object Detection Bounding Box Annotation Tool

Expert annotators draw precise bounding boxes, polygons, and semantic masks across millions of images building training datasets for computer vision models.

ConcaveLabel Studio - Image Annotation · Dataset: Urban Traffic CV · Frame #14,882
Traffic scene annotation
PEDESTRIAN 0.96
PEDESTRIAN 0.96
PEDESTRIAN 0.92
SUV CAR 0.95
SUV CAR 0.92
SUV CAR 0.98
CAR 0.97
CAR 0.96
CAR 0.94
CAR 0.94
CAR 0.99
CAR 0.93
CAR 0.93
CAR 0.97
CAR 0.95
BICYCLE 0.95
BICYCLE 0.89
BICYCLE 0.88
BUS 0.97
TRAFFIC LIGHT 0.91
TRAFFIC LIGHT 0.99
TRAFFIC LIGHT 0.92
CLASS LEGEND
PEDESTRIAN3
CAR9
SUV CAR3
BICYCLE3
BUS1
TRAFFIC LIGHT3
FRAME STATUS
22 objects · QA PASS
Frames Annotated:1.2M mAP:0.94 Classes:42 Annotators:28 Specialists
How It Works

Three things the pipeline does on every image annotation project

Tool-augmented polygon precision
SAM2-assisted segmentation pre-labeling reduces annotation time 40–60% while giving annotators a precise starting point for refinement. Humans correct and verify, they do not rubber-stamp AI output.
Automated quality validation
Polygon area, overlap, boundary gap, and label distribution checks run on every batch before QA review. Systematic errors are caught before they reach your training set.
Multi-annotator consensus on ambiguous regions
Ambiguous boundaries, occluded objects, and edge cases escalated to 3-annotator consensus with full adjudication logging. Your data card shows exactly where disagreements occurred and how they were resolved.
Pipeline Capabilities

What the infrastructure delivers

Consensus-Based Labeling
Every annotation is adjudicated by multiple reviewers with IAA measured per task and class, surfacing ambiguous regions before they corrupt training data quality.
Ontology-Managed Taxonomies
Label hierarchies are defined before annotation begins and locked throughout the project—no mid-project reclassification that invalidates prior work or inflates rework costs.
Active Learning Integration
Uncertain samples route to higher-expertise review tiers automatically, concentrating effort where model uncertainty is highest and reducing overall labeling cost per sample.
Annotation Types

Six annotation formats, all with SAM2 pre-annotation + human QA

Bounding Boxes
Axis-aligned and rotated bounding box annotation for object detection. Supports single-class and multi-class labeling with attribute tagging (occluded, truncated, crowd). Compatible with YOLO, COCO, and Pascal VOC formats.
YOLOCOCOPascal VOCRotated bbox
Polygon Segmentation
Pixel-precise polygon masks for complex object boundaries. SAM2 generates mask candidates; annotators refine boundary accuracy. Ideal for irregular shapes: vehicles, animals, organic objects. More accurate than bbox for training segmentation models.
SAM2 pre-maskBoundary precisionCOCO polygon
Semantic Segmentation
Pixel-level class labeling covering every pixel in the image. Every pixel belongs to a category sky, road, building, pedestrian, vegetation. Essential for scene understanding, autonomous driving, and satellite imagery analysis.
Every pixel labeledCustom class ontologyADE20K compatible
Instance Segmentation
Semantic segmentation that also distinguishes between different instances of the same class person #1 vs person #2, car #1 vs car #2. Each instance gets a unique ID. Essential for crowd analysis, multi-object tracking setup, and surgical scene understanding.
Unique instance IDsCOCO formatCrowd handling
Keypoint Detection
Labeling anatomical or structural keypoints human pose estimation (17 COCO keypoints), face landmarks (68 points), hand skeleton, vehicle structural points, animal anatomy. Supports custom keypoint schemas and visibility flagging for occluded points.
Human poseFace landmarksCustom schemasVisibility flags
What You Get

Annotated image data backed by verifiable quality proof

Every image annotation project delivers three core outputs alongside the labeled dataset.

Annotated Dataset
Labeled images in your format: COCO JSON, Pascal VOC XML, YOLO TXT, CVAT XML, or JSONL. Includes bounding boxes, segmentation masks, keypoints, or classification labels, whichever task types your model requires.
QA Report with Per-Class Kappa
Inter-annotator agreement by object class, polygon boundary accuracy scores, label distribution statistics, and a list of all adjudicated edge cases. Every metric is verifiable against the raw annotation logs.
Data Card & Annotation Guide
ML data card documenting class definitions, annotation tooling, edge case handling rules, known dataset limitations, and quality thresholds applied. Full annotation guidelines included for future auditing.
Pricing

Per-image pricing,
complexity-based tiers

Priced per image based on annotation type, object density, and domain. SAM2 pre-annotation is included in all tiers pricing reflects human QA time required.

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Bounding boxes (simple, <10 objects/image)$0.06–0.15 / image
Polygon / segmentation (standard)$0.18–0.42 / image
Dense segmentation (>20 objects/image)$0.42–1 / image
Keypoint annotation$0.10–0.30 / image
Satellite / aerial imagery$0.30–1 / image

Get 100 images annotated free

Send us a sample of 100 images from your dataset. We will annotate them using our SAM2+human pipeline and return the labeled dataset with IoU metrics no cost, no commitment.

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