Image
Annotation Tool

Label faster with a quality-first annotation platform

Ango Hub is a production ready image labeling platform with built in workflows, quality checks, and automation assist. iMerit delivers your annotation program inside the tool so you can scale output, reduce rework, and keep quality consistent from pilot to production.

Image Annotation Tool

WHAT ANGO HUB OFFERS

Everything you need to run image labeling at production scale, with control over quality, throughput, and performance.

WORKFLOW MANAGEMENT

Configure projects end to end with a customizable workflow editor that supports roles, stages, routing, and handoffs. Keep project instructions centralized so every contributor follows the same standard.

BUILT-IN QUALITY CHECKS

Quality is enforced inside the workflow with reviewer validation and correction steps, multi labeler consensus for difficult tasks, benchmark questions to measure performance, and custom label validation to catch errors early.

AUTOMATION ASSIST

Auto detect and Copilot speed up labeling with auto labeling, YOLOv11 bounding boxes for 80 plus object types, image summaries, and schema aware classification support.

INTEGRATIONS

Connect Ango Hub to your pipelines using SDK and API support, and automate downstream handoffs through webhooks for instant data egress.

ANALYTICS

Monitor progress with detailed project analytics including throughput, cycle time, and quality trends, so you can identify bottlenecks, manage capacity, and improve consistency over time.

SECURITY AND GOVERNANCE

Control access and accountability across teams with role based permissions and governed workflows. Keep quality decisions traceable with in workflow issue resolution and structured review paths.
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USE CASES

Across autonomy, geospatial, retail, robotics, and inspection, iMerit helps teams turn your image data into high-quality ground truth for training and benchmarking image models.

AUTONOMOUS VEHICLES

Image annotation for lanes, vehicles, pedestrians, signs, and drivable areas for ADAS and autonomy

MEDICAL IMAGING

Segmentation and classification to support imaging AI training and validation.

GEO SPATIAL AND MAPPING

Label features in satellite and aerial imagery for mapping, planning, and monitoring.

FACIAL RECOGNITION

Keypoint and face attribute labeling to train and evaluate recognition and verification models.

ROBOTICS

Image annotation for robots to understand objects, depth, and spatial relationships

AGRICULTURE

Label crops, weeds, canopy, and field features to power yield and health insights.

INDUSTRIAL INSPECTION

Defect detection and classification for manufacturing quality and asset maintenance.

DOCUMENT AND id VISION

Annotate document regions, fields, and text areas to improve OCR and document AI.

RETAIL AND E-COMMERCE

Product detection, shelf intelligence, and catalog labeling to improve search and conversion.

Energy and utilities

Detect corrosion, leaks, cracks, and anomalies across pipelines, power lines, and water networks using image labeling for inspection models.

TYPES OF ANNOTATION

Bounding Box

Bounding Boxes

It is the most commonly used type of image annotation in computer vision. iMerit computer vision experts use rectangular box annotation to illustrate objects and train data, enabling algorithms with annotated images to identify and localize objects during the machine learning process. The simplicity of bounding boxes is exactly their strength, making this method of image annotation applicable for a wide range of uses.
Polygon annotation for aircraft detection on airport, AI in Government applications

Polygon Annotation

Expert annotators plot points on each vertex of the target object. Polygon annotation allows all of the object’s exact edges to be annotated, regardless of shape. This allows computer vision and other artificial intelligence models to recognize and respond to objects. This technique is especially useful in computer vision as annotators can use it to identify irregular shapes, allowing computers to identify and respond to them.
Semantic segmentation to classify each pixel of a car, use case of computer vision

Semantic Segmentation

Images are segmented into component parts, by the iMerit team, and then annotated. iMerit computer vision experts detect desired objects within images at the pixel level. With expert semantic segmentation, data can be organized in multiple formats for AI models across a variety of use cases.
LIDAR

LiDAR Annotation

iMerit teams label images and videos in 360-degree visibility, captured by multi-sensor cameras, in order to build accurate, high-quality, ground truth datasets for use in computer vision models such as autonomous vehicles.
Image annotation to classify images on basis of land use category, for geospatial applications

Image Classification

iMerit annotators classify images or objects within images based on custom multi-level taxonomies, including land use, crops, residential property features, among others. Expert image classification turns image data into image insights for AI and ML models.
3d cuboid annotation

3d cuboid annotation

Through the use of cuboids, iMerit annotators can generate training datasets to teach machine learning models to recognize the depth of objects. Expert data labeling creates best-in-class training datasets for computer vision models to detect object and obstacle dimensions. Through the use of anchor points typically placed at the edges of an item, these dots are then connected with a line that results in a 3D representation of the object.
keypoint annotation involve facial recognition

Keypoint annotation

iMerit teams outline objects and shape variations by connecting individual points across objects. This annotation type detects body features and could include facial expressions and emotions. Popular use cases for keypoint annotation involve facial recognition.
Polyline annotation

Polyline annotation

iMerit experts create training datasets using polyline annotation that teach a machine learning model to identify physical boundaries to operate within. Popular use cases include autonomous vehicles and teaching them road boundaries.
Rapid annotation

Rapid annotation

iMerit’s image annotation platform utilizes image interpolation to rapidly annotate suitable files including JPG, PNG, and even CSV. iMerit annotation experts create best-in-class video training datasets in rapid time for any AI or ML project. Give your data science team the expert service they need to take their project from idea to production.

SCALING

HUMAN IN THE LOOP

AI-Assisted features of iMerits’ Ango Hub will automate your AI data workflows to improve efficiency and model RLHF, allowing domain experts to focus on applying their knowledge to create high-quality data.
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Frequently Asked Questions

Ango Hub supports the full spectrum of image annotation types used in computer vision — bounding boxes, polygon annotation, semantic segmentation, keypoint annotation, polyline annotation, image classification, 3D cuboid annotation, and LiDAR annotation. Whether you're labeling lanes for autonomous vehicles, segmenting tumors for medical AI, or detecting defects on a factory floor, the platform is built to handle it at production scale.
Quality is enforced inside the workflow itself — not bolted on afterward. Ango Hub includes reviewer validation and correction steps, multi-labeler consensus for difficult tasks, benchmark questions to measure annotator performance, and custom label validation rules to catch errors early. iMerit also embeds its annotation teams directly inside the platform, giving you end-to-end oversight from pilot to production.

Yes. Ango Hub's Automation Assist features include auto-labeling, YOLOv11-powered bounding boxes covering 80+ object types, image summaries, and schema-aware classification support. These tools reduce manual effort significantly, especially on high-volume projects, while keeping humans in the loop for accuracy on edge cases.

iMerit serves teams across autonomous vehicles (ADAS, lane detection, object recognition), medical imaging (segmentation and classification), geospatial and satellite imagery, retail and e-commerce (product detection, shelf intelligence), agriculture (crop and weed labeling), industrial inspection (defect detection), facial recognition, and robotics. If your model learns from images, iMerit can annotate it.

Ango Hub connects to your pipelines via SDK and API support, and automates downstream data handoffs through webhooks for instant egress. You can plug it into your existing data infrastructure, run annotation programs at scale, and push labeled data directly to your training or evaluation environments without manual exports or bottlenecks.

GET STARTED

TODAY!

Get production-ready labels for computer vision. Ango Hub pairs workflow control and automation assist with iMerit delivery so you can scale image annotation with consistent quality.