Image Annotation Services

iMerit delivers stellar image annotation services that power AI, machine learning, and data operation strategies.


Polygon Annotation
What is image annotation_

What is image annotation?

Image annotation is the process of labeling an image, which strategically involves human-powered work and sometimes, computer-assisted help. It is an important step in creating computer vision models for tasks like image segmentation, image classification, and object detection. Image annotation can range from annotating every group of pixels within an image to one label for an entire image.

Successful image annotation projects involving computer vision are built on high-quality annotation. The type of annotation needed will depend on the use case the project is designed for.

What are the types of image annotation services?

iMerit provides various image annotation services that will cater to a client’s project needs, including bounding boxes, polygon annotations, keypoint annotation, LiDar, semantic segmentation, and image classification. iMerit’s team works with the client to calibrate the quality and throughput of the job and deliver the best cost-quality ratio as you iterate. We recommend running a sample batch to clarify instructions, edge cases, and approximate task times, before launching full batches.

High-quality image annotation generates ground truth datasets for optimal machine learning functionality. There are numerous types of deep learning applications for image annotation across industries including autonomous technology & transportation, medical AI, commerce, geospatial, finance, government, and more.

iMerit’s Image Annotation Services

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 in an airport, use case of Computer Vision
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 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.

Image Annotation

iMerit subject matter experts will guide you through the process to develop a customized end-to-end workflow.

Expert Consultation

Transformative, solution-based approach. Interdisciplinary video annotation problem solving. Agility and responsiveness, time-to-value enhancers.


Targeted resources. Custom skilling. Focused and deep microlearning curriculum. Domain expertise. Rostering tools.

Workflow Customization

Alignment of video annotation tools and processes. Structured Development Milestones. Two-step production and QA annotation workflows.

Feedback Cycle

Transparency via analytics. Real time monitoring and service delivery insights. Edge case Insights. Dynamic model improvement.


Assessment of deliverable. Appraisal of key metrics, quality control processes. Model reconsideration. Analysis of business outcome.

Image Annotation Use Case


Image annotation tool for sports ai

iMerit has developed a customized end-to-end workflow for its engagement with KinaTrax, leveraging its proprietary tools and technology as well as the expertise of its experienced computer vision teams. Our expert labelers extract still images from in-game video footage of the players captured from numerous angles. The images are annotated precisely based on KinaTrax’s requirements.

“iMerit’s data annotation services enable us to build accurate models for hundreds of MLB pitchers and turn these models into actionable insights. We look forward to working with iMerit for seasons to come!” – Steven Cadavid, President, Kinatrax

Semantic Segmentation for Autonomous Vehicles

iMerit employs a team of visual data experts who have performed image annotation on up to 100,000 street images for a client who is a leading global automobile manufacturer and a major contender in the autonomous vehicle segment. The iMerit team has annotated the elements in the images into predetermined classes of objects, ultimately dividing the image into semantically meaningful parts, to train the machine learning algorithm not just to ‘see’ but also to understand and interpret its environment and accuracy.

Detecting operating boundaries with polyline annotation 800 X 600-1

Detecting operating boundaries with polyline annotation

iMerit experts will comb through each pixel of an image using the iMerit proprietary annotation platform to teach a vehicle to accurately detect and identify lines and splines such as street lanes, road markings, directions, divergence, and traffic.

Facial recognition using keypoint annotation

iMerit annotation experts apply keypoints on a face, taking care to apply them to key locations such as the eyes, nose, and mouth. This allows iMerit to create world-class training datasets for use in facial recognition models. This advanced technology is helping in public areas where crimes occur to identity and apprehend perpetrators.

Facial recognition using keypoint annotation

Bounding boxes for better data extraction

To improve the performance of their proprietary tax software, this company worked asked iMerit to annotate a series of documents using bounding boxes. This taught the algorithm to see and automatically extract the numbers that were in the fields on the documentation, sparing users from manual entry and automating and otherwise time-consuming process.

3d cuboids for robotic automation

Through the use of 2D and 3D cuboids, iMerit experts are teaching robots in warehouses, factories, and other sorting and manufacturing facilities how to see and interact with objects. This helps further AI in robotics by allowing the robots to identify the dimensions of a given object, and interact with them accordingly to carry out a certain goal.

3D cuboids for robotic automation
Create larger harvest yields with computer vision

Create larger harvest yields with computer vision

Farmers use computer vision to monitor their crops for pests and plant diseases. Through the use of images collected by drones, iMerit experts can teach a model to recognize pests and potential threats to crop fields by annotating every pixel of a given image.

Image Annotation in Numbers



Images Annotated




We provide image annotation services to AI leaders

Getting Started with Image Annotation

The need for speed in high-quality image annotation has never been greater. iMerit combines the best predictive and automated annotation technology with world-class data annotation and subject matter experts to deliver the data you need to get to production, fast.

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