Semantic Image Segmentation Services

On-demand, Pixel-perfect Semantic Image Segmentation Services, at Scale

Secure high-accuracy human-annotated data at pixel perfect level. With semantic image segmentation, our experienced annotators accurately label and classify data that helps visual perception models learn at the highest standard of accuracy.

BUNCH allocates highly trained annotators to handle millions of images to be useful for computer vision and deep learning. Through strict QA implementation and double-pass annotation techniques, we ensure in-depth labeling and analysis for your images from a predefined set of classes.

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Semantic Image Segmentation Outsourcing Services.
High-Precision Data For Your ML Models

Kickstart your annotation project in days, not weeks

Get your proposal in less than 24 hours

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Semantic Segmentation Annotators

We Analyze And Assign Labels On Images At Pixel Level

Image segmentation or also known as semantic segmentation is an image annotation technique used to classify each pixel in the image. Unlike bounding boxes that can overlap each other, image segmentation is used to label and detect each pixel in an image and segment them to a single class.

High-accuracy image segmentation is crucial for the overall performance of AI-based perception models. In order for these models to perform its best, it needs a lot of high-accuracy data to understand and visualize objects perfectly.

Semantic segmentation is an authoritative technique that our annotation teams have mastered to produce high-quality data that enables your models to perform at optimum level. Some of the applications that require an in-depth analysis of images include automated vehicles, drones and satellites, AI-based medical diagnosis models, robotics, media, and many more.

At BUNCH, your images go through strict QA implementation and double-pass annotation techniques to ensure that images are accurately labeled and assigned to the right classes. Our annotation teams are able to decipher and analyze objects in several image and video qualities.

In-Depth Quality Assessments For Consistent and Accurate Training Data

Semantic image segmentation is a more complex technique to produce training data for computer vision. Accuracy and consistency are important in training ML models. If sidewalks are labeled as roads then that would affect your machine's performance. This is why we enforce the most accurate and consistent image segmentation processes possible. All AI projects are different and our priority is to produce quality training data through rigorous quality assessments that match with your specific AI initiatives.

Accuracy is how close a label is to the truth while consistency is when multiple annotations on different training datasets agree with one another. Our highly skilled annotators ensure both the accuracy and consistency of annotated images by carefully labelling the information you plan to use with your algorithms.

Our senior project management team investigates your data regularly. We conduct in-depth quality control, weekly data deep-dives, and QA audits to ensure that productivity and consistent quality thresholds are met.

High-Accuracy Semantic Image Segmentation For ML Models

Our intensive exposure to different use cases from high-growth industries enable us to provide the highest quality of semantic segmentation results. Here are some of the many applications for which semantic image segmentation is useful.

Semantic Image Segmentation For Medical Imaging

Due to the complexity of medical images, AI-backed medical diagnosis models use semantic segmentation to analyze and segment organs, parts of the brain, cells, and tumors from MRI and CT scans. These help doctors accurately diagnose and treat localized areas.

Semantic Image Segmentation for Facial Recognition

Aside from keypoint or landmark annotation, image segmentation helps improve ML models such as self-driving cars and robots for facial recognition, motion estimation, and movement prediction. Semantic segmentation enable these machines to visualize and track moving objects accurately.

Semantic Image Segmentation for Aerial Imagery

Semantic segmentation is used to train drones and satellites to detect what is on the ground from an aerial perspective. Satellites require immense aerial coverage to process geographical areas and analyze objects of interest. This can also be used for high-precision geo-mapping.

Scenario: What type of Content Moderation tasks could you outsource


Moderating articles, images, audio and videos requires a lot of time and resources. Screening, monitoring and approving these forms of media happen around the clock. 

Taking YouTube as an example, when someone uploads a clip there is a human element of moderation which aids in determining whether the uploaded video is good to go or not. Also, the implications of not moderating content will prove to be negative.


Our experienced Content Moderation specialists can fill any gaps in your work process. You can initially build a small team and later expand if necessary. Content Moderation applies to many forms of media and you can split your work load by setting up support teams based on the moderated media - or however way you prefer. 

Whether you need a general content moderation team to support your operations because of a high workload or to outsource certain parts of the process, BUNCH will help your business move forward.


Scenario: Help your business meet deadlines faster


The content moderation process can be smooth and trouble-free, but with the ever growing landscape of social media and internet content distribution, the volume of that needs to be moderated, real-time is always on the increase. 

Which is why your clients rely on you to moderate their content, failing to do so (including not meeting deadlines) could give your business a bad impression for your clients.


By hiring dedicated content moderation specialists you will meet your deadlines faster. Our agents will comply with your client standards, oversee content for accuracy, errors and other filters. 

Adding cost-effective specialists to your current setup will prove beneficial in the long run. Start off with a small team which can later expand to a larger team.


Semantic Image Segmentation Services Pricing

We reinvented the outsourcing model with flexibility in mind. If you have a recurrent need for image annotation, we set up a fully dedicated team for you, or we can also work on a project basis.
$900 to $1,150 /mo

A full-time dedicated annotator
Complimentary QA audits
Custom shifts

from $0.10 /class

High accuracy
Instant scalability
Committed to your deadlines

Get a proposal here

Let’s Get Started!

We compete in tech-oriented talent, instant scalability and flexibility. Our teams are led by tech experts who understand the help that you need. Share your challenges with us and we will send you a custom proposal in less than 24 hours.

Get a proposal in less than 24 hours
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