Guide to Image Instance Segmentation

We have been exploring different types of image annotation on our blog for the past three months. In this post, we’ll dive into instance segmentation, also known as image instance segmentation, a high-precision image annotation technique.

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Image instance segmentation is a computer vision task that predicts the exact boundaries of each object instance in pixels within an image. In simple terms, it detects, classifies, and segments each object separately. Because it uses exact pixels to define boundaries, it is similar to image segmentation (also called pixel-perfect segmentation) in precision. However, this precision comes at a cost—more time, effort, and processing power. Due to its accuracy, image instance segmentation is widely used in medical imaging, autonomous vehicles, and quality control.

By the end of this guide, you will learn:

• The essentials of instance segmentation
• Real world applications of instance segmentation
• A hands-on demo project for image instance segmentation

What is Instance Segmentation?

Instance segmentation is a more advanced version of object detection that combines classification, detection, and segmentation to distinguish individual objects in an image. While object detection models place bounding boxes around objects, instance segmentation goes further by precisely outlining their boundaries. It does in a very precise way by putting numerous dots along the borders of the object:

Here’s how instance segmentation generally works:

1. Object Detection: The model first detects objects in the image and determines their coordinates. Bounding boxes are generated around detected objects.

2. Segmentation: The model then defines exact pixel coordinates for each object’s boundaries. This helps in understanding the shape and structure of each object. The areas within these boundaries are then classified.

3. Label Assignment: Each segmented object is assigned a unique label or ID, ensuring that multiple objects of the same type (e.g., multiple cars) are still distinguished from each other. This is what sets instance segmentation apart from semantic segmentation.

How is it Different from Image Segmentation?

While instance segmentation and image segmentation may seem similar to each other, especially in the highly accurate labeling they produce, they differ in three key aspects: nature, annotation method, and use case.

1. Nature: Semantic segmentation labels every pixel in an image based on object class but does not differentiate multiple instances of the same object. For example, if an image has five cars, semantic segmentation treats them as a single class, while instance segmentation identifies and separates each car individually.
2. Annotation: Semantic segmentation covers entire objects with pixel labels, while instance segmentation draws precise borders around objects using points and coordinates. This distinction makes image annotation more effective in tasks requiring exact object delineation.
3. Use Case: Both methods are highly precise, but semantic segmentation is often the most detailed because it labels every pixel. It is particularly useful in tasks requiring extreme precision, such as medical imaging, while instance segmentation is preferred when individual object distinction is necessary.

Considerations

Because of its complexity, instance segmentation is significantly slower than object detection—sometimes by a factor of 10 to 100. It also requires more memory and computing power due to its three-step process. As a result, the model latency and required resources are much greater than object detection models.

There’s no perfect solution—only trade-offs. If a project demands high precision, the extra time and high cost of instance segmentation may be justified for its accurate labelings. Otherwise, object detection and classification are usually sufficient for most real life projects. Most public demonstrations focus on object detection because it’s more intuitive and requires fewer resources.

Real-world Applications

Instance segmentation is widely used across various industries, especially in fields that require precise data labeling. Here are some of its most impactful applications:

Autonomous Vehicles

Self-driving cars must analyze the road in real time. They rely on instance segmentation to identify and distinguish pedestrians, vehicles, and road signs, helping ensure safe navigation.

Object detection is sufficient for basic tasks like identifying cars and traffic signs, but in complex urban environments, distinguishing between multiple objects is crucial. Instance segmentation improves safety and accuracy in such scenarios, making it an essential image labeling solution for autonomous driving.

Medical Imaging

Medical imaging involves scanning and analyzing internal body structures for disease detection and treatment. Since precision is critical, both image segmentation and instance segmentation are used in healthcare applications.

Instance segmentation helps in accurately isolating organs, tumors, and cells in medical scans. It is widely used in tasks like tumor detection, organ segmentation, and pathology analysis, aiding radiologists in making precise diagnoses. In this context, data annotation solutions are essential to ensure the highest accuracy in model training.

Inventory Management

Retail and warehouse management require tracking large inventories efficiently. Computer vision, powered by AI, is transforming this process. In tightly packed warehouses, object detection may not be accurate enough. Instance segmentation helps count and classify inventory items with higher accuracy. However, whether it’s necessary depends on the specific use case. AI-driven dataset management tools can optimize inventory tracking by ensuring accurate data labeling for machine learning models.

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Demo Instance Segmentation Project

To better understand how instance segmentation works, let’s create a small project using Unitlab Annotate, an data annotation platform. We’ll set up a project, annotate a small dataset of vehicles and release it as a public dataset.

Project Setup

1. Sign up for a free account on Unitlab Annotate.
2. Navigate to the dashboard and click +Add Project.
3. Choose Image Instance Segmentation as the annotation type and, optionally, select COCO Instance Segmentation as the AI model to speed up labeling.

You can upload your own dataset or download sample images. For a detailed guide, refer to this post.

Data labeling

Once the dataset is uploaded, you can start annotation using Unitlab’s built-in AI models to accelerate the process. The Auto Crop tool can automatically annotate user-defined areas, making manual annotation faster and more precise:

For instance segmentation, you can use traditional polygon tools or polygon brushes with magnets to create accurate annotations. With the magnet tool, you can easily adjust your image annotations without having to adjust individual points that make up the coordinations. Polygon brushes offer a more accurate and flexible approach to instance segmentation because it uses a brush-like tool similar to image segmentation, but the end result is of instance segmentation:

Dataset Release

After annotating a small set of images, we can release the dataset as an open-source resource under the MIT License. As you would not annotate only five images, you would not do all the annotations and dataset management in the same way. Depending on a myriad factors, you would choose a data annotation platform to accelerate this phase of AI/ML development.

Now, as we release our project as a public dataset under MIT License, we can release the public dataset that you can access, copy, and download for your own purposes. If you are under the paid plan with Unitlab Annotate, you can release private datasets that only you can access. This is the standard to keep your proprietary source data private.

Conclusion

Instance segmentation is a powerful technique that enhances object detection by outlining precise boundaries of objects in an image. While it offers superior accuracy, it comes with trade-offs like increased computational costs and processing time.

It is different from semantic segmentation, which is truly a pixel-perfect annotation type. It can segment every pixel in the image to one class, but it cannot differentiate between multiple instances of the same object. There is one segmentation type, called panoptic segmentation, that combines both instance and semantic segmentation, but that's a post for another day.

In the end, we explore how one would approach image instance segmentation with a demo data annotation project within the context of Unitlab Annotate.

Explore More

1. Guide to Pixel-perfect Image Labeling
2. Guide to Object Detection
3. Intro to Polygon Brush Annotation

References

1. Akruti Acharya. (Jan 18, 2024). Instance Segmentation in Computer Vision: A Comprehensive Guide. Encord Blog: https://encord.com/blog/instance-segmentation-guide-computer-vision/
2. IBM. (No date). What is instance segmentation? IBM: https://www.ibm.com/think/topics/instance-segmentation
3. Jacob Solawetz. (Nov 26, 2024). What is Instance Segmentation? A Guide. [2025]. Roboflow Blog: https://blog.roboflow.com/instance-segmentation/