Instance vs. Semantic Segmentation: Similarities & Differences

Image segmentation is a computer vision technique for dividing an image into groups of pixels according to some criteria. The algorithm takes an image as input and divides it into regions or segments, through contours or masks (usually masks).

Segmentation allows computer vision models to understand structure, shapes, and object boundaries. You use segmentation when object outlines matter more than general image classification.

Two major segmentation approaches are semantic segmentation and instance segmentation. Both classify pixels, but only one distinguishes individual objects.

In this guide, we review both techniques and understand their differences. By the end, you will be able to differentiate them clearly.

Let's get started.

Instance Segmentation

Before introducing instance segmentation, it helps to start with the most essential image-related techniques.

The most basic image annotation type is image classification. It involves giving the photo a single label, like cat. This indicates that the image contains a cat, and nothing more. No drawings, no coordinates, no detection.

Localization finds our single object (the cat) and draws a tight bounding box around it. This box shows where the object is located in the image. It provides x, y, height, and width coordinates.

Combining classification and localization for multiple cat instances gives us object detection. Each cat instance is separately localized and has its own bounding box. Each is treated as an independent entity.

Instance segmentation is an extension of object detection. It uses pixel-level mask classification instead of (or alongside) bounding boxes. Both detect and localize objects, but instance segmentation does so more precisely with masks, especially for objects with irregular boundaries.

In short, instance segmentation performs pixel-level classification while also identifying each object instance separately. It provides both class labels and unique masks. It achieves this using connected polygons rather than simple rectangular bounding boxes.

With instance segmentation, it follows, each object gets its own segmented region. You connect points along the borders of the object, and the area inside forms the segmented mask.

This allows you to differentiate one cat from another even if they overlap.

A sample coordinate system for the first Petronas Tower in COCO format would look like this:

{
	"pk":"90056cde-ce60-4157-801e-07d16fd4a05b",
	"class":93,
	"points":[[518.3,3825.7],[580.7,3563.4],[686.8,3207.5],
		[761.8,2870.4],[836.7,2608.1],[899.1,2377.1],[980.3,2277.2],
		[1042.8,2052.4],[1099,1821.4],[1173.9,1671.5],
		[1255.1,1496.7],[1317.5,1346.8],[1404.9,1259.4],
		[1498.6,1196.9],[1567.3,1109.5],[1673.4,1022.1],
		[1723.4,940.9],[1779.6,797.3],[1767.1,959.7],
		[1804.5,1065.8],[1879.5,1090.8],[1923.2,1221.9],
		[1973.1,1284.4],[2016.8,1378],[2060.5,1484.2],[2098,1609.1],
		[2116.7,1783.9],[2141.7,1908.8],[2204.2,1946.2],
		[2204.2,2196],[2204.2,2395.8],[2191.7,2539.4],
		[2272.8,2576.9],[2254.1,2820.4],[2247.9,3045.2],
		[2260.3,3344.9],[2272.8,3582.2],[2254.1,3675.8],
		[2141.7,3750.8],[2160.4,3844.4],[1479.8,3850.7],
		[668.1,3850.7],[512,3819.5]],
	"isVisible":true,
	"markup_type":"polygon"
},

It shows the primary key, visibility, markup type, and class. Most importantly, it includes coordinate pairs (X, Y) for the model to precisely locate the object.

These features make instance segmentation more accurate, but also more expensive. It requires longer annotation time, more model training, higher latency, greater memory use, and more complex deployment.

Therefore, instance segmentation should be used where the additional accuracy justifies the higher cost.

Semantic Segmentation

Semantic segmentation also assigns labels to image regions. However, the goal here is not to distinguish individual objects, but to assign labels such as car, road, or passenger to each pixel in the image.

It shows where specific classes are located. But it does not separate multiple objects of the same class. For example, if you have two towers in an image, semantic segmentation labels both as tower without differentiating them:

Both towers merge into one mask. They are not treated as separate objects. This is the key difference from instance segmentation.

For the image above, the exact annotation output in the COCO format differs as well:

[
	{
		"classID":1,
		"contours":[
		{
			"id":"301147b9-3688-44d2-b0d9-15fc8ed86960",
			"points":[[5614.964444444445,3843.423888888889]]
		},
		{
			"id":"7b77979f-df13-413a-816c-4ac0edb7f3a1",
			"points":[[4394.32,3101.4005555555555],
				[4395.926111111111,3099.7944444444443],
				[4397.532222222222,3101.4005555555555],
				[4395.926111111111,3103.0066666666667]]
		},
	}
]
...

I cut the output short because it would be literally thousands of lines because each point in the image is being assigned to a class.

This is sometimes called pixel-perfect labeling. It provides very high accuracy, but is very costly and time-consuming to produce at scale.

As a result, semantic segmentation is often used where precision is critical, such as in medical imaging.

Panoptic Segmentation

Panoptic segmentation combines the two techniques to create pixel-perfect masks that also separate individual objects:

• Things: countable objects (cars, people, chairs).
• Stuff: non-countable background regions (road, sky, grass).

This approach provides a complete scene structure and is useful when both object-level detail and region-level understanding are required.

Key Differences

So, we have extensively covered both instance and semantic segmentation types. Here's the quick summary table:

Common Model Types

To complete the discussion, here are some of the popular computer vision models for each type:

Semantic Segmentation

• U-Net: Uses an encoder–decoder structure with skip connections to preserve spatial detail. Common in biomedical and satellite imagery.
• DeepLabV3+: Uses atrous convolutions and a decoder to refine object boundaries across different scales.
• SegFormer: Transformer-based model without heavy convolutions. Efficient and scalable across image sizes.

Instance Segmentation

• Mask R-CNN: Extends Faster R-CNN with a branch that predicts pixel masks for detected objects.
• YOLO-based segmentation heads: Generate bounding boxes and masks in a single pass for real-time segmentation.
• Segment Anything (SAM): Produces masks from prompts and is useful for annotation acceleration.

Panoptic segmentation often extends these base models to joint outputs.

Conclusion

Semantic segmentation provides category-level pixel labeling. Instance segmentation adds object-level separation. The best choice depends on the task:

• If you need region understanding → use semantic segmentation.
• If you need to count or track objects → use instance segmentation.
• If you need both → use panoptic segmentation.

That said, be aware that higher accuracy gains come at the cost of expensive data labeling, higher expenses, and longer development.

Explore More

Check out these resources for more on image segmentation:

• Guide to Image Instance Segmentation
• Guide to Pixel-perfect Image Labeling
• Intro to Polygon Brush Annotation

References

• Hojiakbar Barotov (Feb 20, 2025). Guide to Image Instance Segmentation. Unitlab Blog: Source
• Jacob Solawetz (Nov 26, 2024). What is Instance Segmentation? A Guide. [2025] Roboflow Blog: Source
• Keylabs. (Mar 18, 2024). Instance vs Semantic Segmentation: Understanding the Difference. Keylabs: Source
• Media Cybernetics (Aug 28, 2025). Semantic vs. Instance Segmentation in Microscopy: A Complete Guide | AI Essentials. Media Cybernetics: Source