Top Instance Segmentation Models

Measure objects' size and shape. These models are ready to go; often with pre-trained weights and exports available for mobile or server-side inference.

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Instance Segmentation
Instance Segmentation
Instance Segmentation
fEATURED
Instance Segmentation

Model Size:

MB

Parameters:

Top FPS:

Architecture:

Instance Segmentation
Instance Segmentation
Instance Segmentation
fEATURED
Instance Segmentation

Model Size:

MB

Parameters:

Top FPS:

Architecture:

Instance Segmentation
Instance Segmentation
Instance Segmentation
fEATURED
Instance Segmentation

Model Size:

MB

Parameters:

Top FPS:

Architecture:

A simple, fully convolutional model for real-time instance segmentation Learn more »
Instance Segmentation
Instance Segmentation
Instance Segmentation
fEATURED
Instance Segmentation

Model Size:

MB

Parameters:

Top FPS:

Architecture:

FastSAM is an image segmentation model trained using 2% of the data in the Segment Anything Model SA-1B dataset. Learn more »
Instance Segmentation
Instance Segmentation
Instance Segmentation
fEATURED
Instance Segmentation

Model Size:

MB

Parameters:

Top FPS:

Architecture:

Detic is an open source segmentation model developed by Meta Research and released in 2022. Learn more »
Instance Segmentation
Instance Segmentation
Instance Segmentation
fEATURED
Instance Segmentation

Model Size:

MB

Parameters:

Top FPS:

Architecture:

Segment Anything (SAM) is an image segmentation model developed by Meta Research, capable of doing zero-shot segmentation. Learn more »
Instance Segmentation
Instance Segmentation
Instance Segmentation
fEATURED
Instance Segmentation

Model Size:

MB

Parameters:

Top FPS:

Architecture:

The state-of-the-art YOLOv8 model comes with support for instance segmentation tasks. Learn more »
Instance Segmentation
Instance Segmentation
Instance Segmentation
fEATURED
Instance Segmentation

Model Size:

MB

Parameters:

Top FPS:

Architecture:

YOLOv7 Instance Segmentation lets you perform segmentation tasks with the YOLOv7 model. Learn more »
Instance Segmentation
Instance Segmentation
Instance Segmentation
fEATURED
Instance Segmentation

Model Size:

MB

Parameters:

219 million

Top FPS:

Architecture:

Transformers

OneFormer is a state-of-the-art multi-task image segmentation framework that is implemented using transformers. Learn more »
Instance Segmentation
Instance Segmentation
Instance Segmentation
fEATURED
Instance Segmentation

Model Size:

MB

Parameters:

Top FPS:

Architecture:

Mask RCNN is a convolutional neural network for instance segmentation. Learn more »
Instance Segmentation
Instance Segmentation
Instance Segmentation
fEATURED
Instance Segmentation

Model Size:

MB

Parameters:

Top FPS:

Architecture:

CNN, YOLO

YOLOv5 Instance Segmentation is a version of YOLOv5 that can be used for instance segmentation tasks. Learn more »

Frequently Asked Questions

What is instance segmentation?

Instance segmentation identifies objects in an image and maps each pixel to the identified objects. With instance segmentation, you can find exactly where an object is in an image. For example, one could use an instance segmentation model to find all the pixels associated with a forklift in an image.

What are the use cases for instance segmentation?

Instance segmentation models are useful when you need to identify the exact pixels that are connected with an object. This is useful in a number of situations, such as:

  • Analyzing medical images to detect abnormalities.
  • Identifying disease on plants.
  • Identifying different objects on a road to guide a self-driving car.

What models are used for instance segmentation?

The YOLOv5 instance segmentation and the Detectron2 Mask RCNN models are commonly used for instance segmentation.

Where can I learn more about instance segmentation?

See more learning resources

Instance Segmentation Datasets and Demos

Roboflow Universe contains over 100,000 open-source datasets and trained models, many of which you can use for instance segmentation tasks. Below are a few of the many models you can use.

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