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.
Deploy select models (i.e. YOLOv8, CLIP) using the Roboflow Hosted API, or your own hardware using Roboflow Inference.

Frequently Asked Questions

What models are used for instance segmentation?

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

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 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.