Instance Segmentation - MATLAB & Simulink (original) (raw)
Main Content
Perform instance segmentation using pretrained deep learning networks and train networks using transfer learning on custom data
Instance segmentation is a computer vision technique that plays a crucial role in tasks requiring precise object localization and the identification of individual object instances, such as in medical imaging and autonomous driving. By combining the principles of object detection and semantic segmentation, instance segmentation provides a more refined understanding of visual data by identifying individual object instances and delineating their boundaries pixel by pixel. Use instance segmentation to precisely identify, classify, and separate individual objects within an image.
You can run inference on an image using a pretrained deep learning network, or train a network using transfer learning. Transfer learning is an approach in which you start with a pretrained network and train it on a custom data set for your application. You can interactively generate ground truth data for training by using the Image Labeler, Video Labeler, or Ground Truth Labeler (Automated Driving Toolbox) app to label pixels and export label data. Instance segmentation requires Deep Learning Toolbox™. Training and inference support CUDA® enabled GPU. Use of a GPU is recommended, and requires Parallel Computing Toolbox™. For more information, see Parallel Computing Support in MathWorks Products (Parallel Computing Toolbox).
Functions
Configure Instance Segmentation Network
| solov2 | Segment objects using SOLOv2 instance segmentation network (Since R2023b) |
|---|---|
| maskrcnn | Detect objects using Mask R-CNN instance segmentation (Since R2021b) |
Perform Inference
| segmentObjects | Segment objects using Mask R-CNN instance segmentation (Since R2021b) |
|---|---|
| segmentObjects | Segment objects using SOLOv2 instance segmentation (Since R2023b) |
Load Training Data
Train Instance Segmentation Networks
| trainSOLOV2 | Train SOLOv2 network to perform instance segmentation (Since R2023b) |
|---|---|
| trainMaskRCNN | Train Mask R-CNN network to perform instance segmentation (Since R2022a) |
Augment and Preprocess Training Data
| posemaskrcnn | Predict object pose using Pose Mask R-CNN pose estimation (Since R2024a) |
|---|---|
| predictPose | Estimate object pose using Pose Mask R-CNN deep learning network (Since R2024a) |
| trainPoseMaskRCNN | Train Pose Mask R-CNN network to perform pose estimation (Since R2024a) |
Topics
Get Started
- Get Started with Instance Segmentation Using Deep Learning
Segment objects using an instance segmentation model such as SOLOv2 or Mask R-CNN. - Get Started with SOLOv2 for Instance Segmentation
Perform multiclass instance segmentation using SOLOv2 and deep learning. - Getting Started with Mask R-CNN for Instance Segmentation
Perform multiclass instance segmentation using Mask R-CNN and deep learning.
Train Data for Instance Segmentation
- Get Started with Image Preprocessing and Augmentation for Deep Learning
Preprocess data for deep learning applications with deterministic operations such as resizing, or augment training data with randomized operations such as random cropping. - Datastores for Deep Learning (Deep Learning Toolbox)
Learn how to use datastores in deep learning applications.