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Computer Vision Using Deep Learning

Extend deep learning workflows with computer vision applications

Apply deep learning to computer vision applications by using Deep Learning Toolbox™ together with Computer Vision Toolbox™.

Apps

Image LabelerLabel images for computer vision applications
Video LabelerLabel video for computer vision applications

Functions

boxLabelDatastoreDatastore for bounding box label data
pixelLabelDatastoreDatastore for pixel label data
pixelLabelImageDatastoreDatastore for semantic segmentation networks

Topics

Object Detection

Getting Started with Object Detection Using Deep Learning (Computer Vision Toolbox)

Object detection using deep learning neural networks.

Augment Bounding Boxes for Object Detection

This example shows how to perform common kinds of image and bounding box augmentation as part of object detection workflows.

Train Object Detector Using R-CNN Deep Learning

This example shows how to train an object detector using deep learning and R-CNN (Regions with Convolutional Neural Networks).

Import Pretrained ONNX YOLO v2 Object Detector

This example shows how to import a pretrained ONNX™(Open Neural Network Exchange) you only look once (YOLO) v2 [1] object detection network and use it to detect objects.

Export YOLO v2 Object Detector to ONNX

This example shows how to export a YOLO v2 object detection network to ONNX™ (Open Neural Network Exchange) model format.

Semantic Segmentation

Getting Started with Semantic Segmentation Using Deep Learning (Computer Vision Toolbox)

Segment objects by class using deep learning.

Train Simple Semantic Segmentation Network in Deep Network Designer

This example shows how to create and train a simple semantic segmentation network using Deep Network Designer.

Augment Pixel Labels for Semantic Segmentation

This example shows how to perform common kinds of image and pixel label augmentation as part of semantic segmentation workflows.

Semantic Segmentation Using Dilated Convolutions

Train a semantic segmentation network using dilated convolutions.

Semantic Segmentation of Multispectral Images Using Deep Learning

This example shows how to perform semantic segmentation of a multispectral image with seven channels using a U-Net.

3-D Brain Tumor Segmentation Using Deep Learning

This example shows how to train a 3-D U-Net neural network and perform semantic segmentation of brain tumors from 3-D medical images.

Define Custom Pixel Classification Layer with Tversky Loss

This example shows how to define and create a custom pixel classification layer that uses Tversky loss.

Explore Semantic Segmentation Network Using Grad-CAM

This example shows how to explore the predictions of a semantic segmentation network using Grad-CAM.

Video Classification

Activity Recognition from Video and Optical Flow Data Using Deep Learning

This example first shows how to perform activity recognition using a pretrained Inflated 3-D (I3D) two-stream convolutional neural network based video classifier and then shows how to use transfer learning to train such a video classifier using RGB and optical flow data from videos [1].

Gesture Recognition using Videos and Deep Learning

Perform gesture recognition using a pretrained SlowFast video classifier.

Featured Examples