Computer Vision Using Deep Learning
Apply deep learning to computer vision applications by using Deep Learning Toolbox™ together with Computer Vision Toolbox™.
Getting Started with Object Detection Using Deep Learning (Computer Vision Toolbox)
Object detection using deep learning neural networks.
This example shows how to perform common kinds of image and bounding box augmentation as part of object detection workflows.
This example shows how to train an object detector using deep learning and R-CNN (Regions with Convolutional Neural Networks).
This example shows how to import a pretrained ONNX™(Open Neural Network Exchange) you only look once (YOLO) v2  object detection network and use it to detect objects.
This example shows how to export a YOLO v2 object detection network to ONNX™ (Open Neural Network Exchange) model format.
Getting Started with Semantic Segmentation Using Deep Learning (Computer Vision Toolbox)
Segment objects by class using deep learning.
This example shows how to create and train a simple semantic segmentation network using Deep Network Designer.
This example shows how to perform common kinds of image and pixel label augmentation as part of semantic segmentation workflows.
Train a semantic segmentation network using dilated convolutions.
This example shows how to perform semantic segmentation of a multispectral image with seven channels using a U-Net.
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.
This example shows how to define and create a custom pixel classification layer that uses Tversky loss.
This example shows how to explore the predictions of a semantic segmentation network using Grad-CAM.
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 .
Perform gesture recognition using a pretrained SlowFast video classifier.