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 the Computer Vision Toolbox™.
Datastores for Training Data
|Detect objects using R-CNN deep learning detector|
|Detect objects using Fast R-CNN deep learning detector|
|Detect objects using Faster R-CNN deep learning detector|
|Detect objects using SSD deep learning detector|
|Detect objects using YOLO v2 object detector|
|Detect objects using YOLO v3 object detector|
|Detect objects using YOLO v4 object detector|
|Detect anomalies using fully convolutional data description (FCDD) network for anomaly detection|
|Detect objects using Mask R-CNN instance segmentation|
Text Detection and Recognition
- 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  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.
- Deploy Object Detection Model as Microservice (MATLAB Compiler SDK)
Use a microservice to detect objects in images.
- 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 U-Net.
- 3-D Brain Tumor Segmentation Using Deep Learning
This example shows how to 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 pretrained semantic segmentation network using Grad-CAM.
- Generate Adversarial Examples for Semantic Segmentation (Computer Vision Toolbox)
Generate adversarial examples for a semantic segmentation network using the basic iterative method (BIM).
- 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 .
- Gesture Recognition using Videos and Deep Learning
Perform gesture recognition using a pretrained SlowFast video classifier.