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Visión artificial

Amplíe flujos de trabajo de deep learning con aplicaciones de visión artificial

Aplique deep learning en aplicaciones de visión artificial con Deep Learning Toolbox™ y Computer Vision Toolbox™.

Apps

Image Labeler	Label images for computer vision applications
Video Labeler	Label video for computer vision applications

Funciones

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Almacenes de datos para datos de entrenamiento

`boxLabelDatastore`	Datastore for bounding box label data (desde R2019b)
`pixelLabelDatastore`	Datastore for pixel label data

Segmentación semántica

`deeplabv3plusLayers`	Create DeepLab v3+ convolutional neural network for semantic image segmentation (desde R2019b)
`segnetLayers`	Create SegNet layers for semantic segmentation
`unetLayers`	Create U-Net layers for semantic segmentation
`unet3dLayers`	Create 3-D U-Net layers for semantic segmentation of volumetric images (desde R2019b)

Detección de objetos

`rcnnObjectDetector`	Detect objects using R-CNN deep learning detector
`fastRCNNObjectDetector`	Detect objects using Fast R-CNN deep learning detector
`fasterRCNNObjectDetector`	Detect objects using Faster R-CNN deep learning detector
`ssdObjectDetector`	Detect objects using SSD deep learning detector (desde R2020a)
`yolov2ObjectDetector`	Detect objects using YOLO v2 object detector (desde R2019a)
`yolov3ObjectDetector`	Detect objects using YOLO v3 object detector (desde R2021a)
`yolov4ObjectDetector`	Detect objects using YOLO v4 object detector (desde R2022a)
`fastFlowAnomalyDetector`	Detect anomalies using FastFlow network (desde R2023a)
`fcddAnomalyDetector`	Detect anomalies using fully convolutional data description (FCDD) network for anomaly detection (desde R2022b)
`maskrcnn`	Detect objects using Mask R-CNN instance segmentation (desde R2021b)

Detección y reconocimiento de texto

detectTextCRAFT Detect texts in images by using CRAFT deep learning model (desde R2022a)

Temas

Detección de objetos

Getting Started with Object Detection Using Deep Learning (Computer Vision Toolbox)
Perform object detection and instance segmentation 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.
Deploy Object Detection Model as Microservice (MATLAB Compiler SDK)
Use a microservice to detect objects in images.

Segmentación semántica

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).

Clasificación de vídeo

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.

Ejemplos destacados

Object Detection Using Faster R-CNN Deep Learning

Train a Faster R-CNN (regions with convolutional neural networks) object detector.

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Object Detection Using YOLO v2 Deep Learning

Train a you only look once (YOLO) v2 object detector.

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Point Cloud Classification Using PointNet Deep Learning

Train a PointNet network for point cloud classification.

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Object Detection Using SSD Deep Learning

Train a Single Shot Detector (SSD).

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Object Detection Using YOLO v3 Deep Learning

Train a YOLO v3 object detector.

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Object Detection Using YOLO v4 Deep Learning

Detect objects in images using you only look once version 4 (YOLO v4) deep learning network. In this example, you will

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Perform Instance Segmentation Using Mask R-CNN

Segment individual instances of people and cars using a multiclass mask region-based convolutional neural network (R-CNN).

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Semantic Segmentation Using Deep Learning

Segment an image using a semantic segmentation network.

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Generate Image from Segmentation Map Using Deep Learning

Generate a synthetic image of a scene from a semantic segmentation map.

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Estimate Body Pose Using Deep Learning

Estimate the body pose of one or more people using the OpenPose algorithm.

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Activity Recognition from Video and Optical Flow Data Using Deep Learning

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].

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Gesture Recognition using Videos and Deep Learning

Perform gesture recognition using a pretrained SlowFast video classifier.

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Detect Image Anomalies Using Explainable FCDD Network

Use an anomaly detector to distinguish between normal pills and pills with anomalous chips or contamination.

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Classify Defects on Wafer Maps Using Deep Learning

Classify eight types of manufacturing defects on wafer maps using a simple convolutional neural network (CNN).

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Detect Image Anomalies Using Pretrained ResNet-18 Feature Embeddings

Train a similarity-based anomaly detector using one-class learning of feature embeddings extracted from a pretrained ResNet-18 convolutional neural network.

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