Visión artificial

R2025b

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

`boxLabelDatastore`	Datastore for bounding box label data
`pixelLabelDatastore`	Datastore for pixel label data

`visionTransformer`	Pretrained vision transformer (ViT) neural network (Desde R2023b)
`patchEmbeddingLayer`	Patch embedding layer (Desde R2023b)

`unet`	Create U-Net convolutional neural network for semantic segmentation (Desde R2024a)
`unet3d`	Create 3-D U-Net convolutional neural network for semantic segmentation of volumetric images (Desde R2024a)
`deeplabv3plus`	Create DeepLab v3+ convolutional neural network for semantic image segmentation (Desde R2024a)

`rtmdetObjectDetector`	Detect objects using RTMDet object detector (Desde R2024b)
`yolov4ObjectDetector`	Detect objects using YOLO v4 object detector (Desde R2022a)
`yolov2ObjectDetector`	Detect objects using YOLO v2 object detector
`yolov3ObjectDetector`	Detect objects using YOLO v3 object detector
`ssdObjectDetector`	Detect objects using SSD deep learning detector

`solov2`	Segment objects using SOLOv2 instance segmentation network (Desde R2023b)
`maskrcnn`	Detect objects using Mask R-CNN instance segmentation

posemaskrcnn Predict object pose using Pose Mask R-CNN pose estimation (Desde R2024a)

reidentificationNetwork Re-identification deep learning network for re-identifying and tracking objects (Desde R2024a)

yoloxObjectDetector

efficientADAnomalyDetector

patchCoreAnomalyDetector

fcddAnomalyDetector

fastFlowAnomalyDetector

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

Train Vision Transformer Network for Image Classification
This example shows how to fine-tune a pretrained vision transformer (ViT) neural network to perform classification on a new collection of images.

Get Started with Object Detection Using Deep Learning (Computer Vision Toolbox)
Perform object detection using deep learning neural networks such as YOLOX, YOLO v4, RTMDet, and SSD.
Get Started with Instance Segmentation Using Deep Learning (Computer Vision Toolbox)
Segment objects using an instance segmentation model such as SOLOv2 or Mask R-CNN.
Choose an Object Detector (Computer Vision Toolbox)
Compare object detection deep learning models, such as YOLOX, YOLO v4, RTMDet, and SSD.
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 the network 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.

Get Started with Semantic Segmentation Using Deep Learning (Computer Vision Toolbox)
Segment objects by class using deep learning networks such as U-Net and DeepLab v3+.
Semantic Segmentation Using Dilated Convolutions
This example shows how to train a semantic segmentation network using dilated convolutions.
Semantic Segmentation of Multispectral Images Using Deep Learning (Computer Vision Toolbox)
This example shows how to perform semantic segmentation of a multispectral image with seven channels using U-Net.
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).
Prune and Quantize Semantic Segmentation Network
Reduce the memory footprint of a semantic segmentation network and speed-up inference by compressing the network using pruning and quantization.