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yolov3ObjectDetector

Create YOLO v3 object detector

Description

The yolov3ObjectDetector object creates a you only look once version 3 (YOLO v3) object detector for detecting objects in an image. Using this object, you can:

  • Create a pretrained YOLO v3 object detector by using YOLO v3 deep learning networks trained on COCO dataset.

  • Create a custom YOLO v3 object detector by using any pretrained or untrained YOLO v3 deep learning network.

Creation

Description

Pretrained YOLO v3 Object Detector

example

detector = yolov3ObjectDetector(name) creates a pretrained YOLO v3 object detector by using YOLO v3 deep learning networks trained on a COCO dataset.

Custom YOLO v3 Object Detector

detector = yolov3ObjectDetector(name,classes,aboxes) creates a pretrained YOLO v3 object detector and configures it to perform transfer learning using a specified set of object classes and anchor boxes. For optimal results, you must train the detector on new training images before performing detection.

detector = yolov3ObjectDetector(net,classes,aboxes) creates an object detector by using the deep learning network net.

If net is a pretrained YOLO v3 deep learning network, the function creates a YOLO v3 object detector and configures it to perform transfer learning with the specified object classes and anchor boxes.

If net is an untrained YOLO v3 deep learning network, the function creates a YOLO v3 object detector and configures it for object detection. classes and aboxes specify the object classes and the anchor boxes, respectively, for training the YOLO v3 network.

You must train the detector on a training dataset before performing object detection.

example

detector = yolov3ObjectDetector(baseNet,classes,aboxes,'DetectionNetworkSource',layer) creates a YOLO v3 object detector by adding detection heads to a base network, baseNet.

The function adds detection heads to the specified feature extraction layers layer in the base network. To specify the names of the feature extraction layers, use the name-value argument 'DetectionNetworkSource',layer.

If baseNet is a pretrained deep learning network, the function creates a YOLO v3 object detector and configures it to perform transfer learning with the specified object classes and anchor boxes.

If baseNet is an untrained deep learning network, the function creates a YOLO v3 object detector and configures it for object detection. classes and aboxes specify the object classes and the anchor boxes, respectively, for training the YOLO v3 network.

You must train the detector on a training dataset before performing object detection.

detector = yolov3ObjectDetector(___,Name,Value) sets the InputSize and ModelName properties of the object detector by using name-value pair arguments. Name is the property name and Value is the corresponding value. You must enclose each property name in quotes.

Note

This function requires the Computer Vision Toolbox™ Model for YOLO v3 Object Detection. You can install the Computer Vision Toolbox Model for YOLO v3 Object Detection from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons. To run this function, you will require the Deep Learning Toolbox™.

Input Arguments

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Name of the pretrained YOLO v3 deep learning network, specified as one of these:

  • 'darknet53-coco' — A pretrained YOLO v3 deep learning network created using DarkNet-53 as the base network and trained on COCO dataset.

  • 'tiny-yolov3-coco' — A pretrained YOLO v3 deep learning network created using a small base network and trained on COCO dataset.

Data Types: string

Names of object classes for training the detector, specified as a string vector, cell array of character vectors, or categorical vector. This argument sets the ClassNames property of the yolov3ObjectDetector object.

Data Types: char | string | categorical

Anchor boxes for training the object detector, specified as an N-by-1 cell array. N is the number of output layers in the YOLO v3 deep learning network. Each cell contains an M-by-2 matrix, where M is the number of anchor boxes in that layer. Each row in the M-by-2 matrix denotes the size of an anchor box in the form [height width]. This argument sets the AnchorBoxes property of the yolov3ObjectDetector object.

Data Types: cell

YOLO v3 deep learning network, specified as a dlnetwork (Deep Learning Toolbox) object. The input network can be either an untrained or a pretrained deep learning network.

Base network for creating the YOLO v3 deep learning network, specified as a dlnetwork (Deep Learning Toolbox) object, or DAGNetwork (Deep Learning Toolbox) object. The network can be either an untrained or a pretrained deep learning network.

Names of the feature extraction layers in the base network, specified as a cell array of character vectors, or a string array.

The function creates a YOLO v3 network by adding detection head layers to the output of the feature extraction layers in the base network. The feature extraction layers must be specified in the reverse of the order in which they appear in the network architecture. For example, given a base network with four feature extraction layers, you must add the first detection head to the fourth feature extraction layer, the second detection head to the third feature extraction layer, and so on.

Example: layer = {'conv10','fire9-concat','fire8-concat'}

Example: layer = ["conv10","fire9-concat","fire8-concat"]

Data Types: char | string | cell

Properties

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This property is read-only.

YOLO v3 deep learning network to use for object detection, stored as a dlnetwork (Deep Learning Toolbox) object.

This property is read-only.

Names of object classes to detect, stored as a categorical vector. You can set this property by using the input argument classes.

This property is read-only.

Set of anchor boxes, stored as a N-by-1 cell array. Each element in the cell is a M-by-2 matrix. Each row in the M-by-2 matrix denotes the size of the anchor box in the form of [height width]. M denotes the number of anchor boxes. N is the number of output layers in the YOLO v3 deep learning network for which the anchor boxes are defined.

You can set this property by using the input argument aboxes.

This property is read-only.

Set of image sizes used for training, stored as an M-by-2 matrix of type double. Each row is of the form [height width]. To set this property, specify it at object creation.

For example, detector = yolov3ObjectDetector(net,classes,aboxes,'InputSize',[220 220; 440 440]).

Name for the object detector, stored as a character vector or string scalar. To set this property, specify it at object creation.

For example, yolov3ObjectDetector(net,classes,aboxes,'ModelName','customDetector') sets the name for the object detector to 'customDetector'.

Object Functions

detectDetect objects using YOLO v3 object detector
preprocessPreprocess training and test images
forwardCompute YOLO v3 deep learning network output for training
predictCompute YOLO v3 deep learning network output for inference

Examples

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Specify the name of a pretrained YOLO v3 deep learning network.

name = 'tiny-yolov3-coco';

Create YOLO v3 object detector by using the pretrained YOLO v3 network.

detector = yolov3ObjectDetector(name);

Display and inspect the properties of the YOLO v3 object detector.

disp(detector)
  yolov3ObjectDetector with properties:

        Network: [1×1 dlnetwork]
    AnchorBoxes: {2×1 cell}
     ClassNames: [1×80 categorical]
      InputSize: [416 416]
     Learnables: [48×3 table]
          State: [22×3 table]
      ModelName: 'tiny-yolov3-coco'

Use analyzeNetwork to display the YOLO v3 network architecture and get information about the network layers. The network has two detection heads attached to the feature extraction network.

analyzeNetwork(detector.Network)

Detect objects in an unknown image by using the pretrained YOLO v3 object detector.

img = imread('sherlock.jpg');
img = preprocess(detector,img);
[bboxes,scores,labels] = detect(detector,img,'DetectionPreprocessing','none');

Display the detection results.

detectedImg = insertObjectAnnotation(img,'Rectangle',bboxes,labels);
figure
imshow(detectedImg)

This example shows how to create a custom YOLO v3 object detector by using a pretrained SqueezeNet as the base network.

Load a pretrained SqueezeNet network. The SqueezeNet network is a convolutional neural network that you can use as the base network for creating a YOLO v3 object detector.

net = squeezenet
net = 
  DAGNetwork with properties:

         Layers: [68×1 nnet.cnn.layer.Layer]
    Connections: [75×2 table]
     InputNames: {'data'}
    OutputNames: {'ClassificationLayer_predictions'}

Inspect the architecture of the base network by using analyzeNetwork (Deep Learning Toolbox) function.

analyzeNetwork(net)

Specify the anchor boxes and the classes to use to train the YOLO v3 network.

aboxes = {[150,127;97,90;68,67];[38,42;41,29;31,23]};
classes = {'Car','Truck'};

Select two feature extraction layers in the base network to serve as the source for detection subnetwork.

layer = {'fire9-concat','fire8-concat'};

Create a custom YOLO v3 object detector by adding detection heads to the feature extraction layers of the base network. Specify the model name, classes, and the anchor boxes.

detector = yolov3ObjectDetector(net,classes,aboxes,'ModelName','Custom YOLO v3','DetectionNetworkSource',layer);

Inspect the architecture of the YOLO v3 deep learning network by using analyzeNetwork (Deep Learning Toolbox) function.

analyzeNetwork(detector.Network)

Inspect the properties of the YOLO v3 object detector. You can now train the YOLO v3 object detector on a custom training dataset and perform object detection.

disp(detector)
  yolov3ObjectDetector with properties:

        Network: [1×1 dlnetwork]
    AnchorBoxes: {2×1 cell}
     ClassNames: [Car    Truck]
      InputSize: [227 227]
     Learnables: [66×3 table]
          State: [6×3 table]
      ModelName: 'Custom YOLO v3'

For information about how to train a YOLO v3 object detector, see the Object Detection Using YOLO v3 Deep Learning example.

Introduced in R2021a