Configure Pretrained Model

To use a pretrained object detection model, you must first download and install the pretrained object detection model. You can download and install a pretrained model support package using the Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.

This table lists the names of the object detector objects, the corresponding available pretrained models, and the names of the corresponding add-on support packages to download.

Perform Inference Using Pretrained Model

Perform inference and detect objects in a test image using a pretrained detector model. For help selecting a pretrained object detection network for your application, see Choose an Object Detector. To return bounding boxes, confidence scores, and corresponding class labels, pass the pretrained detector object to the corresponding detect object function.

For example, to use the pretrained YOLO v4 tiny-yolov4-coco network listed in the Configure Pretrained Model section, load the model by creating a yolov4ObjectDetector object.

detector = yolov4ObjectDetector("tiny-yolov4-coco");

Detect objects in a test image, I, by using the detect object function of the yolov4ObjectDetector object.

I = imread("carsonroad.png");
[bboxes,scores,labels] = detect(detector,I);

Display the results overlaid on the input image by using the insertObjectAnnotation function.

detectedImg = insertObjectAnnotation(I,"Rectangle",bboxes,labels);
figure
imshow(detectedImg)

To perform inference on a test image using a trained object detection network, use the same process but specify the trained network to the detect function as the detector argument.

MathWorks GitHub Pretrained Networks

The MathWorks^® GitHub repository provides implementations of the latest pretrained object detection deep learning networks to download and use to perform out-of-the-box inference. The pretrained object detection networks have already been trained on standard data sets, such as the COCO and Pascal VOC data sets. You can use these pretrained models directly to detect different objects in a test image.

For a list of all the latest MathWorks pretrained object detectors, see MATLAB Deep Learning (GitHub).

Create Training Data

Use a labeling app to interactively label ground truth data in a video, image sequence, image collection, or custom data source. You can label object detection ground truth using rectangle labels, which define the position and size of the object in the image.

To learn more about labeling images for object detection, see these topics:

Augment and Preprocess Data

Use data augmentation to train the object detector on a limited data set. By altering the data set images in minor ways, such as translating, cropping, or transforming, you can create distinct and unique training data, creating a more robust detector. Use datastores to conveniently read and augment collections of data. Use imageDatastore and the boxLabelDatastore to create datastores for images and labeled bounding box data, respectively.

To learn more about augmenting and pre-processing data for training, see these topics:

For more information about augmenting training data using datastores, see Datastores for Deep Learning (Deep Learning Toolbox) and Perform Additional Image Processing Operations Using Built-In Datastores (Deep Learning Toolbox).

Train Object Detector

To train the object detection network, use a training function that corresponds to your object detection model. For example, use the trainYOLOv4ObjectDetector function if you are using the yolov4ObjectDetector object to configure the detector.

Specify the network training options using the trainingOptions (Deep Learning Toolbox) function. You can determine training options parameters using the Experiment Manager (Deep Learning Toolbox) app. For more information on using Experiment Manager for hyperparameter tuning, see Train Object Detectors in Experiment Manager.

To learn more about training, inference, and evaluating your results, see these examples:

Evaluate and Fine-tune Object Detector Performance

To evaluate the training results against the ground truth with a comprehensive set of metrics, use the evaluateObjectDetection function. The function returns the object detection metrics as an objectDetectionMetrics object. Use these objectDetectionMetrics object functions to evaluate metrics across all, or a selection of, classes and overlap thresholds.

For an example that shows how to use object detection metrics to evaluate and fine-tune an object detector, see the Multiclass Object Detection Using YOLO v2 Deep Learning example. This image shows a sample precision-recall (PR) plot, and the recall and precision plots as a function of confidence score, for selected classes in a data set.