matlab yolov5

YoloV5 is an object detection algorithm that can be implemented in MATLAB. To use YoloV5 in MATLAB, you can follow these steps:

1. Install MATLAB Deep Learning Toolbox and Computer Vision Toolbox.
2. Download the YoloV5 model weights from the official repository.
3. Convert the model weights to MATLAB format using the provided Python script.
4. Load the converted model in MATLAB using the "dlnetwork" function.
5. Load the image you want to detect objects in.
6. Preprocess the image using the "preprocessYOLOv5" function.
7. Run the YoloV5 model on the preprocessed image using the "predict" function.
8. Postprocess the output bounding boxes using the "postprocessYOLOv5" function.

Here is an example code snippet:

% Load YoloV5 model
modelName = 'yolov5s';
weightsFile = 'yolov5s.pt';
convertYoloV5WeightsToMATLAB(weightsFile);
net = dlnetwork(YOLOv5(modelName));

% Load image
img = imread('test.jpg');

% Preprocess image
imgSize = size(img);
img = preprocessYOLOv5(img);

% Run YoloV5 model on image
dlImg = dlarray(single(img),'SSCB');
detections = predict(net,dlImg);

% Postprocess output bounding boxes
bboxes = postprocessYOLOv5(detections,imgSize);

Note that this is just an example and you may need to modify it depending on your specific use case.