在matlab上部署yolov8目标检测模型的源码+模型+说明（下载即用）

This repository provides multiple pretrained YOLO v8[1] object detection networks for MATLAB®, trained on the COCO 2017[2] dataset. These object detectors can detect 80 different object categories including [person, car, traffic light, etc](/src/%2Bhelper/getCOCOClasess.m). [](https://matlab.mathworks.com/open/github/v1?repo=matlab-deep-learning/Pretrained-YOLOv8-Network-For-Object-Detection) **Creator**: MathWorks Development **Includes Codegen support**: ✔ **Includes transfer learning script**: ✔ **Includes Simulink support script**: ✔ ## License The software and model weights are released under the [GNU Affero General Public License v3.0](LICENSE). For alternative licensing, contact [Ultralytics Licensing](https://www.ultralytics.com/license). ## Requirements - MATLAB® R2024a or later - Computer Vision Toolbox™ - Deep Learning Toolbox™ - Deep Learning Toolbox Converter for ONNX Model Format - (optional) Visual Studio C++ compiler for training on Windows - (optional) MATLAB® Coder for code generation - (optional) GPU Coder for code generation ## Getting Started Download or clone this repository to your machine and open it in MATLAB®. ### Download the pretrained network Use the code below to download the pretrained network. ```matlab % Load YOLO v8 model det = yolov8ObjectDetector('yolov8s'); % Analyze loaded model analyzeNetwork(det.Network); ``` modelName of the pretrained YOLO v8 deep learning model, specified as one of these: - yolov8n - yolov8s - yolov8m - yolov8l - yolov8x Following is the description of various YOLO v8 models available in this repo: | Model | Description | |-------------- |--------------------------------------------------------------------------------------------------------------------------------------------------------------------| | yolov8n | Nano pretrained YOLO v8 model optimized for speed and efficiency. | | yolov8s | Small pretrained YOLO v8 model balances speed and accuracy, suitable for applications requiring real-time performance with good detection quality. | | yolov8m | Medium pretrained YOLO v8 model offers higher accuracy with moderate computational demands. | | yolov8l | Large pretrained YOLO v8 model prioritizes maximum detection accuracy for high-end systems, at the cost of computational intensity. | | yolov8x | Extra Large YOLOv8 model is the most accurate but requires significant computational resources, ideal for high-end systems prioritizing detection performance. | ### Detect Objects Using Pretrained YOLO v8 To perform object detection on an example image using the pretrained model, utilize the provided code below. ```matlab % Read test image. I = imread(fullfile('data','inputTeam.jpg')); % Load YOLO v8 small network. det = yolov8ObjectDetector('yolov8s'); % Perform detection using pretrained model. [bboxes, scores, labels] = detect(det, I); % Visualize detection results. annotations = string(labels) + ': ' + string(scores); Iout = insertObjectAnnotation(I, 'rectangle', bboxes, annotations); figure, imshow(Iout); ```  ## Metrics and Evaluation ### Size and Accuracy Metrics | Model | Input image resolution | Size (MB) | mAP | |-------------- |:----------------------:|:---------:|:----:| | yolov8n | 640 x 640 | 10.7 | 37.3 | | yolov8s | 640 x 640 | 37.2 | 44.9 | | yolov8m | 640 x 640 | 85.4 | 50.2 | | yolov8l | 640 x 640 | 143.3 | 52.9 | | yolov8x | 640 x 640 | 222.7 | 53.9 | mAP for models trained on the COCO dataset is computed as average over IoU of .5:.95. ## Transfer learning Training of a YOLO v8 object detector requires GPU drivers. Additionally, when training on Windows, the Visual Studio C++ compiler is required to facilitate the process. ### Setup Run `installUltralytics.m` to install the required Python files and set up the Python environment for training YOLOv8. This MATLAB script automates downloading and setting up a standalone Python environment tailored for YOLOv8 training. It determines the system architecture, downloads the appropriate Python build, extracts it, and configures MATLAB settings to use this Python interpreter. Finally, it installs the Ultralytics package and its dependencies using `pip`. ### Obtain data Use the code below to download the multiclass object detection dataset, or the subsequent steps can be followed to create a custom dataset. The dataset downloaded using the following command will already be in the required format, allowing the [Train YOLO v8 object detector](https://github.com/matlab-deep-learning/Pretrained-YOLOv8-Network-For-Object-Detection/tree/main?tab=readme-ov-file#train-yolo-v8-object-detector) section to be proceeded with directly. ```matlab helper.downloadMultiClassData() ``` For more information about multiclass object detection dataset, see [Multiclass Object Detection Using YOLO v2 Deep Learning](https://in.mathworks.com/help/vision/ug/multiclass-object-detection-using-yolo-v2-deep-learning.html) example. ### Configure custom data to train YOLO v8 object detector Refer to `getYOLOFormat.m` to configure custom data to the YOLO format for training the YOLO v8 object detector. The following sections provides more details on the dataset format. #### Dataset directory structure for custom dataset When using custom dataset for YOLO v8 training, organize training and validation images and labels as shown in the datasets example directory below. It is mandatory to have both training and validation data to train YOLO v8 network. <img src="/data/datasetDir.png" alt="datasetDir" width="500"/> #### Dataset format for custom dataset Labels for training YOLO v8 must be in YOLO format, with each image having its own *.txt file. If an image contains no objects, a *.txt file is not needed. Each *.txt file should have one row per object in the format: class xCenter yCenter width height, where class numbers start from 0, following a zero-indexed system. Box coordinates should be in normalized xywh format, ranging from 0 to 1. If the coordinates are in pixels, divide xCenter and width by the image width, and yCenter and height by the image height as shown below. <img src="/data/datasetFormat.png" alt="datasetFormat" width="500"/> The label file corresponding to the above image contains 3 chairs (class 2).  Refer to the code below to convert bounding box [xCord, yCord, wdt, ht] to [xCenter, yCenter, wdt, ht] format. ```matlab boxes(:,1) = boxes(:,1) + (boxes(:,3)./2); boxes(:,2) = boxes(:,2) + (boxes(:,4)./2); ``` Refer to the code below to normalize ground truth bounding box [xCenter, yCenter, wdt, ht] with respect to input image. ```matlab % Read input image. [Irow, Icol, ~] = size(inpImage); % Normalize xCenter and width of ground truth bounding box. xCenter = xCenter./Icol; wdt = wdt./Icol; % Normalize yCenter and height of ground truth bounding box. yCenter = yCenter./Irow; ht = ht./Irow; ``` ### Train YOLO v8 object detector Run below code to train YOLO v8 object detector on multiclass object detection dataset. For more information about evaluation, see [Multiclass Object Detection Using YOLO v2 Deep Learning](https://in.mathworks.com/help/vision/ug/multiclass-object-detection-using-yolo-v2-deep-learning.html) example. ```matlab yolov8Det = trainYOLOv8ObjectDetector('data.yaml','yolov8n.pt', ImageSize=[720 720 3