Pretrained YOLO v8 Network For Object Detection

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.

Creator: MathWorks Development

Includes Codegen support: ✔

Includes transfer learning script: ❌

License

The software and model weights are released under the GNU Affero General Public License v3.0. For alternative licensing, contact Ultralytics Licensing.

Requirements

• MATLAB® R2023b or later
• Computer Vision Toolbox™
• Deep Learning Toolbox™
• Deep Learning Toolbox Converter for ONNX Model Format
• (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®.

Setup

Add path to the models directory.

addpath('models');

Download the pretrained network

Use the code below to download the pretrained network.

% Load YOLO v8 model
modelName = 'yolov8s';
model = helper.downloadPretrainedYOLOv8(modelName);
net = model.yolov8Net;

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. Alternatively, you can execute the runInference.m script for the same purpose.

% Read test image.
I = imread(fullfile('data','inputTeam.jpg'));

% Get classnames for COCO dataset.
classNames = helper.getCOCOClassNames;
numClasses = size(classNames,1);

% Load YOLO v8 small network.
modelName = 'yolov8s';
data = helper.downloadPretrainedYOLOv8(modelName);
det = data.yolov8Net;

% Perform detection using pretrained model.
executionEnvironment = 'auto';
[bboxes, scores, labelIds] = detectYOLOv8(det, I, numClasses, executionEnvironment);

% Map labelIds back to labels.
labels = classNames(labelIds);

% 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.

Deployment

Code generation enables you to generate code and deploy YOLO v8 on multiple embedded platforms. The list of supported platforms is shown below:

Target	Support	Notes
GPU Coder	✔	run gpuCodegenYOLOv8.m
MATLAB Coder	✔	run codegenYOLOv8.m

To deploy YOLO v8 to GPU Coder, run gpuCodegenYOLOv8.m. This script calls the yolov8Predict.m entry point function and generate CUDA code for it. It will run the generated MEX and give an output. For more information about codegen, see Deep Learning with GPU Coder.

Network Overview

YOLO v8 is one of the best performing object detectors and is considered as an improvement to the existing YOLO variants such as YOLO v5, and YOLOX.

Following are the key features of the YOLO v8 object detector compared to its predecessors:

• Improved Accuracy: YOLO v8 is expected to offer enhanced accuracy in object detection compared to its previous versions. This improvement can lead to more precise and reliable detection results.
• Better Speed and Efficiency: YOLO v8 may have optimizations that allow it to achieve faster processing speeds while maintaining high accuracy. This can be crucial for real-time applications or scenarios with limited computational resources.
• Advanced Backbone Network: YOLO v8 might incorporate a more advanced backbone network architecture, such as Darknet-53 or a similar architecture, which can enable better feature extraction and representation.
• Enhanced Object Classification: YOLO v8 may introduce improvements in object classification capabilities, allowing for more accurate and detailed classification of detected objects.

References

[1] https://github.com/ultralytics/ultralytics

[2] Lin, T., et al. "Microsoft COCO: Common objects in context. arXiv 2014." arXiv preprint arXiv:1405.0312 (2014).

Copyright 2024 The MathWorks, Inc.