yolov8 opencv dnn部署自己的模型

本文介绍: yolov8 opencv dnn部署自己的模型

windows 10
Visual Studio 2019
Nvidia GeForce GTX 1070
opencv4.7.0 (opencv4.5.5在别的地方看到不支持yolov8的推理，所以只使用opencv4.7.0)

yolov8版本: version = ‘8.0.110’
首先将default.yaml中的一些配置修改以下，将只修改的部分贴上去，注意下面的batch一定要设置为1

task: detect  # YOLO task, i.e. detect, segment, classify, pose
mode: export  # YOLO mode, i.e. train, val, predict, export, track, benchmark

# Train settings -------------------------------------------------------------------------------------------------------
# model: C:UsersHUSTDesktopyolov8_ultralyticsultralyticsmodelsv8yolov8.yaml # path to model file, i.e. yolov8n.pt, yolov8n.yaml
model: C:UsersAdministratorDesktopyolov8_ultralyticsrunsdetectyolov8nweightsbest.pt # path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: C:UsersAdministratorDesktopyolov8_ultralytics/ultralytics/datasets/custom.yaml # path to data file, i.e. coco128.yaml
weights: yolov8n.pt
epochs: 1  # number of epochs to train for
patience: 50  # epochs to wait for no observable improvement for early stopping of training
batch: 1  # number of images per batch (-1 for AutoBatch)

default.yaml中的export部分的配置也需要修改

# Export settings ------------------------------------------------------------------------------------------------------
format: onnx  # format to export to
keras: False  # use Keras
optimize: False  # TorchScript: optimize for mobile
int8: False  # CoreML/TF INT8 quantization
dynamic: False  # ONNX/TF/TensorRT: dynamic axes
simplify: False  # ONNX: simplify model
opset: 12 # ONNX: opset version (optional)
workspace: 4  # TensorRT: workspace size (GB)
nms: False  # CoreML: add NMS

然后直接运行ultralytics/yolo/engine/exporter.py
在这里插入图片描述
测试一下导出的best.onnx可不可用，直接正常的val即可

将best.onnx模型放入netron中，onnx的输入和输出如下图1所示

图

const float INPUT_WIDTH = 640.0;
const float INPUT_HEIGHT = 640.0;
const float SCORE_THRESHOLD = 0.45;
const float NMS_THRESHOLD = 0.5;
const float CONFIDENCE_THRESHOLD = 0.25;