YOLOv5:指定类别进行评估验证 背景:在特定场景下,只想关注特定类别的效果,即可指定类别进行评估验证。 目录结构示例 代码实现 主要修改官方代码utils/datasets.py中552行的include_class变量。 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license """ Dataloaders and dataset utils """ import glob import hashlib i...
classes=['class1','class2','class3']# 这里修改为自己的类别 cls_path='runs/predict-cls/exp2'labels_path=Path('cls_result')value_threshold=0.5# 阈值,概率大于该阈值才会进行显示if__name__=='__main__':iflabels_path.exists():shutil.rmtree(labels_path)os.mkdir(labels_path)foriinclasses:d...
classYOLOv5Exporter:def__init__(self,weights,include):self.weights=weightsself.include=includeself.FILE=Path(__file__).resolve()self.ROOT=self.FILE.parents[0]# YOLOv5 root directoryifstr(self.ROOT)notinsys.path:sys.path.append(str(self.ROOT))# add ROOT to PATHself.ROOT=Path(os.path.rel...
# 参考 # coding=utf-8 import cv2 import numpy as np import onnxruntime import time import random from yolov5_onnx.general import non_max_suppression, numpy_cpu_nms class YOLOV5_ONNX(object): def __init__(self,onnx_path = "yolov5_onnx/yolov5n.onnx", classes = []): ''' :par...
#include"yolov5_dnn.h" using namespace cv; //初始化网络,classname void YOLOv5Detector::initConfig(std::string onnxpath, string classpath, int iw, int ih, float threshold) { this->input_w = iw; this->input_h = ih; this->threshold_score = threshold; this->net = cv::dnn::read...
该头文件内容主要来自于 ai benchmark 的 code/include/base/perception_common.h 头文件,包含了对 argmax 和计时功能的定义,以及目标检测任务相关结构体的定义。 #include typedef std::chrono::steady_clock::time_point Time; typedef std::chrono::duration Micro; ...
size=1, device=cpu, half=False, inplace=False, train=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['...
#include "opencv2/opencv.hpp" using namespace std; using namespace cv; static ncnn::UnlockedPoolAllocator g_blob_pool_allocator; static ncnn::PoolAllocator g_workspace_pool_allocator; static ncnn::Net yolov5; class YoloV5Focus : public ncnn::Layer ...
修改include文件中的头文件postprocess.h #defineOBJ_CLASS_NUM 2 #这里的数字修改为数据集的类的个数 修改model目录下的coco_80_labels_list.txt文件, 改为自己的类并保存 file dir 修改build.sh GCC_COMPILER=自己设备的交叉编译器的路径/bin/arm-linux-gnueabihf ...
#include<opencv2/opencv.hpp>struct DetectResult{int classId;float score;cv::Rect box;};classYOLOv5Detector{public:voidinitConfig(std::string onnxpath,int iw,int ih,float threshold);voiddetect(cv::Mat&frame,std::vector<DetectResult>&result);private:int input_w=640;int input_h=640;cv::dn...