增加yoloface_8n的onnx文件 main(xlite-dev/lite.ai.toolkit#412) 37bb7b8 87ec96c File tree examples/hub/onnx/cv yoloface_8n.onnx 1 file changed +0 -0 lines changed examples/hub/onnx/cv/yoloface_8n.onnx 12.1 MB Binary file not shown. ...
一、问题现象(附报错日志上下文):YOLOV8的face_n模型,onnx转om后精度下降,使用msit工具验证存在精度下降二、软件版本: CANN 版本 e.g., CANN 3.0.x,5...
先上效果图,如下如上图,这是yolov6 面部识别的效果图,识别的人脸及其对应的关键点(眼睛,鼻子,嘴) yolov6 face识别模型下载地址如下 yolov6n_face.onnx该模型输入输出结构如下该模型和yolov5 一样,都是单输…
std::string onnx_path ="../hub/onnx/cv/yolov5face-n-640x640.onnx";//yolov5n-facestd::string test_img_path ="../resources/4.jpg"; std::string save_img_path ="../logs/4.jpg";auto*yolov5face =newlite::cv::face::detect::YOLO5Face(onnx_path); std::vector<lite::types::B...
1.2 yolov5-face的onnx模型转换为rknn模型 由于rknntoolkit1.6.0在转换yolov-face的模型时报错,因此首先需要安装toolkit1.7.1,然后再用https://github.com/airockchip/yolov5/tree/master/rknn中的onnx2rknn.py进行模型转换,转换脚本如下。 1. import os 2. import sys 3. import numpy as np 4. from...
YOLOv9 Face 🚀 in PyTorch > ONNX > CoreML > TFLite. Contribute to akanametov/yolov9-face development by creating an account on GitHub.
print("CONVERT TO ONNX") onnxmodel, img, save, na, no = test_export(opt) onnxmodel[-1].export = True net = my_yolov5_model(onnxmodel, save, na, no).to(device) net.eval() f = opt.weights.replace('.pt', '.onnx') # filename input = torch.zeros(2, 3, ...
#include "lite/lite.h" int main(int argc, char *argv[]) { std::string onnx_path = "yolov5s.onnx"; std::string test_img_path = "test_yolov5.jpg"; std::string save_img_path = "test_results.jpg"; auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path); std::vector<...
#include "lite/lite.h" int main(int argc, char *argv[]) { std::string onnx_path = "yolov5s.onnx"; std::string test_img_path = "test_yolov5.jpg"; std::string save_img_path = "test_results.jpg"; auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path); std::vector<...
#include "lite/lite.h" int main(int argc, char *argv[]) { std::string onnx_path = "yolov5s.onnx"; std::string test_img_path = "test_yolov5.jpg"; std::string save_img_path = "test_results.jpg"; auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path); std::vector<...