YOLOv5s:Pre-process(处理输入)和Post-process(处理输出)。 经过这几步之后我们就能够得到一个能够应用于实际工程项目中的推理SDK。 源码链接: https://github.com/gesanqiu/SNPE_Tutorialgithub.com/gesanqiu/SNPE_Tutorial 视频教学: 基于高通SNPE推理引擎的yolov5目标检测算法——项目简介_哔哩哔哩_bilibili...
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) if save_txt or save_img: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels...
yolov5s.pt Speed: 1.0ms pre-process, 19.5ms inference, 1.5ms NMS per image at shape (1, 3, 1280, 1280) yolov5s.engine Speed: 270.5ms pre-process, 3.0ms inference, 2.0ms NMS per image at shape (1, 3, 1280, 1280) 可以看到,转成TensorRT之后,推理(inference)时间确实如某些资料所述...
import onnxruntime import cv2 import matplotlib.pyplot as plt import numpy as np from utils import pre_process, post_process, draw_boxes, draw_points, four_point_transform%matplotlib inlineproviders = [ 'cudaexecutionprovider' ] # onnx gpu推理需要onnxruntime-gpu # providers = ['cpuexecution...
编辑文件prebuild.sh,注释掉除yolov3-tiny的语句 执行: ./prebuild.sh 1. 下载yolov3-tiny.cfg和yolov3-tiny.weights 执行命令 deepstream-app -c deepstream_app_config_yoloV3_tiny.txt 1. yolov5项目克隆和安装(电脑上完成!) 1)安装Anaconda和PyTorch ...
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) if save_txt or save_img: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' ...
Speed: 0.2ms pre-process, 4.7ms inference, 3.9ms NMS per image at shape (12, 3, 640, 640) # s # python val.py --data ./data/coco_chv.yaml --weights runs/train/base_s/weights/best.pt --batch-size 12 # 15.8 GFLOPs Class Images Labels P R mAP@.5 mAP@.5:.95 ...
Processor.mOutputColumn=num_class+5;classes.toArray(PrePostProcessor.mClasses);}catch(IOException e){Log.e("Object Detection","Error reading assets",e);finish();}}});builder.show();} 这里选择的模型数量添加if分支,model_class为模型对应的类别标签,需要仿照classes.txt单独创建,num_class为类别...
(0.021s) Speed: 0.9ms pre-process, 21.2ms inference, 1.3ms NMS per image at shape (1, 3, 640, 640) Results saved to runs/detect/exp2 视频推理In [3] %cd /home/aistudio/YOLOv5-Paddle !python detect.py \ --weights /home/aistudio/YOLOv5-Paddle/runs/train/exp/weights/best....
3. prerun_graph 预运行,准备计算图推理所需资源。设置大小核,核个数、核亲和性、数据精度都在这里。 structoptions { intnum_thread;//核个数设置, intcluster;//大小核设置,可选TENGINE_CLUSTER_[ALL,BIG,MEDIUM,LITTLE] intprecision;//精度设置,TENGINE_MODE_[FP32,FP16,HYBRID_INT8,UINT8,INT8] ...