随笔分类 - yolov
摘要：工控机配置日志 done # # To activate this environment, use # # $ conda activate wind_2022 # # To deactivate an active environment, use # # $ conda deactivate
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摘要：yolov5训练警告反光标检测模型日志 1、标注数据 2、整理数据 3、训练：修改：myvoc.yaml myvoc.yaml train: VOC_2022052001/train.txt val: VOC_2022052001/val.txt # number of classes nc: 1
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摘要：训练日志 Epoch gpu_mem box obj cls total targets img_size 348/359 5.11G 0.01274 0.009867 0 0.02261 70 640: 100%|██████████████████████████████████████████
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摘要：两块Tesla P100训练yolov5时，batch size大小测试 #300 epochs completed in 4.716 hours. batch-size 2 epochs 300 yolov5m.yaml device 0,1 #300 epochs completed in 2.
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摘要：检测日志 (array('d', [385.0, 248.0, 40.0, 182.0, 385.0]), array('d', [171.0, 109.0, 172.0, 244.0, 171.0])) 385 171 248 109 248 109 40 172 40 172 182 244 1
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摘要：给输出框编号 rect_id = 1 for *xyxy, conf, cls in reversed(det): label = f'{names[int(cls)]} {rect_id}' plot_one_box(xyxy, im0, label=label, color=colors[int
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摘要：yolov5训练安全帽检测模型 1、标注数据 2、整理数据 3、训练：修改：myvoc.yaml myvoc.yaml train: VOC_2022050201/train.txt val: VOC_2022050201/val.txt # number of classes nc: 1 # cl
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摘要：yolov5训练分割钢筋区域的模型 使用yolov5训练分割钢筋区域的模型，用于yolov5识别钢筋并计数(yolov5钢筋计数) 1、标注数据 2、整理数据 3、训练：修改：myvoc.yaml myvoc.yaml train: VOC_2022050101/train.txt val: VOC
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摘要：使用yolov5训练识别钢筋的模型，用于yolov5识别钢筋并计数(yolov5钢筋计数) 1、标注数据 2、整理数据 3、训练：修改：myvoc.yaml myvoc.yaml train: VOC_2022042401/train.txt val: VOC_2022042401/val.txt
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摘要：训练yolov5识别小黄球模型 1、标注数据 2、整理数据 3、训练：修改：myvoc.yaml myvoc.yaml train: VOC_2022042301/train.txt val: VOC_2022042301/val.txt # number of classes nc: 1 # cl
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摘要：plot_pr_curve2 def plot_pr_curve2(px, py, conf_p,conf_r,ap, save_dir='.', names=()): fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) p
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摘要：yolov5训练模型日志 早晨9点多开始训练，刚刚终于训练完了！ python train_20220110.py --img-size 640 --batch-size 1 --epochs 300 --data ./data/myvoc.yaml --cfg ./models/yolov5m.y
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摘要：realsense测量2 import pyrealsense2 as rs pipeline = rs.pipeline() config = rs.config() config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30
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摘要：pytorch yolov5运行日志 wind_2021) F:\PytorchProject\Yolov5_DeepSort_Pytorch-master\yolov5> (wind_2021) F:\PytorchProject\Yolov5_DeepSort_Pytorch-master\yo
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摘要：yolov5l.pt89.2 MB yolov5l6.pt147 MB yolov5m.pt40.7 MB yolov5m6.pt68.7 MB yolov5m_Objects365.pt43 MB yolov5n.pt3.77 MB yolov5n6.pt6.56 MB yolov5s.pt14
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摘要：yolov5单图片检测 import argparse import time from pathlib import Path import cv2 import torch import torch.backends.cudnn as cudnn from numpy import random
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摘要：RuntimeError: CUDA out of memory. Tried to allocate 4.29 GiB (GPU 0; 16.00 GiB total capacity; 504.24 MiB already allocated; 13.20 GiB free; 758.00 Mi
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摘要：yolov5数据集制作 F:\PytorchProject\yolov5-master-20210402 python train_2021041901.py --data coco_zy.yaml --cfg models/yolov5s.yaml --weights '' --batch-siz
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摘要：yolov5测试 import argparse import time from pathlib import Path import cv2 import torch import torch.backends.cudnn as cudnn from numpy import random im
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摘要：pyqt5 yolov5运行日志 img = img.half() img = img.float()
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