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YOLOv1学习：（一）网络结构推导与实现 YOLOv1学习：（一）网络结构推导与实现原论文网络结构知乎看到的网络结构分析（见参考资料1）二次网络结构分析7*7*30输出解释实际操作如图所示，分为7*7个小格子，每个格子预测两个bounding box。如果一个目标的中心落入一个网格单元中，该网格单元负责检测 该目标。对每一个切割的小单元格预测（置信度，边界框的位置），每个bounding box需要4个数值来表示其位置，(Center_x,Center_y,width,height)，即(bounding box的中心点的x坐标，y坐标，bounding box的宽度，高度)置信度定义为该区域内是否包含物体的概率，打标签的时候，正样本（与真实物体有最大IOU的边框设为正样本）置信度真值为1，负样本为0.还要得到分类的概率结果；20个分类每个类别的概率。7*7*30中的30=（20类概率+2*5（置信度，边框位置））Pytorch实现网络结构基本骨架import torch import torch.nn as nn feature = nn.Sequential( nn.Conv2d(in_channels=3,out_channels=64,kernel_size=7,stride=2,padding=3), nn.MaxPool2d(kernel_size=2,stride=2), nn.Conv2d(in_channels=64,out_channels=192,kernel_size=3,stride=1,padding=1), nn.MaxPool2d(kernel_size=2,stride=2), nn.Conv2d(in_channels=192,out_channels=128,kernel_size=1,stride=1,padding=0), nn.Conv2d(in_channels=128,out_channels=256,kernel_size=3,stride=1,padding=1), nn.Conv2d(in_channels=256,out_channels=256,kernel_size=1,stride=1,padding=0), nn.Conv2d(in_channels=256,out_channels=512,kernel_size=3,stride=1,padding=1), nn.MaxPool2d(kernel_size=2,stride=2), nn.Conv2d(in_channels=512,out_channels=256,kernel_size=1,stride=1,padding=0), nn.Conv2d(in_channels=256,out_channels=512,kernel_size=3,stride=1,padding=1), nn.Conv2d(in_channels=512,out_channels=256,kernel_size=1,stride=1,padding=0), nn.Conv2d(in_channels=256,out_channels=512,kernel_size=3,stride=1,padding=1), nn.Conv2d(in_channels=512,out_channels=256,kernel_size=1,stride=1,padding=0), nn.Conv2d(in_channels=256,out_channels=512,kernel_size=3,stride=1,padding=1), nn.Conv2d(in_channels=512,out_channels=256,kernel_size=1,stride=1,padding=0), nn.Conv2d(in_channels=256,out_channels=512,kernel_size=3,stride=1,padding=1), nn.Conv2d(in_channels=512,out_channels=512,kernel_size=1,stride=1,padding=0), nn.Conv2d(in_channels=512,out_channels=1024,kernel_size=3,stride=1,padding=1), nn.MaxPool2d(kernel_size=2,stride=2), nn.Conv2d(in_channels=1024,out_channels=512,kernel_size=1,stride=1,padding=0), nn.Conv2d(in_channels=512,out_channels=1024,kernel_size=3,stride=1,padding=1), nn.Conv2d(in_channels=1024,out_channels=512,kernel_size=1,stride=1,padding=0), nn.Conv2d(in_channels=512,out_channels=1024,kernel_size=3,stride=1,padding=1), nn.Conv2d(in_channels=1024,out_channels=1024,kernel_size=3,stride=1,padding=1), nn.Conv2d(in_channels=1024,out_channels=1024,kernel_size=3,stride=2,padding=1), nn.Conv2d(in_channels=1024,out_channels=1024,kernel_size=3,stride=1,padding=1), nn.Conv2d(in_channels=1024,out_channels=1024,kernel_size=3,stride=1,padding=1), ) classify = nn.Sequential( nn.Flatten(), nn.Linear(1024 * 7 * 7, 4096), nn.Linear(4096, 1470) #1470=7*7*30 ) yolov1 = nn.Sequential( feature, classify )基本骨架-结构打印Sequential( (0): Sequential( (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)) (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (2): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (4): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1)) (5): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (6): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (7): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (8): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (9): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) (10): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (11): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) (12): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (13): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) (14): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (15): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) (16): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (17): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) (18): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (19): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (20): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1)) (21): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (22): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1)) (23): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (24): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (25): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (26): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (27): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (1): Sequential( (0): Flatten() (1): Linear(in_features=50176, out_features=4096, bias=True) (2): Linear(in_features=4096, out_features=1470, bias=True) ) )加入损失函数和Dropoutimport torch import torch.nn as nn feature = nn.Sequential( nn.Conv2d(in_channels=3,out_channels=64,kernel_size=7,stride=2,padding=3), nn.LeakyReLU(), nn.MaxPool2d(kernel_size=2,stride=2), nn.Conv2d(in_channels=64,out_channels=192,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), nn.MaxPool2d(kernel_size=2,stride=2), nn.Conv2d(in_channels=192,out_channels=128,kernel_size=1,stride=1,padding=0), nn.LeakyReLU(), nn.Conv2d(in_channels=128,out_channels=256,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), nn.Conv2d(in_channels=256,out_channels=256,kernel_size=1,stride=1,padding=0), nn.LeakyReLU(), nn.Conv2d(in_channels=256,out_channels=512,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), nn.MaxPool2d(kernel_size=2,stride=2), nn.Conv2d(in_channels=512,out_channels=256,kernel_size=1,stride=1,padding=0), nn.LeakyReLU(), nn.Conv2d(in_channels=256,out_channels=512,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), nn.Conv2d(in_channels=512,out_channels=256,kernel_size=1,stride=1,padding=0), nn.LeakyReLU(), nn.Conv2d(in_channels=256,out_channels=512,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), nn.Conv2d(in_channels=512,out_channels=256,kernel_size=1,stride=1,padding=0), nn.LeakyReLU(), nn.Conv2d(in_channels=256,out_channels=512,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), nn.Conv2d(in_channels=512,out_channels=256,kernel_size=1,stride=1,padding=0), nn.LeakyReLU(), nn.Conv2d(in_channels=256,out_channels=512,kernel_size=3,stride=1,padding=1), nn.Conv2d(in_channels=512,out_channels=512,kernel_size=1,stride=1,padding=0), nn.LeakyReLU(), nn.Conv2d(in_channels=512,out_channels=1024,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), nn.MaxPool2d(kernel_size=2,stride=2), nn.Conv2d(in_channels=1024,out_channels=512,kernel_size=1,stride=1,padding=0), nn.LeakyReLU(), nn.Conv2d(in_channels=512,out_channels=1024,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), nn.Conv2d(in_channels=1024,out_channels=512,kernel_size=1,stride=1,padding=0), nn.LeakyReLU(), nn.Conv2d(in_channels=512,out_channels=1024,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), nn.Conv2d(in_channels=1024,out_channels=1024,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), nn.Conv2d(in_channels=1024,out_channels=1024,kernel_size=3,stride=2,padding=1), nn.LeakyReLU(), nn.Conv2d(in_channels=1024,out_channels=1024,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), nn.Conv2d(in_channels=1024,out_channels=1024,kernel_size=3,stride=1,padding=1), nn.LeakyReLU(), ) classify = nn.Sequential( nn.Flatten(), nn.Linear(1024 * 7 * 7, 4096), nn.Dropout(0.5), nn.Linear(4096, 1470) #1470=7*7*30 ) yolov1 = nn.Sequential( feature, classify ) print(yolov1)参考资料YOLO V1 网络结构分析：https://zhuanlan.zhihu.com/p/220062200?utm_source=wechat_sessionYOLOv1算法理解:https://www.cnblogs.com/ywheunji/p/10808989.html -
ubuntu编译安装谷歌tesseract-ocr 安装依赖-leptonica库下载源码git clone https://github.com/DanBloomberg/leptonica.gitconfiguresudo apt install automake bash autogen.sh ./configure编译安装make sudo make install这样就安装好了leptonica库谷歌tesseract-ocr编译安装下载源码git clone https://github.com/tesseract-ocr/tesseract.git tesseract-ocr安装依赖sudo apt-get install g++ autoconf automake libtool autoconf-archive pkg-config libpng12-dev libjpeg8-dev libtiff5-dev zlib1g-dev libleptonica-dev -y安装训练所需要的库(只是调用可以不用安装)sudo apt-get install libicu-dev libpango1.0-dev libcairo2-devconfigurebash autogen.sh ./configure编译安装make sudo make install # 可选项，不训练可以选择不执行下面两条 make training sudo make training-install sudo ldconfig安装对应的字体库并添加对应的环境变量下载好的语言包 放在/usr/local/share/tessdata目录里面。语言包地址：https://github.com/tesseract-ocr/tessdata_best。里面有各种语言包，都是训练好的语言包。简体中文下载：chi_sim.traineddata ， chi_sim_vert.traineddata英文包：eng.traineddata。设置环境变量vim ~/.bashrc # 在.bashrc的文件末尾加入以下内容 export TESSDATA_PREFIX=/usr/local/share/tessdata source ~/.bashrc查看字体库tesseract --list-langs使用tesseract-ocr测试# 识别/home/app/1.png这张图片，内容输出到output.txt 里面，用chi_sim 中文来识别（不用加.traineddata，会默认加） tesseract /home/app/1.png output -l chi_sim # 查看识别结果 cat output.txt
