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  • Darknet19的实现

    千次阅读 2020-04-24 17:03:53
    环境设置:tensorflow2.1...#darknet19细节 import tensorflow as tf from tensorflow.keras.layers import Dense,Flatten,Conv2D,MaxPooling2D,ZeroPadding2D,UpSampling2D from tensorflow.keras.layers import In...

    环境设置:tensorflow2.1

    代码如下:

    #darknet19细节
    import tensorflow as tf
    from tensorflow.keras.layers import Dense,Flatten,Conv2D,MaxPooling2D,ZeroPadding2D,UpSampling2D
    from tensorflow.keras.layers import Input,AveragePooling2D,Activation
    from tensorflow.keras import Model
    inputs=Input([256,256,3])
    """
    #前面两层卷积的结构函数
    """
    def conv2d_1(filters,inputs):
        x=Conv2D(filters,(3,3),strides=1,padding='same',activation='relu')(inputs)
        x=MaxPooling2D()(x)
        return x
    
    def conv2d_2(filters,inputs):
        filter1,filter2=filters
        x=Conv2D(filter1,(3,3),padding='same',activation='relu')(inputs)
        x=Conv2D(filter2,1,padding='same',activation='relu')(x)
        x=Conv2D(filter1,3,padding='same',activation='relu')(x)
        x=MaxPooling2D()(x)
        return x
    def conv2d_3(filters,inputs):
        filter1,filter2=filters
        x=Conv2D(filter1,(3,3),padding='same',activation='relu')(inputs)
        x=Conv2D(filter2,(1,1),padding='same',activation='relu')(x)
        x=Conv2D(filter1,(3,3),padding='same',activation='relu')(x)
        x=Conv2D(filter2,(1,1),padding='same',activation='relu')(x) 
        x=Conv2D(filter1,(3,3),padding='same',activation='relu')(x)
        return x
    
        
        
    
    x=conv2d_1(32,inputs)
    x=conv2d_1(64,x)
    
    x=conv2d_2([128,64],x)
    x=conv2d_2([256,128],x)
    x=conv2d_3([512,256],x)
    x=MaxPooling2D()(x)
    x=conv2d_3([1024,512],x)
    x=Conv2D(1000,1,padding='same',activation='relu')(x)
    x=AveragePooling2D(pool_size=(8,8))(x)
    x=Flatten()(x)
    x=Activation('softmax')(x)
    model=Model(inputs,x)
    model.summary()

    可以通过model.summary()查看网络结构

    #darknet19结构图
    """
    Model: "model"
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    input_1 (InputLayer)         [(None, 256, 256, 3)]     0         
    _________________________________________________________________
    conv2d (Conv2D)              (None, 256, 256, 32)      896       
    _________________________________________________________________
    max_pooling2d (MaxPooling2D) (None, 128, 128, 32)      0         
    _________________________________________________________________
    conv2d_1 (Conv2D)            (None, 128, 128, 64)      18496     
    _________________________________________________________________
    max_pooling2d_1 (MaxPooling2 (None, 64, 64, 64)        0         
    _________________________________________________________________
    conv2d_2 (Conv2D)            (None, 64, 64, 128)       73856     
    _________________________________________________________________
    conv2d_3 (Conv2D)            (None, 64, 64, 64)        8256      
    _________________________________________________________________
    conv2d_4 (Conv2D)            (None, 64, 64, 128)       73856     
    _________________________________________________________________
    max_pooling2d_2 (MaxPooling2 (None, 32, 32, 128)       0         
    _________________________________________________________________
    conv2d_5 (Conv2D)            (None, 32, 32, 256)       295168    
    _________________________________________________________________
    conv2d_6 (Conv2D)            (None, 32, 32, 128)       32896     
    _________________________________________________________________
    conv2d_7 (Conv2D)            (None, 32, 32, 256)       295168    
    _________________________________________________________________
    max_pooling2d_3 (MaxPooling2 (None, 16, 16, 256)       0         
    _________________________________________________________________
    conv2d_8 (Conv2D)            (None, 16, 16, 512)       1180160   
    _________________________________________________________________
    conv2d_9 (Conv2D)            (None, 16, 16, 256)       131328    
    _________________________________________________________________
    conv2d_10 (Conv2D)           (None, 16, 16, 512)       1180160   
    _________________________________________________________________
    conv2d_11 (Conv2D)           (None, 16, 16, 256)       131328    
    _________________________________________________________________
    conv2d_12 (Conv2D)           (None, 16, 16, 512)       1180160   
    _________________________________________________________________
    max_pooling2d_4 (MaxPooling2 (None, 8, 8, 512)         0         
    _________________________________________________________________
    conv2d_13 (Conv2D)           (None, 8, 8, 1024)        4719616   
    _________________________________________________________________
    conv2d_14 (Conv2D)           (None, 8, 8, 512)         524800    
    _________________________________________________________________
    conv2d_15 (Conv2D)           (None, 8, 8, 1024)        4719616   
    _________________________________________________________________
    conv2d_16 (Conv2D)           (None, 8, 8, 512)         524800    
    _________________________________________________________________
    conv2d_17 (Conv2D)           (None, 8, 8, 1024)        4719616   
    _________________________________________________________________
    conv2d_18 (Conv2D)           (None, 8, 8, 1000)        1025000   
    _________________________________________________________________
    average_pooling2d (AveragePo (None, 1, 1, 1000)        0         
    _________________________________________________________________
    flatten (Flatten)            (None, 1000)              0         
    _________________________________________________________________
    activation (Activation)      (None, 1000)              0         
    =================================================================
    Total params: 20,835,176
    Trainable params: 20,835,176
    Non-trainable params: 0
    """

     

    展开全文
  • yolov2预训练权重darknet19.weights,可以不通过外网下载,直接在百度网盘提取下载即可
  • darknet 19 模型文件

    2018-12-02 12:38:40
    darknet是一个较为轻型的完全基于C与CUDA的开源深度学习框架,其主要特点就是容易安装,没有任何依赖项(OpenCV都可以不用),移植性非常好,支持CPU与GPU两种计算方式。Darknet的优势: darknet完全由C语言实现,...
  • Darknet: Open Source Neural Networks in C - Classifying With darknet19.weights Models Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and ...

    Darknet: Open Source Neural Networks in C - Classifying With darknet19.weights Models

    Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. You can find the source on GitHub or you can read more about what Darknet can do right here:
    https://github.com/pjreddie/darknet

    1. ImageNet Classification

    https://pjreddie.com/darknet/imagenet/

    Classify images with popular models like ResNet and ResNeXt.
    You can use Darknet to classify images for the 1000-class ImageNet challenge. If you haven’t installed Darknet yet, you should do that first.
    http://image-net.org/challenges/LSVRC/2015/index
    https://pjreddie.com/darknet/install/

    1.1 Classifying With Pre-Trained Models

    Here are the commands to install Darknet, download a classification weights file, and run a classifier on an image:

    git clone https://github.com/pjreddie/darknet.git
    cd darknet
    make
    wget https://pjreddie.com/media/files/darknet19.weights
    ./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights data/dog.jpg
    
    strong@foreverstrong:~/eclipse-darknet/darknet_models$ wget https://pjreddie.com/media/files/darknet19.weights
    --2018-11-07 19:51:53--  https://pjreddie.com/media/files/darknet19.weights
    Resolving pjreddie.com (pjreddie.com)... 128.208.3.39
    Connecting to pjreddie.com (pjreddie.com)|128.208.3.39|:443... connected.
    HTTP request sent, awaiting response... 200 OK
    Length: 83427120 (80M) [application/octet-stream]
    Saving to: ‘darknet19.weights’
    
    darknet19.weights            100%[===========================================>]  79.56M  3.49MB/s    in 26s     
    
    2018-11-07 19:52:20 (3.08 MB/s) - ‘darknet19.weights’ saved [83427120/83427120]
    
    strong@foreverstrong:~/eclipse-darknet/darknet_models$ 
    

    This example uses the Darknet19 model, you can read more about it below. After running this command you should see the following output:
    https://pjreddie.com/darknet/imagenet/#darknet19

    layer     filters    size              input                output
        0 conv     32  3 x 3 / 1   256 x 256 x   3   ->   256 x 256 x  32  0.113 BFLOPs
        1 max          2 x 2 / 2   256 x 256 x  32   ->   128 x 128 x  32
        2 conv     64  3 x 3 / 1   128 x 128 x  32   ->   128 x 128 x  64  0.604 BFLOPs
        3 max          2 x 2 / 2   128 x 128 x  64   ->    64 x  64 x  64
        4 conv    128  3 x 3 / 1    64 x  64 x  64   ->    64 x  64 x 128  0.604 BFLOPs
        5 conv     64  1 x 1 / 1    64 x  64 x 128   ->    64 x  64 x  64  0.067 BFLOPs
        6 conv    128  3 x 3 / 1    64 x  64 x  64   ->    64 x  64 x 128  0.604 BFLOPs
        7 max          2 x 2 / 2    64 x  64 x 128   ->    32 x  32 x 128
        8 conv    256  3 x 3 / 1    32 x  32 x 128   ->    32 x  32 x 256  0.604 BFLOPs
        9 conv    128  1 x 1 / 1    32 x  32 x 256   ->    32 x  32 x 128  0.067 BFLOPs
       10 conv    256  3 x 3 / 1    32 x  32 x 128   ->    32 x  32 x 256  0.604 BFLOPs
       11 max          2 x 2 / 2    32 x  32 x 256   ->    16 x  16 x 256
       12 conv    512  3 x 3 / 1    16 x  16 x 256   ->    16 x  16 x 512  0.604 BFLOPs
       13 conv    256  1 x 1 / 1    16 x  16 x 512   ->    16 x  16 x 256  0.067 BFLOPs
       14 conv    512  3 x 3 / 1    16 x  16 x 256   ->    16 x  16 x 512  0.604 BFLOPs
       15 conv    256  1 x 1 / 1    16 x  16 x 512   ->    16 x  16 x 256  0.067 BFLOPs
       16 conv    512  3 x 3 / 1    16 x  16 x 256   ->    16 x  16 x 512  0.604 BFLOPs
       17 max          2 x 2 / 2    16 x  16 x 512   ->     8 x   8 x 512
       18 conv   1024  3 x 3 / 1     8 x   8 x 512   ->     8 x   8 x1024  0.604 BFLOPs
       19 conv    512  1 x 1 / 1     8 x   8 x1024   ->     8 x   8 x 512  0.067 BFLOPs
       20 conv   1024  3 x 3 / 1     8 x   8 x 512   ->     8 x   8 x1024  0.604 BFLOPs
       21 conv    512  1 x 1 / 1     8 x   8 x1024   ->     8 x   8 x 512  0.067 BFLOPs
       22 conv   1024  3 x 3 / 1     8 x   8 x 512   ->     8 x   8 x1024  0.604 BFLOPs
       23 conv   1000  1 x 1 / 1     8 x   8 x1024   ->     8 x   8 x1000  0.131 BFLOPs
       24 avg                        8 x   8 x1000   ->  1000
       25 softmax                                        1000
    Loading weights from darknet19.weights...Done!
    data/dog.jpg: Predicted in 0.769246 seconds.
    42.55%: malamute
    22.93%: Eskimo dog
    12.51%: Siberian husky
     2.76%: bicycle-built-for-two
     1.20%: mountain bike
    
    malamute ['mæləmjuːt]:n. 北极狗,爱斯基摩狗
    Eskimo dog:爱斯基摩狗,北极雪橇狗
    Siberian husky:西伯利亚爱斯基摩狗
    mountain bike:n. 山地车,山地自行车
    

    Darknet displays information as it loads the config file and weights, then it classifies the image and prints the top-10 classes for the image. Kelp is a mixed breed dog but she has a lot of malamute in her so we’ll consider this a success!

    kelp [kelp]:n. 巨藻,海藻,海草灰 vi. 烧制海草灰
    

    You can also try with other images, like the bald eagle image:

    ./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights data/eagle.jpg
    

    Which produces:

    ...
    data/eagle.jpg: Predicted in 0.707070 seconds.
    84.68%: bald eagle
    11.91%: kite
     2.62%: vulture
     0.08%: great grey owl
     0.07%: hen
    
    bald eagle:秃鹰 (美国的国鸟),比喻秃头的政治家
    vulture ['vʌltʃə]:n. 秃鹰,秃鹫,贪婪的人
    owl [aʊl]:n. 猫头鹰,枭,惯于晚上活动的人
    hen [hen]:n. 母鸡,女人,雌禽
    

    Pretty good!

    If you don’t specify an image file you will be prompted at run-time for an image. This way you can classify multiple in a row without reloading the whole model. Use the command:

    ./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights
    

    Then you will get a prompt that looks like:

    ....
    25: Softmax Layer: 1000 inputs
    Loading weights from darknet19.weights...Done!
    Enter Image Path:
    

    Whenever you get bored of classifying images you can use Ctrl-C to exit the program.

    1. Makefile

    GPU=1
    CUDNN=1
    OPENCV=0
    OPENMP=0
    DEBUG=0
    

    2. Program Arguments
    right-click on the darknet_181107 -> Properties -> Run/Debug Settings -> New -> C/C++ Application -> OK

    在这里插入图片描述

    在这里插入图片描述

    在这里插入图片描述

    /home/strong/eclipse-darknet/darknet_models/darknet19.weights

    terminal

    ./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights data/dog.jpg
    

    argument

    classifier predict cfg/imagenet1k.data cfg/darknet19.cfg /home/strong/eclipse-darknet/darknet_models/darknet19.weights data/dog.jpg
    

    在这里插入图片描述

    在这里插入图片描述

    Run darknet_181107-darknet19
    right-click on the darknet_181107 -> Properties -> Run As -> Run Configurations…

    在这里插入图片描述

    Run

    在这里插入图片描述

    layer     filters    size              input                output
        0 conv     32  3 x 3 / 1   256 x 256 x   3   ->   256 x 256 x  32  0.113 BFLOPs
        1 max          2 x 2 / 2   256 x 256 x  32   ->   128 x 128 x  32
        2 conv     64  3 x 3 / 1   128 x 128 x  32   ->   128 x 128 x  64  0.604 BFLOPs
        3 max          2 x 2 / 2   128 x 128 x  64   ->    64 x  64 x  64
        4 conv    128  3 x 3 / 1    64 x  64 x  64   ->    64 x  64 x 128  0.604 BFLOPs
        5 conv     64  1 x 1 / 1    64 x  64 x 128   ->    64 x  64 x  64  0.067 BFLOPs
        6 conv    128  3 x 3 / 1    64 x  64 x  64   ->    64 x  64 x 128  0.604 BFLOPs
        7 max          2 x 2 / 2    64 x  64 x 128   ->    32 x  32 x 128
        8 conv    256  3 x 3 / 1    32 x  32 x 128   ->    32 x  32 x 256  0.604 BFLOPs
        9 conv    128  1 x 1 / 1    32 x  32 x 256   ->    32 x  32 x 128  0.067 BFLOPs
       10 conv    256  3 x 3 / 1    32 x  32 x 128   ->    32 x  32 x 256  0.604 BFLOPs
       11 max          2 x 2 / 2    32 x  32 x 256   ->    16 x  16 x 256
       12 conv    512  3 x 3 / 1    16 x  16 x 256   ->    16 x  16 x 512  0.604 BFLOPs
       13 conv    256  1 x 1 / 1    16 x  16 x 512   ->    16 x  16 x 256  0.067 BFLOPs
       14 conv    512  3 x 3 / 1    16 x  16 x 256   ->    16 x  16 x 512  0.604 BFLOPs
       15 conv    256  1 x 1 / 1    16 x  16 x 512   ->    16 x  16 x 256  0.067 BFLOPs
       16 conv    512  3 x 3 / 1    16 x  16 x 256   ->    16 x  16 x 512  0.604 BFLOPs
       17 max          2 x 2 / 2    16 x  16 x 512   ->     8 x   8 x 512
       18 conv   1024  3 x 3 / 1     8 x   8 x 512   ->     8 x   8 x1024  0.604 BFLOPs
       19 conv    512  1 x 1 / 1     8 x   8 x1024   ->     8 x   8 x 512  0.067 BFLOPs
       20 conv   1024  3 x 3 / 1     8 x   8 x 512   ->     8 x   8 x1024  0.604 BFLOPs
       21 conv    512  1 x 1 / 1     8 x   8 x1024   ->     8 x   8 x 512  0.067 BFLOPs
       22 conv   1024  3 x 3 / 1     8 x   8 x 512   ->     8 x   8 x1024  0.604 BFLOPs
       23 conv   1000  1 x 1 / 1     8 x   8 x1024   ->     8 x   8 x1000  0.131 BFLOPs
       24 avg                        8 x   8 x1000   ->  1000
       25 softmax                                        1000
    Loading weights from /home/strong/eclipse-darknet/darknet_models/darknet19.weights...Done!
    data/dog.jpg: Predicted in 0.005434 seconds.
    42.07%: malamute
    23.15%: Eskimo dog
    12.66%: Siberian husky
     2.79%: bicycle-built-for-two
     1.20%: mountain bike
    

    Wordbook

    you only look once,YOLO
    Visual Object Classes,VOC
    Pattern Analysis, Statistical Modelling and Computational Learning,PASCAL
    mean Average Precision,mAP:平均精度均值
    floating point operations per second,FLOPS
    frame rate or frame frequency, frames per second,FPS
    hertz,Hz
    billion,Bn
    operations,Ops
    configuration,cfg
    ImageNet Large Scale Visual Recognition Challenge,ILSVRC
    Microsoft Common Objects in Context,MS COCO

    展开全文
  • darknet19.mlpkginstall

    2020-11-03 12:43:51
    迁移学习(Transfer Learning):Matlab预训练模型的原始安装程序,用于特征提取、表达、目标识别等诸多任务
  • yolo.v2 darknet19结构

    千次阅读 2018-06-28 10:44:00
    Darknet19( (conv1s): Sequential( (0): Sequential( (0): Conv2d_BatchNorm( (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (b...
    Darknet19(
      (conv1s): Sequential(
        (0): Sequential(
          (0): Conv2d_BatchNorm(
            (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
        )
        (1): Sequential(
          (0): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)
          (1): Conv2d_BatchNorm(
            (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
        )
        (2): Sequential(
          (0): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)
          (1): Conv2d_BatchNorm(
            (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (2): Conv2d_BatchNorm(
            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (3): Conv2d_BatchNorm(
            (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
        )
        (3): Sequential(
          (0): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)
          (1): Conv2d_BatchNorm(
            (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (2): Conv2d_BatchNorm(
            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (3): Conv2d_BatchNorm(
            (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
        )
        (4): Sequential(
          (0): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)
          (1): Conv2d_BatchNorm(
            (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (2): Conv2d_BatchNorm(
            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (3): Conv2d_BatchNorm(
            (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (4): Conv2d_BatchNorm(
            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (5): Conv2d_BatchNorm(
            (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
        )
      )

    (conv2): Sequential( (0): MaxPool2d(kernel_size
    =(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False) (1): Conv2d_BatchNorm( (conv): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) (2): Conv2d_BatchNorm( (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) (3): Conv2d_BatchNorm( (conv): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) (4): Conv2d_BatchNorm( (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) (5): Conv2d_BatchNorm( (conv): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) )

    (conv3): Sequential( (0): Conv2d_BatchNorm( (conv): Conv2d(
    1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) (1): Conv2d_BatchNorm( (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) ) (reorg): ReorgLayer( )

    (conv4): Sequential( (0): Conv2d_BatchNorm( (conv): Conv2d(
    3072, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) )

    (conv5): Conv2d( (conv): Conv2d(
    1024, 125, kernel_size=(1, 1), stride=(1, 1)) )

    (global_average_pool): AvgPool2d(kernel_size
    =(1, 1), stride=(1, 1), padding=0, ceil_mode=False, count_include_pad=True) )

     

    转载于:https://www.cnblogs.com/buyizhiyou/p/9237527.html

    展开全文
  • darknet19-voc:在VOC数据集上训练的Darknet-19基本网络。 darknet19-coco:在COCO数据集上训练的Darknet-19基本网络。 tinyYOLOv2-coco:在COCO数据集上训练的较小的基础网络。 对经过训练的预训练网络进行检测以...
  • darknet 19_448 模型文件

    2018-12-02 12:40:34
    darknet是一个较为轻型的完全基于C与CUDA的开源深度学习框架,其主要特点就是容易安装,没有任何依赖项(OpenCV都可以不用),移植性非常好,支持CPU与GPU两种计算方式。Darknet的优势: darknet完全由C语言实现,...
  • darknet19的配置文件

    千次阅读 2018-08-31 10:34:00
    batch=128 subdivisions=1 height=224 width=224 channels=3//图像的通道数 momentum=0.9//动量 decay=0.0005、、权重衰减正则项,防止过拟合 max_crop=448 learning_rate=0.1、、初始学习率 policy=poly、、随着...
    [net]
    batch=128
    subdivisions=1
    height=224
    width=224
    channels=3//图像的通道数
    momentum=0.9//动量
    decay=0.0005、、权重衰减正则项,防止过拟合
    max_crop=448
    
    learning_rate=0.1、、初始学习率
    policy=poly、、随着迭代次数的增加不断调整
    power=4、、pow开方的次数
    max_batches=1600000、、最大迭代次数
    
    [convolutional]
    batch_normalize=1、、是否做bn处理
    filters=32、、输出多少个特征图
    size=3、、卷积核的尺寸
    stride=1、、做卷积运算的步长
    pad=1、、如果pad为0,padding由 padding参数指定。如果pad为1,padding大小为size/2
    activation=leaky
    
    [maxpool]
    size=2、、池化的尺寸
    stride=2、、池化的步长
    
    [convolutional]
    batch_normalize=1
    filters=64
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [maxpool]
    size=2
    stride=2
    
    [convolutional]
    batch_normalize=1
    filters=128
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=64
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=128
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [maxpool]
    size=2
    stride=2
    
    [convolutional]
    batch_normalize=1
    filters=256
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=128
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=256
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [maxpool]
    size=2
    stride=2
    
    [convolutional]
    batch_normalize=1
    filters=512
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=256
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=512
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=256
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=512
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [maxpool]
    size=2
    stride=2
    
    [convolutional]
    batch_normalize=1
    filters=1024
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=512
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=1024
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=512
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=1024
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    filters=1000
    size=1
    stride=1
    pad=1
    activation=linear
    
    [avgpool]
    
    [softmax]
    groups=1
    
    [cost]
    type=sse//拟合数据和真实数据对应的误差平方和
    
    
    展开全文
  • functools 函数 partial 函数 partial函数(偏函数) 可以将一个函数的参数设为默认值,返回一个新函数,使调用更加简单、方便 import functools as f def print_function(*args,**kwargs):#*args可以传递元组,**...
  • 第一: LeNet5 import tensorflow as tf from tensorflow import keras from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics model = Sequential() model.add( layers.Input( shape =...
  • darknet

    千次阅读 2019-06-08 21:46:16
    他是yolo v2中的特征提取器,因为它有19层卷积,所以又叫做darknet19。 它如下图为他的结构,包括19个卷积层和5个maxpooling层。Darknet-19与VGG16模型设计原则是一致的,主要采用3 * 3卷积,采用2 * 2的maxpooling...
  • Darknet-19 YOLO

    2020-11-21 21:46:40
    <p>Have you ever tried the darknet-19 version of yolov2? (original version in paper) When I replace the mobilenet into darknet-19, I can only get 55.5 mAP, which is even worse than yolov1...</p><p>该...
  • Darknet

    2020-09-17 11:00:39
    YOLO作者自己写的一个深度学习框架叫darknet(见YOLO原文2.2部分),后来在YOLO9000中又提了一个基于ResNet魔改的19层卷积网络,称为Darknet-19,在YOLOv3中又提了一个更深的Darknet-53。这两个都是用于提取特征的...
  • CV 经典主干网络 (Backbone) 系列: Darknet-19 作者:Joseph Redmon 发表时间:2016 Paper 原文: YOLO9000: Better, Faster, Stronger 该篇是 CV 经典主干网络 (Backbone) 系列 下的一篇文章。 1. 网络结构 Darknet...

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