Yolo Test on Ubuntu 18.04

本文最后更新于：May 7, 2023 pm

[TOC]

YOLO

  • Darknet: Open Source Neural Networks in C

  • YOLO: Real-Time Object Detection

Build / Install

Detection Using A Pre-Trained Model

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./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

  • output 

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    layer     filters    size              input                output
        0 conv     32  3 x 3 / 1   608 x 608 x   3   ->   608 x 608 x  32  0.639 BFLOPs
        1 conv     64  3 x 3 / 2   608 x 608 x  32   ->   304 x 304 x  64  3.407 BFLOPs
        2 conv     32  1 x 1 / 1   304 x 304 x  64   ->   304 x 304 x  32  0.379 BFLOPs
        3 conv     64  3 x 3 / 1   304 x 304 x  32   ->   304 x 304 x  64  3.407 BFLOPs
        4 res    1                 304 x 304 x  64   ->   304 x 304 x  64
        5 conv    128  3 x 3 / 2   304 x 304 x  64   ->   152 x 152 x 128  3.407 BFLOPs
        6 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64  0.379 BFLOPs
        7 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128  3.407 BFLOPs
        8 res    5                 152 x 152 x 128   ->   152 x 152 x 128
        9 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64  0.379 BFLOPs
      10 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128  3.407 BFLOPs
      11 res    8                 152 x 152 x 128   ->   152 x 152 x 128
      12 conv    256  3 x 3 / 2   152 x 152 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      13 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      14 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      15 res   12                  76 x  76 x 256   ->    76 x  76 x 256
      16 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      17 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      18 res   15                  76 x  76 x 256   ->    76 x  76 x 256
      19 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      20 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      21 res   18                  76 x  76 x 256   ->    76 x  76 x 256
      22 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      23 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      24 res   21                  76 x  76 x 256   ->    76 x  76 x 256
      25 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      26 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      27 res   24                  76 x  76 x 256   ->    76 x  76 x 256
      28 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      29 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      30 res   27                  76 x  76 x 256   ->    76 x  76 x 256
      31 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      32 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      33 res   30                  76 x  76 x 256   ->    76 x  76 x 256
      34 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      35 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      36 res   33                  76 x  76 x 256   ->    76 x  76 x 256
      37 conv    512  3 x 3 / 2    76 x  76 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      38 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
      39 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      40 res   37                  38 x  38 x 512   ->    38 x  38 x 512
      41 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
      42 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      43 res   40                  38 x  38 x 512   ->    38 x  38 x 512
      44 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
      45 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      46 res   43                  38 x  38 x 512   ->    38 x  38 x 512
      47 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
      48 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      49 res   46                  38 x  38 x 512   ->    38 x  38 x 512
      50 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
      51 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      52 res   49                  38 x  38 x 512   ->    38 x  38 x 512
      53 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
      54 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      55 res   52                  38 x  38 x 512   ->    38 x  38 x 512
      56 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
      57 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      58 res   55                  38 x  38 x 512   ->    38 x  38 x 512
      59 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
      60 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      61 res   58                  38 x  38 x 512   ->    38 x  38 x 512
      62 conv   1024  3 x 3 / 2    38 x  38 x 512   ->    19 x  19 x1024  3.407 BFLOPs
      63 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
      64 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
      65 res   62                  19 x  19 x1024   ->    19 x  19 x1024
      66 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
      67 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
      68 res   65                  19 x  19 x1024   ->    19 x  19 x1024
      69 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
      70 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
      71 res   68                  19 x  19 x1024   ->    19 x  19 x1024
      72 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
      73 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
      74 res   71                  19 x  19 x1024   ->    19 x  19 x1024
      75 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
      76 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
      77 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
      78 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
      79 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
      80 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
      81 conv    255  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 255  0.189 BFLOPs
      82 yolo
      83 route  79
      84 conv    256  1 x 1 / 1    19 x  19 x 512   ->    19 x  19 x 256  0.095 BFLOPs
      85 upsample            2x    19 x  19 x 256   ->    38 x  38 x 256
      86 route  85 61
      87 conv    256  1 x 1 / 1    38 x  38 x 768   ->    38 x  38 x 256  0.568 BFLOPs
      88 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      89 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
      90 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      91 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
      92 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
      93 conv    255  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 255  0.377 BFLOPs
      94 yolo
      95 route  91
      96 conv    128  1 x 1 / 1    38 x  38 x 256   ->    38 x  38 x 128  0.095 BFLOPs
      97 upsample            2x    38 x  38 x 128   ->    76 x  76 x 128
      98 route  97 36
      99 conv    128  1 x 1 / 1    76 x  76 x 384   ->    76 x  76 x 128  0.568 BFLOPs
      100 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      101 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      102 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      103 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      104 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      105 conv    255  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 255  0.754 BFLOPs
      106 yolo
    Loading weights from yolov3.weights...Done!
    data/dog.jpg: Predicted in 18.148226 seconds.
    dog: 100%
    truck: 92%
    bicycle: 99%

  • fixed CUDA Error with the cfg code below

    out of memory

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    # cfg/yolov3.cfg
    batch=16

配置文件 yolov3.cfg 内容如下

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[net]
batch=64                           # 每batch个样本更新一次参数。
subdivisions=8                     # 如果内存不够大，将batch分割为subdivisions个子batch，每个子batch的大小为batch/subdivisions。
                                   # 在darknet代码中，会将batch/subdivisions命名为batch。
height=416                         # input图像的高
width=416                          # Input图像的宽
channels=3                         # Input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
angle=0                            # 通过旋转角度来生成更多训练样本
saturation = 1.5                   # 通过调整饱和度来生成更多训练样本
exposure = 1.5                     # 通过调整曝光量来生成更多训练样本
hue=.1                             # 通过调整色调来生成更多训练样本

learning_rate=0.0001               # 初始学习率
max_batches = 45000                # 训练达到max_batches后停止学习
policy=steps                       # 调整学习率的policy，有如下policy：CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM
steps=100,25000,35000              # 根据batch_num调整学习率
scales=10,.1,.1                    # 学习率变化的比例，累计相乘

[convolutional]
batch_normalize=1                  # 是否做BN
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                   # 激活函数：
                                   # logistic，loggy，relu，elu，relie，plse，hardtan，lhtan，linear，ramp，leaky，tanh，stair

[maxpool]
size=2                             # 池化层尺寸
stride=2                           # 池化步进

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

......
......

#######

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[route]                            # the route layer is to bring finer grained features in from earlier in the network
layers=-9

[reorg]                            # the reorg layer is to make these features match the feature map size at the later layer. 
                                   # The end feature map is 13x13, the feature map from earlier is 26x26x512. 
                                   # The reorg layer maps the 26x26x512 feature map onto a 13x13x2048 feature map 
                                   # so that it can be concatenated with the feature maps at 13x13 resolution.
stride=2

[route]
layers=-1,-3

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[convolutional]
size=1
stride=1
pad=1
filters=125                        # region前最后一个卷积层的filters数是特定的，计算公式为filter=num*(classes+5) 
                                   # 5的意义是5个坐标，论文中的tx,ty,tw,th,to
activation=linear

[region]
anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52          # 预选框，可以手工挑选，
                                                                                # 也可以通过k means 从训练样本中学出
bias_match=1
classes=20                         # 网络需要识别的物体种类数
coords=4                           # 每个box的4个坐标tx,ty,tw,th
num=5                              # 每个grid cell预测几个box,和anchors的数量一致。当想要使用更多anchors时需要调大num，且如果调大num后训练时Obj趋近0的话可以尝试调大object_scale
softmax=1                          # 使用softmax做激活函数
jitter=.2                          # 通过抖动增加噪声来抑制过拟合
rescore=1                          # 暂理解为一个开关，非0时通过重打分来调整l.delta（预测值与真实值的差）

object_scale=5                     # 栅格中有物体时，bbox的confidence loss对总loss计算贡献的权重
noobject_scale=1                   # 栅格中没有物体时，bbox的confidence loss对总loss计算贡献的权重
class_scale=1                      # 类别loss对总loss计算贡献的权重                      
coord_scale=1                      # bbox坐标预测loss对总loss计算贡献的权重

absolute=1
thresh = .6
random=0                           # random为1时会启用Multi-Scale Training，随机使用不同尺寸的图片进行训练。

Training YOLO on VOC

Yolo for ROS

  • https://github.com/leggedrobotics/darknet_ros

  • YOLO ROS: Real-Time Object Detection for ROS

cfg files

  • darknet_ros.launch

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    <!-- darknet_ros.launch -->

    <!-- ROS and network parameter files -->
    <arg name="ros_param_file"             default="$(find darknet_ros)/config/ros.yaml"/>
    <arg name="network_param_file"         default="$(find darknet_ros)/config/yolov3.yaml"/>

  • ros.yaml

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    # ros.yaml
    subscribers:
      camera_reading:
        topic: /camera/zed/rgb/image_rect_color
        queue_size: 1

Build

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cd ws_yolo/src
git clone --recursive git@github.com:leggedrobotics/darknet_ros.git
cd ../

catkin_make -DCMAKE_BUILD_TYPE=Release
# or
catkin build darknet_ros -DCMAKE_BUILD_TYPE=Release
ML
#Machine Learning #Deep Learning #Object Detection #Yolo
Yolo Test on Ubuntu 18.04
https://cgabc.xyz/posts/65ad6508/
Author
Gavin Gao
Posted on
June 25, 2022
Licensed under