3 篇文章带有标签 “深度学习”

FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04
LABEL maintainer="wang-junjian@qq.com"

#auto install tzdata(opencv depend)
ENV DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y \
    git wget nano \
    libopencv-dev python3-opencv \
    && rm -rf /var/lib/apt/lists/*

#set your localtime
RUN ln -fs /usr/share/zoneinfo/Asia/Shanghai /etc/localtime

WORKDIR /
// ...

• 构建容器

docker build -t darknet:latest-gpu-yolov4 .

• 训练的样本：train.txt

images/IMG_9255.JPG
images/IMG_9266.JPG
images/IMG_9280.JPG

• 验证的样本：valid.txt

images/IMG_9263.JPG

• 标注类型：voc.names

close
open

2. 修改网络配置文件

nano /detectron/project/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml

MODEL:
  TYPE: generalized_rcnn
  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
  NUM_CLASSES: 2
  FASTER_RCNN: True
  MASK_ON: True
NUM_GPUS: 1
SOLVER:
  WEIGHT_DECAY: 0.0001
  LR_POLICY: steps_with_decay
  BASE_LR: 0.002
  GAMMA: 0.1
  MAX_ITER: 4000
  STEPS: [0, 3000, 4000]
FPN:
  FPN_ON: True
  MULTILEVEL_ROIS: True
  MULTILEVEL_RPN: True
FAST_RCNN:
  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 7
  ROI_XFORM_SAMPLING_RATIO: 2
MRCNN:
  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
  RESOLUTION: 28  # (output mask resolution) default 14
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 14  # default 7
  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
  DILATION: 1  # default 2
  CONV_INIT: MSRAFill  # default GaussianFill
TRAIN:
  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
  DATASETS: ('coco_helmet_train', 'coco_helmet_val')
  SCALES: (800,)
  MAX_SIZE: 1333
  BATCH_SIZE_PER_IM: 512
  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
TEST:
  DATASETS: ('coco_2014_minival',)
  SCALE: 800
  MAX_SIZE: 1333
  NMS: 0.5
  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
  RPN_POST_NMS_TOP_N: 1000
OUTPUT_DIR: .

• 训练的样本：train.txt

yolos/IMG_9255.JPG
yolos/IMG_9266.JPG
yolos/IMG_9280.JPG

• 验证的样本：valid.txt

yolos/IMG_9263.JPG

• 标注类型：voc.names

close
open

• 配置文件：voc.data

classes= 2
train  = cfg/train.txt
valid  = cfg/valid.txt
names = cfg/voc.names
backup = backup

• 修改YOLO神经网络文件：yolov3.cfg

603行：filters=21    # (classes + 5)*3
610行：classes=2
689行：filters=21
696行：classes=2
776行：filters=21
783行：classes=2

• 使用LabelImg标注图像样本集

# python3 labelImg.py [图像目录] [标注名字文件] [标注目录]
python3 labelImg.py open-close/yolos/ open-close/cfg/yolo.names

• 下载基于imagenet的预训练模型 darknet53.conv.74

wget https://pjreddie.com/media/files/darknet53.conv.74