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How to train Darknet YOLOv4

0. Install cuda, cudnn and opencv

1. Compile on Linux

  git clone https://github.com/LiaoSteve/darknet.git
  cd darknet
  gedit Makefile

  • GPU=1 to build with CUDA to accelerate by using GPU (CUDA should be in /usr/local/cuda)

  • CUDNN=1 to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in /usr/local/cudnn)

  • OPENCV=1 to build with OpenCV 4.x/3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams.

  • LIBSO=1 to build a library darknet.so and binary runable file uselib that uses this library .

  • Choose your GPU capability (ARCH)

  • Notice that your cuda path NVCC

  • Save and close the Makefile, and type make in terminal.

2. Create my own dataset, and create your labels

  • Download labelImg in Windows OS: choose Windows_v1.8.0 and unzip it.

  • git clone https://github.com/LiaoSteve/darknet.git, and open it.

  • Enter the data dir and delete the deleteme.txt inside these dirs below.

    VOCdevkit
|----VOC2007
    |----Annotations
        |----deleteme.txt                  
    |----JPEGImages 
        |----deleteme.txt     
    |---- labels
        |----deleteme.txt

  • Put your images to JPEGImages dir, and open labelImg's data folder, edit predefined_classes.txt (type your desired classes per line)

  • Click labelImg.exe, choose Open Dir to JPEGImages images, and Change Save Dir to Annotations dir.

  • Use pascal VOC label format, and start labeling your images.

  • In data dir, open terminal and run open xml_to_txt.py, and revise the code line 14 (classes = ["dog","cat"]) to your classes :

    python xml_to_txt.py

  • In data dir, open terminal and run create_imageSets.py, and check your ImageSets/Main dir, there are four .txt files:

    python create_imageSets.py

  • Now in data dir, you will see 2007_train.txt, 2007_val.txt, and there are many .txt YOLO format labels in VOCdevkit/VOC2007/labels

  • (Optional) Copy .txt YOLO format labels to images dir :

    cp -r ./VOCdevkit/VOC2007/labels/*.txt ./VOCdevkit/VOC2007/JPEGImages/

  • Create obj.names file in darknet/data dir, and type your classes name (each line one class) :

    gedit obj.names

  • Create obj.data file :

    gedit obj.data

  • Paste text (revise classes) below to obj.data:

    classes = 2
train  = data/2007_train.txt
valid  = data/2007_val.txt
names = data/obj.names
backup = backup/
# notice the space near the '=' symbol

3. Download pre-trained weights yolov4.conv.137, darknet53.conv.74 and put it in darknet dir.

4. Edit cfg file.

  • Edit cfg file: filters=(classes + 5)x3

  • Calculate anchors:

    ./darknet detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416

5. Start training by using the command line:

  • In darknet dir :
./darknet detector train data/obj.data cfg/yolov4.cfg yolov4.conv.137 -map -dont_show > train.log
  • Or remote version with no GUI :
./darknet detector train data/obj.data cfg/my_yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map

6. After training

  • Test image :
./darknet detector test data/obj.data cfg/yolov4.cfg backup/yolov4.weight
  • Test video and save :
./darknet detector demo data/obj.data cfg/yolov4.cfg backup/yolov4.weight video.mp4 -out_filename result.avi
  • Test webcam :
./darknet detector demo data/obj.data cfg/yolov4.cfg backup/yolov4.weight -c 0
  • Test mAP :
./darknet detector map data/obj.data cfg/yolov4.cfg backup/yolov4.weight -iou_thresh 0.5 -thresh 0.25
  • Test recall :
./darknet detector recall data/obj.data cfg/yolov4.cfg backup/yolov3.weight

8. How to improve object detection :

9. Some tips

How to create custom dataset from OpenImage Dataset

  • https://github.com/theAIGuysCode/OIDv4_ToolKit

  • Download images and labels :

    pip install -r requirement.txt
python main.py downloader --classes Apple --type_csv train --limit 100

  • Revise classes.txt, and run python convert_annotations.py

    python convert_annotations.py

  • Copy images to VOC2007/JPEG dir, and copy labels (yolo format .txt) to labels dir

  • Copy generate_train.py to darknet/data/ dir, run and train yolov4 :

    python generate_train.py

./darknet detector train data/obj.data cfg/yolov4.cfg yolov4.conv.137 -map | tee -a train.log

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