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For Inferencing:

  1. First step. make sure anaconda and python > 3.8 is installed

  2. Create new environment using environment.yaml with following command.

conda env create -n <env_name> -f environment.yaml
  1. Download all the required DLL from google drive below and put all of them to current folder as below. https://drive.google.com/drive/folders/1HimwhHXfF9Fe4R-0t26Nj0zz-TWz82Za
  • Darknet-Easy-Installation\
    • data\
    • cfg\
    • image\
    • training\
    • darknet.py
    • environment.yaml
    • main.py
    • readme.txt
    • cudnn_adv_infer64_8.dll
    • cudnn_adv_train64_8.dll
    • ...
  1. Activate the environment created. activate your environment with cuda installed
conda activate <env_name>
cd to folder path
  1. Testing on the object detection python main.py

Enjoy your Darknet YOLO object detection!

For model training:

  1. Please download the darknet-master.rar from the link below, extract and put in C-drive: https://drive.google.com/drive/folders/1BPMs6rr0uEXY6Q_DrlUJ9uZIhtKVB1H_?usp=sharing
  • C:\
    • darknet-master
  1. Please download the pre-trained model yolov4.conv.137 from link below and put in data folder: https://drive.google.com/drive/folders/1Ikti48wHULfJ-Rfi4s6_Ibf68PUJQfyZ?usp=sharing
  • training\
    • data\
      • anchors\
      • code.txt
      • obj.data
      • obj.names
      • test.txt
      • train.txt
      • yolov3_custom.cfg
      • yolov4.conv.137
  1. test the model training using following command: activate your environment with cuda installed
conda activate <env_name>
cd to folder path
python main_train.py
  1. prepare the dataset according to guidance of AlexeyAb: https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects

Enjoy training your own custom object detection!

#If cuda error, please update your geforce driver to the latest version

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