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

3. 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
  • ...

4. Activate the environment created. activate your environment with cuda installed

conda activate <env_name>
cd to folder path

5. 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

2. 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

3. 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

4. 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