Custom Darknet for training YOLOv4 on Google Colab with custom dataset

Setup darknet environment in Colab Notebook

To enable GPU backend for your notebook: Runtime->Change runtime type->Hardware Accelerator->GPU

# run these command line from notebook cell

!git clone https://github.com/quangnhat185/darknet_for_colab.git
%cd darknet_for_colab
!make
!chmod +x ./darknet

Tuning parameters from Colab environment

Double click on yolov4_config.pyto edit model parameters. More details about the meaning of each parameter can be found here

Generate YOLOv4 config and test file

!python yolov4_setup.py

Train with YOLOv4

!./darknet detector train data/yolov4.data cfg/yolov4_custom_train.cfg {weights_path} -map

Predict with YOLOv4

• Image (predicted image is saved at predictions.jpg:

  %cp data/yolov4.data cfg/coco.data
  !./darknet detect cfg/yolov4_custom_test.cfg {weights_path} {img_path}
  

• Video:

  usage: darknet_video.py [-h] -v VIDEO [-c CONFIG] -w WEIGHTS [-l LABEL]
                  [-m META] [-o OUTPUT]
  
  optional arguments:
    -h, --help            show this help message and exit
    -v VIDEO, --video VIDEO
                          Path to input video
    -c CONFIG, --config CONFIG
                          Path to yolo config file
    -w WEIGHTS, --weights WEIGHTS
                          Path to yolo weight
    -l LABEL, --label LABEL
                          Path to label file
    -m META, --meta META  Path to metaPath
    -o OUTPUT, --output OUTPUT
                          Path to output file  
  

  !python darknet_video.py -v {video path} -c cfg/yolov4_custom_test.cfg -w {weights_path}  -o output.mp4
  

Tutorial

YOLOv4 in Google Colab: Train your Custom Dataset (Traffic Signs) with ease

License

MIT License