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Last pushed: a year ago
Short Description
Docker version of Darknet
Full Description

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

For more information see the Darknet project website.

For questions or issues please use the Google Group.

#About This Fork#

This is a Fork for my Master Thesis at Reutlingen University 2016.
Some changes where made to the code for easy use and scripts are added to run Darknet in Docker -> train,analyze and use.

#How to install#

  1. Install Cuda
  2. Install Docker + Nvidia Docker and test it
  3. Clone this repo
  4. go to /docker and run: sudo ./
    • this will download the docker image and mount /opt/DockerDarknet on your system to transfer files to the docker and back

<i>Dakrnet is now installed with all his deps (openCv is not but you can add it)</i>

#How to use the standard Darknet#
just connect to the Docker
docker exec -it darknet /bin/bash
Now you can use Darknet yolo with the standard docu:

#How to use the analyze tool#
this tool is always running at http://hostip

#How to use Darknet with the added skripts#
first go to /opt/DockerDarknet/scripts

##Convert your training data##

  1. change the path at the or VBB_converter.js to your data as normal. (data must be at /opt/DockerDarknet or a subfolder)
  2. start the script you need
    • VBB: docker exec darknet /bin/sh –c "node /opt/DockerDarknet/scripts/VBB_Converter.js"
    • VOC: docker exec darknet /bin/sh –c "python /opt/DockerDarknet/scripts/"

##Train the NET##

  1. Be sure your training images are at /opt/DockerDarknet/training/images and your labels are at /opt/DockerDarknet/training/labels with the correct format!
  2. Edit with your classnumber and labels you want
  3. Run: ./
  4. Choose your config file like /cfg/yolo.cfg and change the classnumber and output layer to the values given from the prompt
  5. Run: ./ cfg/yolo.cfg darknet.conv.weights this will also start you a analyze tool at http://hostip for the loss function

##stop training##

  1. run ./

##use the NET##

  1. run ./ cfg/yolo.cfg backup/yolo-wights-final imagepath
    you can also you it with -tresh parameter or without imagepath to check multible pictures
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