rofl256/dockerdarknet repository overview

#Darknet# 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 ./initDarknetDocker.sh

• this will download the docker image and mount /opt/DockerDarknet on your system to transfer files to the docker and back

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

#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: http://pjreddie.com/darknet/yolo/⁠

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

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

##Convert your training data##

1. change the path at the convert.py 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/convert.py"

##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 config.sh with your classnumber and labels you want
3. Run: ./config.sh
4. Choose your config file like /cfg/yolo.cfg and change the classnumber and output layer to the values given from the config.sh prompt
5. Run: ./train.sh 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 ./stopTraining.sh

##use the NET##

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