CDTM-Deep-Learning-Drones
Course Material for CDTM Deep Learning Drones Course
Citation
If you find this useful for your research please cite our paper:
@inbook{Christ2016,
Author = {Christ, Patrick Ferdinand and Lachner, Florian and H{\"o}sl, Axel and Menze, Bjoern and Diepold, Klaus and Butz, Andreas},
Booktitle = {Computer Vision -- ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II},
Doi = {10.1007/978-3-319-48881-3_17},
Editor = {Hua, Gang and J{\'e}gou, Herv{\'e}},
Isbn = {978-3-319-48881-3},
Pages = {238--253},
Publisher = {Springer International Publishing},
Title = {Human-Drone-Interaction: A Case Study to Investigate the Relation Between Autonomy and User Experience},
Url = {http://dx.doi.org/10.1007/978-3-319-48881-3_17},
Year = {2016},
Bdsk-Url-1 = {http://dx.doi.org/10.1007/978-3-319-48881-3_17}}
Usage Docker Container
Build from scratch using
nvidia-docker build -t YOURNICENAMEFORTHECONTAINER .
Pull from Dockerhub.com
nvidia-docker pull patrickchrist/cdtm-deep-learning-drones
(or CPU-only:)
docker pull mjdev/cdtm-deep-learning-drones
Start Docker Container Using
With Nvidia GPU:
sudo nvidia-docker run -v /home/$(whoami):/data -p 5000:5000 patrickchrist/cdtm-deep-learning-drones
or CPU-only:
docker run -v /home/$(whoami):/data -p 5000:5000 mjdev/cdtm-deep-learning-drones
Enter a running Docker Container
Get the docker container id and remember it.
sudo nvidia-docker ps
Login using the following command.
sudo docker exec -it CONTAINERID bash
Python ARDrone Lib
To install the Python ARDrone Lib for Windows follow please the README in /python-ardrone.
Python ARDone Lib PS-Drone
Second Lib to control the drone.
Object Detection Tutorial Using Digits DetectNET
Download Data from Kitti
Labels
http://kitti.is.tue.mpg.de/kitti/data_object_label_2.zip
Data
http://kitti.is.tue.mpg.de/kitti/data_object_image_2.zip
Infos
http://kitti.is.tue.mpg.de/kitti/devkit_object.zip
Follow Digits Tutorial
Follow the Digits Object Detection Tutorial. [https://github.com/NVIDIA/DIGITS/tree/master/examples/object-detection]