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2664 images of drones, labeled, with trained YOLO weights.
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Latest commit 4188486 Apr 29, 2019

README.md

DroneNet

DroneNet is Joseph Redmon's YOLO real-time object detection system retrained on 2664 images of DJI drones, labeled.
The original and labeled images used for retraining can be found under the image and label folders respectively.


Setting up

  1. Install the Ubuntu Linux distribution.

  2. Open terminal and enter the following lines to build Darknet:

git clone https://github.com/pjreddie/darknet.git
cd darknet
make
  1. Move drone.data, drone.names, and yolo-drone.cfg under the cfg folder, create a weights directory and move yolo-drone.weights into the folder, move drone.jpg under the data folder, and move test.txt and train.txt in the root directory of your cloned darknet.

  2. Change lines 2 and 3 to your path in drone.data.


Running

Open terminal in the root directory of the darknet executable and enter:

./darknet detector test cfg/drone.data cfg/yolo-drone.cfg weights/yolo-drone.weights data/drone.jpg

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