Difference between revisions of "YOLO"
Jump to navigation
Jump to search
Line 2: | Line 2: | ||
*Fist, we install OpenCV: | *Fist, we install OpenCV: | ||
− | + | *[[OpenCV Installation]] | |
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
=== Installing Darknet (GPU + OpenCV) === | === Installing Darknet (GPU + OpenCV) === |
Revision as of 22:25, 4 January 2019
Installation
- Fist, we install OpenCV:
- OpenCV Installation
Installing Darknet (GPU + OpenCV)
- In our forked version of the original repository, the GPU & OPENCV flags in the Makefile have already been set to 1, and the darknet/examples/rnn.c has been modified for being able to save the generate your own model. So we only need to do:
git clone https://github.com/mywdka/darknet.git cd darknet make
Using YOLO with Darknet (GPU + OpenCV). Tested on Ubuntu 16.04
- Download the weights:
wget https://pjreddie.com/media/files/yolov3.weights
- Go to the directory:
cd darknet
- Run the detector (image):
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
- Run the detector (video):
./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights <video file>
- Run the detector (webcam):
./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights