YOLO

Installation

• Fist, we install OpenCV:
• OpenCV Installation

• Then, we need to install our modified version of Darknet:
• Darknet Installation

• Go to the directory and download the YOLO weights:

cd darknet
wget https://pjreddie.com/media/files/yolov3.weights

Using YOLO

• Using YOLO with Darknet (GPU + OpenCV). Tested on Ubuntu 16.04

Using YOLO with an image

• Go to the directory and run the detector for images:

cd darknet
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

Using YOLO with a video

• Go to the directory and run the detector for videos:

cd darknet
./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights data/ny720p2.mp4

• Run the detector (webcam):

./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights

Training your own Dataset

• Download the convolutional weights from the darknet53 model that are pre-trained on Imagenet and place them in the Darknet folder:

wget https://pjreddie.com/media/files/darknet53.conv.74

*Step 1: Dataset

• Download the images for the categories that you would like YOLO to detect.

• Step 2: Data Annotation
• Download the YOLO annotation tool:

https://github.com/ManivannanMurugavel/Yolo-Annotation-Tool-New-

• Enter the directory with the terminal:

cd Downloads
cd Yolo-Annotation-Tool-New-
python main.py

• Write the path where you have placed the images of the first category of your dataset and click the button "Load"
• Select the category of those images in the combo box next that "Choose Class".
• Create the bounding boxes in each one of the images in this category by first, clicking on the image and then clicking on the button Next
• When you are done with all the images of that category, write the path of the next category and press the Load button.
• Select the category of those images in the combo box next that "Choose Class".
• Create the bounding boxes for all the images of this category.
• Do the same for the rest of categories.
• Whenever you are done, go to the terminal and close the tool by typing CTRL+C
• The tool should have created one text file that contains the objects coordinates for each one of the images of your dataset.
• Now we need to split the images of the dataset into two sets: train and test.
• We will do that, by typing in the terminal:

python process.py

• By default this is done in a 90%/10% ratio, but it can be changed in the script.
• This script will generate the train.txt and test.txt files that we need to copy into the Darknet folder.

• We need to create 3 files:
  • myDataset.data

• More information:
• [1]
• [2]
• [3]

Guide

• https://pjreddie.com/darknet/yolo/
• Terminal basic tutorial: https://maker.pro/linux/tutorial/basic-linux-commands-for-beginners