This project is still under development.

• Description
• Installation
• Running the script
• Demo
• Contributing

In this notebook, I’ll perform a full implementation of YOLOv3 in PyTorch based on the following materials:

• Orginial implementation of darknet
• YOLOv3: An Incremental Improvement, Joseph Redmon, Ali Farhadi
• How to implement a YOLO (v3) object detector from scratch in PyTorch, Ayoosh Kathuria.

Example of image detection output:

Project was created with Python 3.6.x and pyTorch 0.4.0.

Clone repository and install requirements:

git clone https://github.com/BarbaraStempien/DL--YOLO-Real-Time-Object-Detection.git
cd DL--YOLO-Real-Time-Object-Detection
pip3 install -r requirements.txt

Download YOLOv3 weights:

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

You can download different weights from the YOLO website.

python image_detection.py -h

-h allows you to look up all terminal flags

python image_detection.py --images imgs --det det 

--images flag defines the directory of your input images --det flag defines the directory of your output imanges (directory to which images with detected objects will be saved)

python image_detection.py --images imgs --det det --reso 320

--reso flag allows you to change resolution of the input images, default value is 416. Number must be a multiple of 32 and greater than 32.

python image_detection.py --confidence 0.25

--confidence flag allows you to change confidence level. By default, model displays object detected with a confidence of 0.5 or higher.

python video_detection.py -h

-h flag allows you to look up all terminal flags defined.

python video_detection.py --video video/example.avi

--video flag defines the directory and name of your input video. Video must be in the the .avi format.

python video_detection.py

Run video detection script without a --video flag to use machine's default camera.

python video_detection.py --reso 320

--reso flag allows you to change resolution of the video, default value is 160.

python video_detection.py --confidence 0.25

--confidence flag allows you to change confidence level. By default, model displays object detected with a confidence of 0.5 or higher.

To stop web cam recording, press Q on the keyboard.

If you want to contribute, check out CONTRIBUTING.md.