YoloV3 Real-Time Object Detection on Screen With GradScreen Library and Tensorflow

This Project has been supported by the Turkish-German Univeristy
Supervised by Dr.-Ing. Soner Emec
Built by Cabbar Serif, Ibrahim Nemmura, Ovais Fakhani, Hasan Güzelmansur

The Project on Youtube

Getting started

Conda (Recommended)

# Tensorflow GPU
conda env create -f erste-gruppe.yml
conda activate erste-gruppe

Pip

# TensorFlow GPU
pip install -r requirements-gpu.txt

Nvidia Driver (For GPU, if you haven't set it up already)

# Ubuntu 18.04
sudo apt-add-repository -r ppa:graphics-drivers/ppa
sudo apt install nvidia-driver-430
# Windows/Other
https://www.nvidia.com/Download/index.aspx

Downloading official pretrained weights

For Linux: Let's download official yolov3 weights pretrained on COCO dataset.

# yolov3
wget https://pjreddie.com/media/files/yolov3.weights -O weights/yolov3.weights

# yolov3-tiny
wget https://pjreddie.com/media/files/yolov3-tiny.weights -O weights/yolov3-tiny.weights

For Windows: You can download the yolov3 weights by clicking here and yolov3-tiny here then save them to the weights folder.

Saving your yolov3 weights as a TensorFlow model.

Load the weights using load_weights.py script. This will convert the yolov3 weights into TensorFlow .ckpt model files!

# yolov3
python load_weights.py

# yolov3-tiny
python load_weights.py --weights ./weights/yolov3-tiny.weights --output ./weights/yolov3-tiny.tf --tiny

After executing one of the above lines, you should see .tf files in your weights folder.

baha-a/YoloV3_ObjectDetection_real-time_on_screen