hjinnkim / yolov3-pytorch-ros

YOLOv3 Pytorch Implementation with ROS

yolov3_pytorch_ros

ROS package for running yolov3 with USB_CAM_node

Table of contents

• Environment
• Installation
• Usb cam setting
• Using custom dataset
• Running yolov3

Environment

• Ubuntu 18.04, Python 2.7, torch=1.4.0

Before you use this repository, we recommend you to install CUDA toolkit and cudnn

• Python 2.7 and torch 1.4.0, the recommended compatible version of CUDA toolkit is 10.x (10.0, 10.1 or 10.2) and cudnn is 7.6.5

Installation

Download the required packages by

# In your catkin_ws/src/
git clone https://github.com/hjinnkim/yolov3-pytorch-ros.git
cd yolov3_pytorch_ros/weights
sh download_weights.sh

and go to your catkin_ws and catkin_make

Also, you need cv_bridge packages.

sudo apt install ros-melodic-cv-bridge

rospy code needs execution previliege.

roscd yolov3-ros/src/
chmod +x yolov3_ros.py

Custom Dataset

If you have your custom trained weights, place the weights file in the weights directory and place the .names file in the data directory. Change the parameters in launch directory according to your files.

Running YOLO with ROS

You can run the yolov3 by:

roslaunch yolov3-ros yolov3-[corresponding file].launch

The yolov3_node will publish two topics:

/yolov3_detect/compressed
/yolov3_detect/objects

You can see the detected images by

rqt_image_view