This repo contains a ROS noetic package for the official YOLOv7. It wraps the official implementation into a ROS node (so most credit goes to the YOLOv7 creators).

There are currently two YOLOv7 variants out there. This repo contains the implementation from the paper YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.

Following ROS packages are required:

• vision_msgs
• geometry_msgs

First, clone the repo into your catkin workspace and build the package:

git clone https://github.com/lukazso/yolov7-ros.git ~/catkin_ws/src/
cd ~/catkin_ws
catkin build yolov7_ros

The Python requirements are listed in the requirements.txt. You can simply install them as

pip install -r requirements.txt

Download the YOLOv7 weights from the official repository.

Berkeley DeepDrive weights: I trained YoloV7 with a basic hyperparameter set (no special hyperparameter optimization) on the Berkeley DeepDrive dataset. You can download the weights here.

The package has been tested under Ubuntu 20.04 and Python 3.8.10.

Before you launch the node, adjust the parameters in the launch file. For example, you need to set the path to your YOLOv7 weights and the image topic to which this node should listen to. The launch file also contains a description for each parameter.

roslaunch yolov7_ros yolov7.launch

Each time a new image is received it is then fed into YOLOv7.

You can visualize the yolo results if you set the visualize flag in the launch file. Also, with the classes_path parameter you can provide a .txt file with the class labels. An example file is provided in berkeley.txt or coco.txt.

• The detections are published using the vision_msgs/Detection2DArray message type.
• The detections will be published under /yolov7/out_topic.
• If you set the visualize parameter to true, the detections will be drawn into the image, which is then published under /yolov7/out_topic/visualization.

• ROS2 implementation