This is a web interface to YOLOv8 neural network implemented on Rust using ONNX.
This is a source code for a "How to create YOLOv8-based object detection web service using Python, Julia, Node.js, JavaScript, Go and Rust" tutorial extended by instance segmentation. This web application uses the YOLOv8 both for object detection and segmentation.
See demo here:
The algorithm, that used to run YOLOv8 segmentation using ONNX described in this article.
- Clone this repository:
git clone git@github.com:AndreyGermanov/yolov8_onnx_rust_segmentation.git
Ensure that the ONNX runtime installed on your operating system, because the library that integrated to the Rust package may not work correctly. To install it, you can download the archive for your operating system from here, extract and copy contents of "lib" subfolder to the system libraries path of your operating system.
- Download the yolov8m_seg.onnx model file and put it to the root of downloaded repository.
Execute:
cargo run
It will start a webserver on http://localhost:8080. Use any web browser to open the web interface.
Using the interface you can upload the image to the object detector and see bounding boxes and segmentation masks of all objects detected on it.