YOLOv8 Object Detection

Requirements

• export YOLOv8 model into torchscript, see models/yolo.sh or take a look at quick start section Obtain YOLOv8
• download libtorch, see https://pytorch.org/

The following library is tested against stable version libtorch-2.4.0.

Quick Start

Install opencv

In case you want to use opencv, please install it first:

apt install libopencv-dev clang libclang-dev

Obtain YOLOv8

virtualenv ultra
. ultra/bin/activate
pip3 install ultralytics
yolo export model=yolov8n.pt
ls -l yolov8n.torchscript

More on yolo export: https://docs.ultralytics.com/modes/export/#__tabbed_1_2

Get libtorch

Get libtorch from https://pytorch.org/, the current bindings are using stable version of 2.4.0. Please decide beforehand if you want to use cpu or cuda version.

unzip ~/Downloads/libtorch-cxx11-abi-shared-with-deps-2.4.0+cpu.zip

Build

source ./env # environment
cargo build --release

Build Examples

The video example needs to have opencv installed and enabled as feature.

source ./env # environment
cargo build --release --examples --features=opencv

Run Examples

source ./env # environment
target/release/examples/yolo-predict

source ./env # environment
target/release/examples/video <some.video.file>

Description

This library tries to do things as close as ultralytics python package.

Image pre-processing

Two options are available: using torch and tensor, or using opencv. In the latter case the opencv feature must be turned on (default is off).

We pad the image exactly as ultralytics does, i.e. the aspect ratio is preserved and the gray (114, 114, 114) coler is used as padding to keep (640, 640) as input dimension.

Image post-processing

We try to use torch and tensors as long as possible. However, in certain cases it might be faster to move the prediction to the cpu. In that case, please initialize yolo as:

let yolo = yolo_v8::YoloV8ObjectDetection::with_model(yolo_v8::YOLOModel::Nano).post_process_on_cpu();