YOLOv8-TensorRT/docs/Normal.md

Normal Usage of ultralytics

Export TensorRT Engine

1. ONNX -> TensorRT

You can export your onnx model by ultralytics API.

yolo export model=yolov8s.pt format=onnx opset=11 simplify=True

or run this python script:

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8s.pt")  # load a pretrained model (recommended for training)
success = model.export(format="onnx", opset=11, simplify=True)  # export the model to onnx format
assert success

Then build engine by Trtexec Tools.

You can export TensorRT engine by trtexec tools.

Usage:

/usr/src/tensorrt/bin/trtexec \
--onnx=yolov8s.onnx \
--saveEngine=yolov8s.engine \
--fp16

2. Direct to TensorRT (NOT RECOMMAND!!)

Usage:

yolo export model=yolov8s.pt format=engine device=0

or run python script:

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8s.pt")  # load a pretrained model (recommended for training)
success = model.export(format="engine", device=0)  # export the model to engine format
assert success

After executing the above script, you will get an engine named yolov8s.engine .

Inference with c++

You can infer with c++ in csrc/detect/normal .

Build:

Please set you own librarys in CMakeLists.txt and modify CLASS_NAMES and COLORS in main.cpp.

Besides, you can modify the postprocess parameters such as num_labels and score_thres and iou_thres and topk in main.cpp.

int num_labels = 80;
int topk = 100;
float score_thres = 0.25f;
float iou_thres = 0.65f;

And build:

export root=${PWD}
cd src/detect/normal
mkdir build && cd build
cmake ..
make
mv yolov8 ${root}
cd ${root}

Usage:

# infer image
./yolov8 yolov8s.engine data/bus.jpg
# infer images
./yolov8 yolov8s.engine data
# infer video
./yolov8 yolov8s.engine data/test.mp4 # the video path