# Normal Usage of [`ultralytics`](https://github.com/ultralytics/ultralytics) ## Export TensorRT Engine ### 1. ONNX -> TensorRT You can export your onnx model by `ultralytics` API. ``` shell yolo export model=yolov8s.pt format=onnx opset=11 simplify=True ``` or run this python script: ```python 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`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec) tools. Usage: ``` shell /usr/src/tensorrt/bin/trtexec \ --onnx=yolov8s.onnx \ --saveEngine=yolov8s.engine \ --fp16 ``` ### 2. Direct to TensorRT (NOT RECOMMAND!!) Usage: ```shell yolo export model=yolov8s.pt format=engine device=0 ``` or run python script: ```python 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`](../csrc/detect/normal) . ### Build: Please set you own librarys in [`CMakeLists.txt`](../csrc/detect/normal/CMakeLists.txt) and modify `CLASS_NAMES` and `COLORS` in [`main.cpp`](../csrc/detect/normal/main.cpp). Besides, you can modify the postprocess parameters such as `num_labels` and `score_thres` and `iou_thres` and `topk` in [`main.cpp`](../csrc/detect/normal/main.cpp). ```c++ int num_labels = 80; int topk = 100; float score_thres = 0.25f; float iou_thres = 0.65f; ``` And build: ``` shell export root=${PWD} cd src/detect/normal mkdir build && cd build cmake .. make mv yolov8 ${root} cd ${root} ``` Usage: ``` shell # 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 ```