# Download ultralytics
wget https://github.com/ultralytics/ultralytics/archive/refs/tags/v8.3.0.zip -O ultralytics-8.3.0.zip
# Unzip ultralytics
unzip ultralytics-8.3.0.zip
cd ultralytics-8.3.0
# Download models
wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt -O yolo11n.pt
wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt -O yolo11n-cls.pt
wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt -O yolo11n-seg.pt
wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt -O yolo11n-pose.pt
# Generate .wts
cp [PATH-TO-TENSORRTX]/yolo11/gen_wts.py .
python gen_wts.py -w yolo11n.pt -o yolo11n.wts -t detect
python gen_wts.py -w yolo11n-cls.pt -o yolo11n-cls.wts -t cls
python gen_wts.py -w yolo11n-seg.pt -o yolo11n-seg.wts -t seg
python gen_wts.py -w yolo11n-pose.pt -o yolo11n-pose.wts -t pose
# A file 'yolo11n.wts' will be generated.

cd [PATH-TO-TENSORRTX]/yolo11
mkdir build
cd build
cmake ..
make

cp [PATH-TO-ultralytics]/yolo11n.wts .
# Build and serialize TensorRT engine
./yolo11_det -s yolo11n.wts yolo11n.engine [n/s/m/l/x]
# Run inference
./yolo11_det -d yolo11n.engine ../images [c/g]
# results saved in build directory

cp [PATH-TO-ultralytics]/yolo11n-cls.wts .
# Build and serialize TensorRT engine
./yolo11_cls -s yolo11n-cls.wts yolo11n-cls.engine [n/s/m/l/x]
# Download ImageNet labels
wget https://github.com/joannzhang00/ImageNet-dataset-classes-labels/blob/main/imagenet_classes.txt
# Run inference
./yolo11_cls -d yolo11n-cls.engine ../images

cp [PATH-TO-ultralytics]/yolo11n-seg.wts .
# Build and serialize TensorRT engine
./yolo11_seg -s yolo11n-seg.wts yolo11n-seg.engine [n/s/m/l/x]
# Download the labels file
wget -O coco.txt https://raw.githubusercontent.com/amikelive/coco-labels/master/coco-labels-2014_2017.txt
# Run inference
./yolo11_seg -d yolo11n-seg.engine ../images c coco.txt

cp [PATH-TO-ultralytics]/yolo11n-pose.wts .
# Build and serialize TensorRT engine
./yolo11_pose -s yolo11n-pose.wts yolo11n-pose.engine [n/s/m/l/x]
# Run inference
./yolo11_pose -d yolo11n-pose.engine ../images

// Install python-tensorrt, pycuda, etc.
// Ensure the yolo11n.engine
python yolo11_det_trt.py ./build/yolo11n.engine ./build/libmyplugins.so
# faq: in windows bug pycuda._driver.LogicError
# faq: in linux bug Segmentation fault
# Add the following code to the py file:
# import pycuda.autoinit
# import pycuda.driver as cuda