INSTALL.md

Installation

1. Clone YOLOv9 repo and install its requirements:

git clone https://github.com/WongKinYiu/yolov9
cd yolov9
pip install -r requirements.txt  

3. Download yolov9-c.pt or yolov9-e.pt model.
4. Convert the model to onnx format:

• Put reparameterize.py file to YOLOv9 installation folder and perform re-parameterization:

python reparameterize.py yolov9-c.pt yolov9-c-converted.pt

• Or you can skip re-parameterization and downloaded the re-parameterized models yolov9-c-converted.pt and yolov9-e-converted.pt.

• Then export the model:

python export.py --weights yolov9-c-converted.pt --include onnx

4. Build a TensorRT engine:

trtexec.exe --onnx=yolov9-c-converted.onnx --explicitBatch --saveEngine=yolov9-c.engine --fp16

Note

'--fp16' is an optional argument for performing inference using fp16 precision.

5. Set opencv and tensorrt installation paths in CMakeLists.txt:

# Find and include OpenCV
set(OpenCV_DIR "your path to OpenCV")
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})

# Set TensorRT path if not set in environment variables
set(TENSORRT_DIR "your path to TensorRT")

6. Build the project:

mkdir build
cd build
cmake ..
cmake --build . --config Release

Tested Environment

• TensorRT 8.6
• CUDA 11.8
• Windows 10