README.md

YOLOv9 tensorrt deployment

This repository provides an API for accelerating inference deployment, with two open interface implementation: C++ and Python. C++also provides the use of CUDA programming to accelerate YOLOv9 model preprocessing and post-processing to pursue faster model inference speed🔥🔥🔥

⭐ Build

1. Export onnx

Clone YOLOv9 code repository, download the original model provided by the repository, or train your own model, such as yolov9-c.pt

# export onnx
python export.py --weights yolov9-c.pt --simplify --include "onnx"

2. Setup

Place the exported onnx file in the "yolov9-tensorrt/configs" folder and configure the relevant parameters through the "yolov9-tensorrt/configs/yolov9.yaml" file

# move onnx
cd yolov9-Tensorrt
mv yolov9-c.onnx ./configs

Modify parameter configuration in configs/yolov9-yaml

# modify configuration in configs/yolov9.yaml
confTreshold: 0.25              #Detection confidence threshold
nmsTreshold : 0.45              #nms threshold
maxSupportBatchSize: 1          #support max input batch size
quantizationInfer: "FP16"       #support FP32 or FP16 quantization
onnxFile: "yolov9-c.onnx"       # The currently used onnx model file
engineFile: "yolov9-c.engine"   # Automatically generate file names for the Tensorrt inference engine

3. Build project

mkdir build
cd build
cmake ..
make -j4

4. python API

Modify parameter configuration in configs/yolov9py-yaml

# modify configuration in configs/yolov9py.yaml
confTreshold: 0.3               # detect treshold
nmsThreshold: 0.45              #nms treshold
quantizationInfer: "FP16"       #FP32 or FP16
onnxFile: "yolov9-c.onnx"       # The currently used onnx model file
engineFile: "yolov9-c.engine"   # Automatically generate file names for the Tensorrt inference engine

🌠 Run demo

The first run will generate the inference engine ".engine" file in the configs folder. If the inference engine has already been generated, it will not be generated again

run c++ demo API

# run images floder
./demo ../data

run python demo API

# run images floder
python yolov9_trt.py --configs configs --yaml_file yolov9py.yaml --data data

👏 Acknowledgement

This project is based on the following awesome projects:

• Yolov9 - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information.
• TensorRT - TensorRT samples and api documentation.

🤗 Citation

@article{wang2024yolov9,
  title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},
  author={Wang, Chien-Yao  and Liao, Hong-Yuan Mark},
  booktitle={arXiv preprint arXiv:2402.13616},
  year={2024}
}