YOLOv10-TensorRT10

YOLOv10 series model supports the latest TensorRT10.

Introduce

Yolov10 model supports TensorRT-10.

Environment

CUDA: 11.8 CUDNN: 8.9.1.23 TensorRT: TensorRT-10.2.0.19

Support

• YOLOv10-det support FP32/FP16/INT8 and Python/C++ API

Config

• Choose the YOLOv10 sub-model n/s/m/b/l/x from command line arguments.
• Other configs please check src/config.h

Build and Run

1. generate .wts from pytorch with .pt, or download .wts from model zoo

git clone https://github.com/THU-MIG/yolov10.git
cd yolov10/
wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt

git clone https://github.com/mpj1234/YOLOv10-TensorRT10.git
cp [PATH-TO-YOLOv10-TensorRT10]/gen_wts.py [YOLOv10]/.

python gen_wts.py -w yolov10n.pt -o yolov10n.wts
# A file 'yolov10n.wts' will be generated.

2. build YOLOv10-TensorRT10 and run

Detection

cd [PATH-TO-YOLOv10-TensorRT10]/
# Update kNumClass in src/config.h if your model is trained on custom dataset
mkdir build
cd build
cp [PATH-TO-yolov10]/yolov10n.wts .
cmake ..
make

# Build and serialize TensorRT engine
./yolov10_det -s yolov10n.wts yolov10n.engine [n/s/m/b/l/x]

# Run inference
./yolov10_det -d yolov10n.engine ../images
# The results are displayed in the console

3. Optional, load and run the tensorrt model in Python

// Install python-tensorrt, pycuda, etc.
// Ensure the yolov10n.engine
python yolov10_det_trt.py ./build/yolov10n.engine ./build/libmyplugins.so

INT8 Quantization

1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download my calibration images coco_calib from GoogleDrive or BaiduPan pwd: a9wh
2. unzip it in yolov10/build
3. set the macro USE_INT8 in src/config.h and make again
4. serialize the model and test