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README.md
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gen_wts.py
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yolov10_det.cpp
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yolov10_det_trt.py
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README.md

Introduce

Yolov10 model supports TensorRT-8.

Environment

CUDA: 11.8

CUDNN: 8.9.1.23

TensorRT: TensorRT-8.2.5.1 / GPU: RTX1650

TensorRT: TensorRT-8.4.3.1 / GPU: RTX4070

# faq
Error Code 1: Internal Error (Unsupported SM: 0x809)
The architecture of the higher version does not support the use of the earlier version of TensorRT,
and you need to upgrade the TensorRT version

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/wang-xinyu/tensorrtx.git
cp [PATH-TO-TENSORRTX]/yolov10/gen_wts.py .

python gen_wts.py -w yolov10n.pt -o yolov10n.wts
# A file 'yolov10n.wts' will be generated.
  1. build tensorrtx/yolov10 and run

Detection

cd [PATH-TO-TENSORRTX]/yolov10

# add test images
mkdir images
cp [PATH-TO-TENSORRTX]/yolov3-spp/samples/*.jpg ./images

# 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
  1. 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

More Information

See the readme in home page.