• This project is based on wang-xinyu/tensorrtx and qfgaohao/pytorch-ssd. The project has been tested on TensorRT 7.0 CUDA 10.2 CUDNN 7.6.5, and costs about 1ms(1000fps) to inference an image on GeForce GTX 1660 Ti.

• The project also has been tested on TensorRT 7.1.0(Developer Preview) CUDA 10.2 CUDNN 8.0.0(Developer Preview), and costs about 10-12ms(83-100fps) to inference an image on TX2 (by using the MAX-N mode and jetson_clocks).

• Another project "Scaled-YOLOv4-TensorRT".

(1) Generate mobilenet-v1-ssd.wts from pytorch implementation

  git clone https://github.com/tjuskyzhang/mobilenetv1-ssd-tensorrt.git
  
  git clone https://github.com/qfgaohao/pytorch-ssd.git
  
  cd pytorch-ssd
  
  wget -P models https://storage.googleapis.com/models-hao/mobilenet-v1-ssd-mp-0_675.pth
  
  wget -P models https://storage.googleapis.com/models-hao/voc-model-labels.txt

// 权重下载链接：https://pan.baidu.com/s/1Nagw-qP_PdTG4u_a9Dml-Q 提取码：yg27

  cp ../mobilenetv1-ssd-tensorrt/gen_wts.py .

  python gen_wts.py

// A file named 'mobilenet-v1-ssd.wts' will be generated.

  cp models/mobilenet-v1-ssd.wts ../mobilenetv1-ssd-tensorrt

(2) Build and run

  cd mobilenetv1-ssd-tensorrt

  mkdir build

  cd build

  cmake ..

  make

// Serialize the model and generate ssd_mobilenet.engine

  ./mobilenet-ssd-tensorrt -s

// Deserialize and generate the detection results _dog.jpg and so on.

  ./mobilenet-ssd-tensorrt -d ../samples