This repository contains step by step guide to build and convert YoloV7 model into a TensorRT engine on Jetson. This has been tested on Jetson Nano or Jetson Xavier.
Please install Jetpack OS version 4.6 as mentioned by Nvidia and follow below steps. Please follow each steps exactly mentioned in the video links below :
Build YoloV7 TensorRT Engine on Jetson Nano:
Object Detection YoloV7 TensorRT Engine on Jetson Nano:
Jetson Xavier:
Please install below libraries::
$ sudo apt-get update
$ sudo apt-get install -y liblapack-dev libblas-dev gfortran libfreetype6-dev libopenblas-base libopenmpi-dev libjpeg-dev zlib1g-dev
$ sudo apt-get install -y python3-pip
Numpy comes pre installed with Jetpack, so make sure you uninstall it first and then confirm if it's uninstalled or not. Upgrade pip3 as well and then install below packages:
$ numpy==1.19.0
$ pandas==0.22.0
$ Pillow==8.4.0
$ PyYAML==3.12
$ scipy==1.5.4
$ psutil
$ tqdm==4.64.1
$ imutils
If PyYAML is giving issues, run pip3 install "cython<3.0.0" && pip install --no-build-isolation pyyaml==6.0
We need to first export few paths
$ export PATH=/usr/local/cuda-10.2/bin${PATH:+:${PATH}}
$ export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
$ python3 -m pip install pycuda --user
$ sudo apt install python3-seaborn
$ wget https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl -O torch-1.10.0-cp36-cp36m-linux_aarch64.whl
$ pip3 install torch-1.10.0-cp36-cp36m-linux_aarch64.whl
$ git clone --branch v0.11.1 https://github.com/pytorch/vision torchvision
$ cd torchvision
$ sudo python3 setup.py install
sudo python3 -m pip install -U jetson-stats==3.1.4
This marks the installation of all the required libraries.
Yolov7-tiny.pt is already provided in the repo. But if you want you can download any other version of the yolov7 model. Then run below command to convert .pt file into .wts file
$ python3 gen_wts.py -w yolov7-tiny.pt -o yolov7-tiny.wts
Create a build directory inside yolov5. Copy and paste generated wts file into build directory and run below commands. If using custom model, make sure to update kNumClas in yolov7/include/config.h
$ cd yolov7/
$ mkdir build
$ cd build
$ cp ../../yolov7-tiny.wts .
$ cmake ..
$ make
$ sudo ./yolov7 -s yolov7-tiny.wts yolov7-tiny.engine t
$ sudo ./yolov7 -d yolov7-tiny.engine ../images
This will do inferencing over images and output will be saved in build directory.
Use app.py to do inferencing on any video file or camera.
$ python3 app.py
If you have custom model, make sure to update categories as per your classes in yolovDet.py .