README.md

Start DeepStream Using the engine build from YOLOv8-TensorRT

1. Build you own TensorRT engine from trtexec or build.py

For example, if you have built an engine named yolov8s.engine.

2. Compile deepstream plugin

First, modify the CMakeLists.txt

# Set your own TensorRT path
set(TensorRT_INCLUDE_DIRS /usr/include/x86_64-linux-gnu)
set(TensorRT_LIBRARIES /usr/lib/x86_64-linux-gnu)
# Set your own DeepStream path
set(DEEPSTREAM /opt/nvidia/deepstream/deepstream)

Second, build deepstream plugin

mkdir build
cd build
cmake ..
make

You will get a lib libnvdsinfer_custom_bbox_yoloV8.so in build.

3. Modify the deepstream config

The net config is config_yoloV8.txt. Please modify by your own model.

net-scale-factor=0.0039215697906911373                              # the normalize param == 1/255
model-engine-file=./yolov8s.engine                                  # the engine path you build
labelfile-path=./labels.txt                                         # the class name path
num-detected-classes=80                                             # the number of classes
output-blob-names=num_dets;bboxes;scores;labels                     # the model output names
custom-lib-path=./build/libnvdsinfer_custom_bbox_yoloV8.so          # the deepstream plugin you build

The deepstream config is deepstream_app_config.txt.

****
[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI
type=3
uri=file://./sample_1080p_h264.mp4                                  # the video path or stream you want to detect
****
****
config-file=config_yoloV8.txt                                       # the net config path

You can get more information from deepstream offical.

4. Runing detector !

deepstream-app -c deepstream_app_config.txt