As it says on the tin.
Make sure you have docker and nvidia-docker installed and attach a V4L2 compatible camera and note its device id. Start a docker container using
$ docker run --mount source=isaac-sdk-build-cache,target=/root -v <path to project directory>:/workspace -w /workspace --gpus=all --device <path to camera eg: /dev/video2> --net=host -it firekind/isaac:2020.2-deepstream-5.0.1-devel /bin/bash
then, download the models.
/workspace$ ./download-models.sh
This will download the models into the models/yolo
directory. Then, compile the custom deepstream yolo plugin.
$ cd lib
$ export CUDA_VER=10.2
$ make
Edit the device_id
under the config
section of app/graphs/detector.app.json
file to the device id of your setup. Then, run:
$ bazel run //apps:detector
and open localhost:3000
on the browser to see the results.
Note: By default, the graph uses yoloV3 tiny. To use yoloV3, edit the
config-file-path
property of thenvinfer
element inapp/graphs/detector.app.json
toapp/configs/yolov3-config.txt