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DeepStream-Yolo

NVIDIA DeepStream SDK 6.1 / 6.0.1 / 6.0 configuration for YOLO models

Future updates

  • DeepStream tutorials
  • YOLOX support
  • YOLOv6 support
  • Dynamic batch-size

Improvements on this repository

  • Darknet cfg params parser (no need to edit nvdsparsebbox_Yolo.cpp or other files)
  • Support for new_coords and scale_x_y params
  • Support for new models
  • Support for new layers
  • Support for new activations
  • Support for convolutional groups
  • Support for INT8 calibration
  • Support for non square models
  • New documentation for multiple models
  • YOLOv5 support
  • YOLOR support
  • GPU YOLO Decoder #138
  • PP-YOLOE support
  • YOLOv7 support
  • Optimized NMS #142
  • Models benchmarks

Getting started

Requirements

DeepStream 6.1 on x86 platform

DeepStream 6.0.1 / 6.0 on x86 platform

DeepStream 6.1 on Jetson platform

DeepStream 6.0.1 / 6.0 on Jetson platform

Suported models

Benchmarks

Config

board = NVIDIA Tesla V100 16GB (AWS: p3.2xlarge)
batch-size = 1
eval = val2017 (COCO)
sample = 1920x1080 video

NOTE: Used maintain-aspect-ratio=1 in config_infer file for Darknet (with letter_box=1) and PyTorch models.

NMS config

  • Eval
nms-iou-threshold = 0.6 (Darknet) / 0.65 (PyTorch) / 0.7 (Paddle)
pre-cluster-threshold = 0.001
topk = 300
  • Test
nms-iou-threshold = 0.45 / 0.7 (Paddle)
pre-cluster-threshold = 0.25
topk = 300

Results

NOTE: * = PyTorch

NOTE: ** = The YOLOv4 is trained with the trainvalno5k set, so the mAP is high on val2017 test

DeepStream Precision Resolution IoU=0.5:0.95 IoU=0.5 IoU=0.75 FPS
(without display)
PP-YOLOE-x FP16 640 0.506 0.681 0.551 116.54
PP-YOLOE-l FP16 640 0.498 0.674 0.545 187.93
PP-YOLOE-m FP16 640 0.476 0.646 0.522 257.42
PP-YOLOE-s (400) FP16 640 0.422 0.589 0.463 465.23
YOLOv7-E6E FP16 1280 0.476 0.648 0.521 47.82
YOLOv7-D6 FP16 1280 0.479 0.648 0.520 60.66
YOLOv7-E6 FP16 1280 0.471 0.640 0.516 73.05
YOLOv7-W6 FP16 1280 0.444 0.610 0.483 110.29
YOLOv7-X* FP16 640 0.496 0.679 0.536 162.31
YOLOv7* FP16 640 0.476 0.660 0.518 237.79
YOLOv7-Tiny Leaky* FP16 640 0.345 0.516 0.372 611.36
YOLOv7-Tiny Leaky* FP16 416 0.328 0.493 0.348 633.73
YOLOv5x6 6.1 FP16 1280 0.508 0.683 0.554 54.88
YOLOv5l6 6.1 FP16 1280 0.494 0.668 0.540 87.86
YOLOv5m6 6.1 FP16 1280 0.469 0.644 0.514 142.68
YOLOv5s6 6.1 FP16 1280 0.399 0.581 0.438 271.19
YOLOv5n6 6.1 FP16 1280 0.317 0.487 0.344 392.20
YOLOv5x 6.1 FP16 640 0.470 0.652 0.513 152.99
YOLOv5l 6.1 FP16 640 0.454 0.636 0.496 247.60
YOLOv5m 6.1 FP16 640 0.421 0.604 0.458 375.06
YOLOv5s 6.1 FP16 640 0.344 0.528 0.371 602.44
YOLOv5n 6.1 FP16 640 0.247 0.413 0.256 629.04
YOLOv4** FP16 608 0.497 0.739 0.549 206.23
YOLOv4-Tiny FP16 416 0.215 0.402 0.205 634.69

dGPU installation

To install the DeepStream on dGPU (x86 platform), without docker, we need to do some steps to prepare the computer.

DeepStream 6.1

1. Disable Secure Boot in BIOS

2. Install dependencies

sudo apt-get update
sudo apt-get install gcc make git libtool autoconf autogen pkg-config cmake
sudo apt-get install python3 python3-dev python3-pip
sudo apt-get install dkms
sudo apt-get install libssl1.1 libgstreamer1.0-0 gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav libgstrtspserver-1.0-0 libjansson4 libyaml-cpp-dev
sudo apt-get install linux-headers-$(uname -r)

NOTE: Purge all NVIDIA driver, CUDA, etc (replace $CUDA_PATH to your CUDA path)

sudo nvidia-uninstall
sudo $CUDA_PATH/bin/cuda-uninstaller
sudo apt-get remove --purge '*nvidia*'
sudo apt-get remove --purge '*cuda*'
sudo apt-get remove --purge '*cudnn*'
sudo apt-get remove --purge '*tensorrt*'
sudo apt autoremove --purge && sudo apt autoclean && sudo apt clean

3. Install CUDA Keyring

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.0-1_all.deb
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt-get update

4. Download and install NVIDIA Driver

  • TITAN, GeForce RTX / GTX series and RTX / Quadro series

    wget https://us.download.nvidia.com/XFree86/Linux-x86_64/510.47.03/NVIDIA-Linux-x86_64-510.47.03.run
    
  • Data center / Tesla series

    wget https://us.download.nvidia.com/tesla/510.47.03/NVIDIA-Linux-x86_64-510.47.03.run
    
  • Run

    sudo sh NVIDIA-Linux-x86_64-510.47.03.run --silent --disable-nouveau --dkms --install-libglvnd
    

    NOTE: This step will disable the nouveau drivers.

  • Reboot

    sudo reboot
    
  • Install

    sudo sh NVIDIA-Linux-x86_64-510.47.03.run --silent --disable-nouveau --dkms --install-libglvnd
    

NOTE: If you are using a laptop with NVIDIA Optimius, run

sudo apt-get install nvidia-prime
sudo prime-select nvidia

5. Download and install CUDA

wget https://developer.download.nvidia.com/compute/cuda/11.6.1/local_installers/cuda_11.6.1_510.47.03_linux.run
sudo sh cuda_11.6.1_510.47.03_linux.run --silent --toolkit
  • Export environment variables

    echo $'export PATH=/usr/local/cuda-11.6/bin${PATH:+:${PATH}}\nexport LD_LIBRARY_PATH=/usr/local/cuda-11.6/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}' >> ~/.bashrc && source ~/.bashrc
    

6. Download from NVIDIA website and install the TensorRT

TensorRT 8.2 GA Update 4 for Ubuntu 20.04 and CUDA 11.0, 11.1, 11.2, 11.3, 11.4 and 11.5 DEB local repo Package

sudo dpkg -i nv-tensorrt-repo-ubuntu2004-cuda11.4-trt8.2.5.1-ga-20220505_1-1_amd64.deb
sudo apt-key add /var/nv-tensorrt-repo-ubuntu2004-cuda11.4-trt8.2.5.1-ga-20220505/82307095.pub
sudo apt-get update
sudo apt-get install libnvinfer8=8.2.5-1+cuda11.4 libnvinfer-plugin8=8.2.5-1+cuda11.4 libnvparsers8=8.2.5-1+cuda11.4 libnvonnxparsers8=8.2.5-1+cuda11.4 libnvinfer-bin=8.2.5-1+cuda11.4 libnvinfer-dev=8.2.5-1+cuda11.4 libnvinfer-plugin-dev=8.2.5-1+cuda11.4 libnvparsers-dev=8.2.5-1+cuda11.4 libnvonnxparsers-dev=8.2.5-1+cuda11.4 libnvinfer-samples=8.2.5-1+cuda11.4 libnvinfer-doc=8.2.5-1+cuda11.4 libcudnn8-dev=8.4.0.27-1+cuda11.6 libcudnn8=8.4.0.27-1+cuda11.6
sudo apt-mark hold libnvinfer* libnvparsers* libnvonnxparsers* libcudnn8* tensorrt

7. Download from NVIDIA website and install the DeepStream SDK

DeepStream 6.1 for Servers and Workstations (.deb)

sudo apt-get install ./deepstream-6.1_6.1.0-1_amd64.deb
rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin
sudo ln -snf /usr/local/cuda-11.6 /usr/local/cuda

8. Reboot the computer

sudo reboot
DeepStream 6.0.1 / 6.0

1. Disable Secure Boot in BIOS

If you are using a laptop with newer Intel/AMD processors and your Graphics in Settings->Details->About tab is llvmpipe, please update the kernel.
wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-headers-5.11.0-051100_5.11.0-051100.202102142330_all.deb
wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-headers-5.11.0-051100-generic_5.11.0-051100.202102142330_amd64.deb
wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-image-unsigned-5.11.0-051100-generic_5.11.0-051100.202102142330_amd64.deb
wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-modules-5.11.0-051100-generic_5.11.0-051100.202102142330_amd64.deb
sudo dpkg -i  *.deb
sudo reboot

2. Install dependencies

sudo apt-get update
sudo apt-get install gcc make git libtool autoconf autogen pkg-config cmake
sudo apt-get install python3 python3-dev python3-pip
sudo apt install libssl1.0.0 libgstreamer1.0-0 gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav libgstrtspserver-1.0-0 libjansson4
sudo apt-get install linux-headers-$(uname -r)

NOTE: Install DKMS only if you are using the default Ubuntu kernel

sudo apt-get install dkms

NOTE: Purge all NVIDIA driver, CUDA, etc (replace $CUDA_PATH to your CUDA path)

sudo nvidia-uninstall
sudo $CUDA_PATH/bin/cuda-uninstaller
sudo apt-get remove --purge '*nvidia*'
sudo apt-get remove --purge '*cuda*'
sudo apt-get remove --purge '*cudnn*'
sudo apt-get remove --purge '*tensorrt*'
sudo apt autoremove --purge && sudo apt autoclean && sudo apt clean

3. Install CUDA Keyring

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt-get update

4. Download and install NVIDIA Driver

  • TITAN, GeForce RTX / GTX series and RTX / Quadro series

    wget https://us.download.nvidia.com/XFree86/Linux-x86_64/470.129.06/NVIDIA-Linux-x86_64-470.129.06.run
    
  • Data center / Tesla series

    wget https://us.download.nvidia.com/tesla/470.129.06/NVIDIA-Linux-x86_64-470.129.06.run
    
  • Run

    sudo sh NVIDIA-Linux-x86_64-470.129.06.run --silent --disable-nouveau --dkms --install-libglvnd
    

    NOTE: This step will disable the nouveau drivers.

    NOTE: Remove --dkms flag if you installed the 5.11.0 kernel.

  • Reboot

    sudo reboot
    
  • Install

    sudo sh NVIDIA-Linux-x86_64-470.129.06.run --silent --disable-nouveau --dkms --install-libglvnd
    

    NOTE: Remove --dkms flag if you installed the 5.11.0 kernel.

NOTE: If you are using a laptop with NVIDIA Optimius, run

sudo apt-get install nvidia-prime
sudo prime-select nvidia

5. Download and install CUDA

wget https://developer.download.nvidia.com/compute/cuda/11.4.1/local_installers/cuda_11.4.1_470.57.02_linux.run
sudo sh cuda_11.4.1_470.57.02_linux.run --silent --toolkit
  • Export environment variables

    echo $'export PATH=/usr/local/cuda-11.4/bin${PATH:+:${PATH}}\nexport LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}' >> ~/.bashrc && source ~/.bashrc
    

6. Download from NVIDIA website and install the TensorRT

TensorRT 8.0.1 GA for Ubuntu 18.04 and CUDA 11.3 DEB local repo package

sudo dpkg -i nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626_1-1_amd64.deb
sudo apt-key add /var/nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626/7fa2af80.pub
sudo apt-get update
sudo apt-get install libnvinfer8=8.0.1-1+cuda11.3 libnvinfer-plugin8=8.0.1-1+cuda11.3 libnvparsers8=8.0.1-1+cuda11.3 libnvonnxparsers8=8.0.1-1+cuda11.3 libnvinfer-bin=8.0.1-1+cuda11.3 libnvinfer-dev=8.0.1-1+cuda11.3 libnvinfer-plugin-dev=8.0.1-1+cuda11.3 libnvparsers-dev=8.0.1-1+cuda11.3 libnvonnxparsers-dev=8.0.1-1+cuda11.3 libnvinfer-samples=8.0.1-1+cuda11.3 libnvinfer-doc=8.0.1-1+cuda11.3 libcudnn8-dev=8.2.1.32-1+cuda11.3 libcudnn8=8.2.1.32-1+cuda11.3
sudo apt-mark hold libnvinfer* libnvparsers* libnvonnxparsers* libcudnn8* tensorrt

7. Download from NVIDIA website and install the DeepStream SDK

  • DeepStream 6.0.1 for Servers and Workstations (.deb)

    sudo apt-get install ./deepstream-6.0_6.0.1-1_amd64.deb
    
  • DeepStream 6.0 for Servers and Workstations (.deb)

    sudo apt-get install ./deepstream-6.0_6.0.0-1_amd64.deb
    
  • Run

    rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin
    sudo ln -snf /usr/local/cuda-11.4 /usr/local/cuda
    

8. Reboot the computer

sudo reboot

Basic usage

1. Download the repo

git clone https://github.com/marcoslucianops/DeepStream-Yolo.git
cd DeepStream-Yolo

2. Download the cfg and weights files from Darknet repo to the DeepStream-Yolo folder

3. Compile the lib

  • DeepStream 6.1 on x86 platform

    CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
    
  • DeepStream 6.0.1 / 6.0 on x86 platform

    CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
    
  • DeepStream 6.1 on Jetson platform

    CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
    
  • DeepStream 6.0.1 / 6.0 on Jetson platform

    CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
    

4. Edit the config_infer_primary.txt file according to your model (example for YOLOv4)

[property]
...
custom-network-config=yolov4.cfg
model-file=yolov4.weights
...

5. Run

deepstream-app -c deepstream_app_config.txt

NOTE: If you want to use YOLOv2 or YOLOv2-Tiny models, change the deepstream_app_config.txt file before run it

...
[primary-gie]
...
config-file=config_infer_primary_yoloV2.txt
...

Docker usage

  • x86 platform

    nvcr.io/nvidia/deepstream:6.1-devel
    nvcr.io/nvidia/deepstream:6.1-triton
    
  • Jetson platform

    nvcr.io/nvidia/deepstream-l4t:6.1-samples
    nvcr.io/nvidia/deepstream-l4t:6.1-triton
    

    NOTE: To compile the nvdsinfer_custom_impl_Yolo, you need to install the g++ inside the container

    apt-get install build-essential
    

    NOTE: With DeepStream 6.1, the container image missed to include certain header files that will be available on host machine with Compute libraries installed from Jetpack. To mount the headers, use:

    -v /usr/include/aarch64-linux-gnu/NvInfer.h:/usr/include/aarch64-linux-gnu/NvInfer.h -v /usr/include/aarch64-linux-gnu/NvInferLegacyDims.h:/usr/include/aarch64-linux-gnu/NvInferLegacyDims.h -v /usr/include/aarch64-linux-gnu/NvInferRuntimeCommon.h:/usr/include/aarch64-linux-gnu/NvInferRuntimeCommon.h -v /usr/include/aarch64-linux-gnu/NvInferVersion.h:/usr/include/aarch64-linux-gnu/NvInferVersion.h -v /usr/include/aarch64-linux-gnu/NvInferRuntime.h:/usr/include/aarch64-linux-gnu/NvInferRuntime.h -v /usr/include/aarch64-linux-gnu/NvInferImpl.h:/usr/include/aarch64-linux-gnu/NvInferImpl.h -v /usr/include/aarch64-linux-gnu/NvCaffeParser.h:/usr/include/aarch64-linux-gnu/NvCaffeParser.h -v /usr/include/aarch64-linux-gnu/NvUffParser.h:/usr/include/aarch64-linux-gnu/NvUffParser.h -v /usr/include/aarch64-linux-gnu/NvInferPlugin.h:/usr/include/aarch64-linux-gnu/NvInferPlugin.h -v /usr/include/aarch64-linux-gnu/NvInferPluginUtils.h:/usr/include/aarch64-linux-gnu/NvInferPluginUtils.h -v /usr/local/cuda/:/usr/local/cuda/
    
    Example
    sudo docker run -it --rm --net=host --runtime nvidia -e DISPLAY=$DISPLAY -w /opt/nvidia/deepstream/deepstream-6.1 -v /tmp/.X11-unix/:/tmp/.X11-unix -v /usr/include/aarch64-linux-gnu/NvInfer.h:/usr/include/aarch64-linux-gnu/NvInfer.h -v /usr/include/aarch64-linux-gnu/NvInferLegacyDims.h:/usr/include/aarch64-linux-gnu/NvInferLegacyDims.h -v /usr/include/aarch64-linux-gnu/NvInferRuntimeCommon.h:/usr/include/aarch64-linux-gnu/NvInferRuntimeCommon.h -v /usr/include/aarch64-linux-gnu/NvInferVersion.h:/usr/include/aarch64-linux-gnu/NvInferVersion.h -v /usr/include/aarch64-linux-gnu/NvInferRuntime.h:/usr/include/aarch64-linux-gnu/NvInferRuntime.h -v /usr/include/aarch64-linux-gnu/NvInferImpl.h:/usr/include/aarch64-linux-gnu/NvInferImpl.h -v /usr/include/aarch64-linux-gnu/NvCaffeParser.h:/usr/include/aarch64-linux-gnu/NvCaffeParser.h -v /usr/include/aarch64-linux-gnu/NvUffParser.h:/usr/include/aarch64-linux-gnu/NvUffParser.h -v /usr/include/aarch64-linux-gnu/NvInferPlugin.h:/usr/include/aarch64-linux-gnu/NvInferPlugin.h -v /usr/include/aarch64-linux-gnu/NvInferPluginUtils.h:/usr/include/aarch64-linux-gnu/NvInferPluginUtils.h -v /usr/local/cuda/:/usr/local/cuda/ nvcr.io/nvidia/deepstream-l4t:6.1-samples
    

NMS Configuration

To change the nms-iou-threshold, pre-cluster-threshold and topk values, modify the config_infer file and regenerate the model engine file

[class-attrs-all]
nms-iou-threshold=0.45
pre-cluster-threshold=0.25
topk=300

NOTE: It is important to regenerate the engine to get the max detection speed based on pre-cluster-threshold you set.

NOTE: Lower topk values will result in more performance.

NOTE: Make sure to set cluster-mode=2 in the config_infer file.

INT8 calibration

1. Install OpenCV

sudo apt-get install libopencv-dev

2. Compile/recompile the nvdsinfer_custom_impl_Yolo lib with OpenCV support

  • DeepStream 6.1 on x86 platform

    CUDA_VER=11.6 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
    
  • DeepStream 6.0.1 / 6.0 on x86 platform

    CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
    
  • DeepStream 6.1 on Jetson platform

    CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
    
  • DeepStream 6.0.1 / 6.0 on Jetson platform

    CUDA_VER=10.2 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
    

3. For COCO dataset, download the val2017, extract, and move to DeepStream-Yolo folder

  • Select 1000 random images from COCO dataset to run calibration

    mkdir calibration
    
    for jpg in $(ls -1 val2017/*.jpg | sort -R | head -1000); do \
        cp ${jpg} calibration/; \
    done
    
  • Create the calibration.txt file with all selected images

    realpath calibration/*jpg > calibration.txt
    
  • Set environment variables

    export INT8_CALIB_IMG_PATH=calibration.txt
    export INT8_CALIB_BATCH_SIZE=1
    
  • Edit the config_infer file

    ...
    model-engine-file=model_b1_gpu0_fp32.engine
    #int8-calib-file=calib.table
    ...
    network-mode=0
    ...
    

    To

    ...
    model-engine-file=model_b1_gpu0_int8.engine
    int8-calib-file=calib.table
    ...
    network-mode=1
    ...
    
  • Run

    deepstream-app -c deepstream_app_config.txt
    

NOTE: NVIDIA recommends at least 500 images to get a good accuracy. On this example, I used 1000 images to get better accuracy (more images = more accuracy). Higher INT8_CALIB_BATCH_SIZE values will result in more accuracy and faster calibration speed. Set it according to you GPU memory. This process can take a long time.

Extract metadata

You can get metadata from DeepStream using Python and C/C++. For C/C++, you can edit the deepstream-app or deepstream-test codes. For Python, your can install and edit deepstream_python_apps.

Basically, you need manipulate the NvDsObjectMeta (Python / C/C++) and NvDsFrameMeta (Python / C/C++) to get the label, position, etc. of bboxes.

My projects: https://www.youtube.com/MarcosLucianoTV

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