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
Suported models
Benchmarks
dGPU installation
Basic usage
Docker usage
NMS configuration
INT8 calibration
YOLOv5 usage
YOLOR usage
PP-YOLOE usage
YOLOv7 usage
Using your custom model
Multiple YOLO GIEs

Requirements

DeepStream 6.1 on x86 platform

Ubuntu 20.04
CUDA 11.6 Update 1
TensorRT 8.2 GA Update 4 (8.2.5.1)
NVIDIA Driver 510.47.03
NVIDIA DeepStream SDK 6.1
GStreamer 1.16.2
DeepStream-Yolo

DeepStream 6.0.1 / 6.0 on x86 platform

Ubuntu 18.04
CUDA 11.4 Update 1
TensorRT 8.0 GA (8.0.1)
NVIDIA Driver >= 470.63.01
NVIDIA DeepStream SDK 6.0.1 / 6.0
GStreamer 1.14.5
DeepStream-Yolo

DeepStream 6.1 on Jetson platform

JetPack 5.0.1 DP
NVIDIA DeepStream SDK 6.1
DeepStream-Yolo

DeepStream 6.0.1 / 6.0 on Jetson platform

JetPack 4.6.1
NVIDIA DeepStream SDK 6.0.1 / 6.0
DeepStream-Yolo

Suported models

Darknet YOLO
YOLOv5 >= 2.0
YOLOR
PP-YOLOE
YOLOv7
MobileNet-YOLO
YOLO-Fastest

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

Files

README.md

Latest commit

History

README.md

File metadata and controls

DeepStream-Yolo

Future updates

Improvements on this repository

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

NMS config

Results

dGPU installation

1. Disable Secure Boot in BIOS

2. Install dependencies

3. Install CUDA Keyring

4. Download and install NVIDIA Driver

5. Download and install CUDA

6. Download from NVIDIA website and install the TensorRT

7. Download from NVIDIA website and install the DeepStream SDK

8. Reboot the computer

1. Disable Secure Boot in BIOS

2. Install dependencies

3. Install CUDA Keyring

4. Download and install NVIDIA Driver

5. Download and install CUDA

6. Download from NVIDIA website and install the TensorRT

7. Download from NVIDIA website and install the DeepStream SDK

8. Reboot the computer

Basic usage

1. Download the repo

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

3. Compile the lib

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

5. Run

Docker usage

NMS Configuration

INT8 calibration

1. Install OpenCV

2. Compile/recompile the nvdsinfer_custom_impl_Yolo lib with OpenCV support

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

Extract metadata

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

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

2. Compile/recompile the `nvdsinfer_custom_impl_Yolo` lib with OpenCV support