NVIDIA DeepStream SDK 6.2 / 6.1.1 / 6.1 / 6.0.1 / 6.0 / 5.1 configuration for YOLO models

• DeepStream tutorials
• Updated INT8 calibration
• Support for segmentation models
• Support for classification models

• Support for INT8 calibration
• Support for non square models
• Models benchmarks
• Support for Darknet models (YOLOv4, etc) using cfg and weights conversion with GPU post-processing
• Support for YOLO-NAS, PPYOLOE+, PPYOLOE, DAMO-YOLO, YOLOX, YOLOR, YOLOv8, YOLOv7, YOLOv6 and YOLOv5 using ONNX conversion with GPU post-processing
• GPU bbox parser (it is slightly slower than CPU bbox parser on V100 GPU tests)
• Support for DeepStream 5.1
• Custom ONNX model parser (NvDsInferYoloCudaEngineGet)
• Dynamic batch-size for Darknet and ONNX exported models
• INT8 calibration (PTQ) for Darknet and ONNX exported models
• New output structure (fix wrong output on DeepStream < 6.2) - it need to export the ONNX model with the new export file, generate the TensorRT engine again with the updated files, and use the new config_infer_primary file according to your model

• Requirements
• Suported models
• Benchmarks
• dGPU installation
• Basic usage
• Docker usage
• NMS configuration
• INT8 calibration
• YOLOv5 usage
• YOLOv6 usage
• YOLOv7 usage
• YOLOv8 usage
• YOLOR usage
• YOLOX usage
• DAMO-YOLO usage
• PP-YOLOE / PP-YOLOE+ usage
• YOLO-NAS usage
• Using your custom model
• Multiple YOLO GIEs

• Ubuntu 20.04
• CUDA 11.8
• TensorRT 8.5 GA Update 1 (8.5.2.2)
• NVIDIA Driver 525.85.12 (Data center / Tesla series) / 525.105.17 (TITAN, GeForce RTX / GTX series and RTX / Quadro series)
• NVIDIA DeepStream SDK 6.2
• GStreamer 1.16.3
• DeepStream-Yolo

• Ubuntu 20.04
• CUDA 11.7 Update 1
• TensorRT 8.4 GA (8.4.1.5)
• NVIDIA Driver 515.65.01
• NVIDIA DeepStream SDK 6.1.1
• GStreamer 1.16.2
• DeepStream-Yolo

• 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

• 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

• Ubuntu 18.04
• CUDA 11.1
• TensorRT 7.2.2
• NVIDIA Driver 460.32.03
• NVIDIA DeepStream SDK 5.1
• GStreamer 1.14.5
• DeepStream-Yolo

• JetPack 5.1.1 / 5.1
• NVIDIA DeepStream SDK 6.2
• DeepStream-Yolo

• JetPack 5.0.2
• NVIDIA DeepStream SDK 6.1.1
• DeepStream-Yolo

• JetPack 5.0.1 DP
• NVIDIA DeepStream SDK 6.1
• DeepStream-Yolo

• JetPack 4.6.2
• NVIDIA DeepStream SDK 6.0.1 / 6.0
• DeepStream-Yolo

• JetPack 4.5.1
• NVIDIA DeepStream SDK 5.1
• DeepStream-Yolo

• Darknet
• MobileNet-YOLO
• YOLO-Fastest
• YOLOv5
• YOLOv6
• YOLOv7
• YOLOv8
• YOLOR
• YOLOX
• DAMO-YOLO
• PP-YOLOE / PP-YOLOE+
• YOLO-NAS

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

• DeepStream 6.2 on x86 platform

  CUDA_VER=11.8 make -C nvdsinfer_custom_impl_Yolo
  

• DeepStream 6.1.1 on x86 platform

  CUDA_VER=11.7 make -C nvdsinfer_custom_impl_Yolo
  

• 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 5.1 on x86 platform

  CUDA_VER=11.1 make -C nvdsinfer_custom_impl_Yolo
  

• DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
  

• DeepStream 6.0.1 / 6.0 / 5.1 on Jetson platform

  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  

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

NOTE: By default, the dynamic batch-size is set. To use implicit batch-size, uncomment the line

...
force-implicit-batch-dim=1
...

deepstream-app -c deepstream_app_config.txt

NOTE: The TensorRT engine file may take a very long time to generate (sometimes more than 10 minutes).

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
...

• x86 platform

  nvcr.io/nvidia/deepstream:6.2-devel
  nvcr.io/nvidia/deepstream:6.2-triton
  

• Jetson platform

  nvcr.io/nvidia/deepstream-l4t:6.2-samples
  nvcr.io/nvidia/deepstream-l4t:6.2-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 6.2, the docker containers do not package libraries necessary for certain multimedia operations like audio data parsing, CPU decode, and CPU encode. This change could affect processing certain video streams/files like mp4 that include audio track. Please run the below script inside the docker images to install additional packages that might be necessary to use all of the DeepStreamSDK features:

  /opt/nvidia/deepstream/deepstream/user_additional_install.sh
  

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

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

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

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