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How to use custom models on deepstream-app

Directory tree

1. Download the repo

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

2. Copy the class names file to DeepStream-Yolo folder and remane it to labels.txt

3. Copy the onnx or cfg and weights files to DeepStream-Yolo folder

Compile the lib

  1. Set the CUDA_VER according to your DeepStream version
export CUDA_VER=XY.Z

  • x86 platform

    DeepStream 7.1 = 12.6
DeepStream 7.0 / 6.4 = 12.2
DeepStream 6.3 = 12.1
DeepStream 6.2 = 11.8
DeepStream 6.1.1 = 11.7
DeepStream 6.1 = 11.6
DeepStream 6.0.1 / 6.0 = 11.4
DeepStream 5.1 = 11.1

  • Jetson platform

    DeepStream 7.1 = 12.6
DeepStream 7.0 / 6.4 = 12.2
DeepStream 6.3 / 6.2 / 6.1.1 / 6.1 = 11.4
DeepStream 6.0.1 / 6.0 / 5.1 = 10.2
  1. Make the lib
make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo

Understanding and editing deepstream_app_config file

To understand and edit deepstream_app_config.txt file, read the DeepStream Reference Application - Configuration Groups

  • tiled-display

    [tiled-display]
enable=1
# If you have 1 stream use 1/1 (rows/columns), if you have 4 streams use 2/2 or 4/1 or 1/4 (rows/columns)
rows=1
columns=1
# Resolution of tiled display
width=1280
height=720
gpu-id=0
nvbuf-memory-type=0

  • source

    • Example for 1 source:

      [source0]
enable=1
# 1=Camera (V4L2), 2=URI, 3=MultiURI, 4=RTSP, 5=Camera (CSI; Jetson only)
type=3
# Stream URL
uri=rtsp://192.168.1.2/Streaming/Channels/101/httppreview
# Number of sources copy (if > 1, edit rows/columns in tiled-display section; use type=3 for more than 1 source)
num-sources=1
gpu-id=0
cudadec-memtype=0

    • Example for 1 duplcated source:

      [source0]
enable=1
type=3
uri=rtsp://192.168.1.2/Streaming/Channels/101/
num-sources=2
gpu-id=0
cudadec-memtype=0

    • Example for 2 sources:

      [source0]
enable=1
type=3
uri=rtsp://192.168.1.2/Streaming/Channels/101/
num-sources=1
gpu-id=0
cudadec-memtype=0

[source1]
enable=1
type=3
uri=rtsp://192.168.1.3/Streaming/Channels/101/
num-sources=1
gpu-id=0
cudadec-memtype=0

  • sink

    [sink0]
enable=1
# 1=Fakesink, 2=EGL (nveglglessink), 3=Filesink, 4=RTSP, 5=Overlay (Jetson only)
type=2
# Indicates how fast the stream is to be rendered (0=As fast as possible, 1=Synchronously)
sync=0
gpu-id=0
nvbuf-memory-type=0

  • streammux

    [streammux]
gpu-id=0
# Boolean property to inform muxer that sources are live
live-source=1
batch-size=1
batched-push-timeout=40000
# Resolution of streammux
width=1920
height=1080
enable-padding=0
nvbuf-memory-type=0

  • primary-gie

    [primary-gie]
enable=1
gpu-id=0
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary.txt

    NOTE: Edit the config-file according to your YOLO model.

Understanding and editing config_infer_primary file

To understand and edit config_infer_primary.txt file, read the DeepStream Plugin Guide - Gst-nvinfer File Configuration Specifications

  • model-color-format

    # 0=RGB, 1=BGR, 2=GRAYSCALE
model-color-format=0

    NOTE: Set it according to the number of channels in the cfg file (1=GRAYSCALE, 3=RGB for Darknet YOLO) or your model configuration (ONNX).

  • custom-network-config and model-file (Darknet YOLO)

    • Example for custom YOLOv4 model

      custom-network-config=yolov4_custom.cfg
model-file=yolov4_custom.weights

  • onnx-file (ONNX)

    • Example for custom YOLOv8 model

      onnx-file=yolov8s_custom.onnx

  • model-engine-file

    • Example for batch-size=1 and network-mode=2

      model-engine-file=model_b1_gpu0_fp16.engine

    • Example for batch-size=1 and network-mode=1

      model-engine-file=model_b1_gpu0_int8.engine

    • Example for batch-size=1 and network-mode=0

      model-engine-file=model_b1_gpu0_fp32.engine

    • Example for batch-size=2 and network-mode=0

      model-engine-file=model_b2_gpu0_fp32.engine

    NOTE: To change the generated engine filename (Darknet YOLO), you need to edit and rebuild the nvdsinfer_model_builder.cpp file (/opt/nvidia/deepstream/deepstream/sources/libs/nvdsinfer/nvdsinfer_model_builder.cpp, lines 825-827)

    suggestedPathName =
    modelPath + "_b" + std::to_string(initParams.maxBatchSize) + "_" +
    devId + "_" + networkMode2Str(networkMode) + ".engine";

  • batch-size

    batch-size=1

  • network-mode

    # 0=FP32, 1=INT8, 2=FP16
network-mode=0

  • num-detected-classes

    num-detected-classes=80

    NOTE: Set it according to number of classes in cfg file (Darknet YOLO) or your model configuration (ONNX).

  • interval

    # Number of consecutive batches to be skipped
interval=0

Testing the model

deepstream-app -c deepstream_app_config.txt