Add custom models doc
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@@ -23,7 +23,6 @@ cudadec-memtype=0
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enable=1
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type=2
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sync=0
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source-id=0
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gpu-id=0
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nvbuf-memory-type=0
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docs/customModels.md
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docs/customModels.md
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# How to use custom models in DeepStream
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* [Requirements](#requirements)
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* [Editing files](#editing-files)
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* [Compile lib](#compile-lib)
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* [Understanding and editing deepstream_app_config](#understanding-and-editing-deepstream_app_config)
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* [Understanding and editing config_infer_primary](#understanding-and-editing-config_infer_primary)
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* [Testing model](#testing-model)
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##
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### Requirements
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* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
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* [Pre-treined YOLO model](https://github.com/AlexeyAB/darknet)
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##
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### Editing files
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#### 1. Download the repo
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```
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git clone https://github.com/marcoslucianops/DeepStream-Yolo.git
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cd DeepStream-Yolo
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```
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#### 2. Remane the obj.names file to labels.txt and copy it to DeepStream-Yolo directory
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#### 3. Copy the yolo.cfg and yolo.weights files to DeepStream-Yolo directory
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#### 4. Edit config_infer_primary.txt for your model
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```
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[property]
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...
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# CFG
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custom-network-config=yolo.cfg
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# Weights
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model-file=yolo.weights
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# Model labels file
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labelfile-path=labels.txt
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...
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```
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**NOTE**: If you want to use YOLOv2 or YOLOv2-Tiny models, change the deepstream_app_config.txt file before run it
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```
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[primary-gie]
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enable=1
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gpu-id=0
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gie-unique-id=1
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nvbuf-memory-type=0
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config-file=config_infer_primary_yoloV2.txt
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```
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**NOTE**: The config_infer_primary.txt file uses cluster-mode=4 and NMS = 0.45 (via code) when beta_nms isn't available (when beta_nms is available, NMS = beta_nms), while the config_infer_primary_yoloV2.txt file uses cluster-mode=2 and nms-iou-threshold=0.45 to set NMS.
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##
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### Compile lib
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* x86 platform
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```
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CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
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```
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* Jetson platform
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```
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CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
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```
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##
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### Understanding and editing deepstream_app_config
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To understand and edit deepstream_app_config.txt file, read the [DeepStream Reference Application - Configuration Groups](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_ref_app_deepstream.html#configuration-groups)
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##
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#### tiled-display
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```
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[tiled-display]
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enable=1
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# 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)
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rows=1
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columns=1
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# Resolution of tiled display
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width=1280
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height=720
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gpu-id=0
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nvbuf-memory-type=0
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```
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##
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#### source
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* Example for 1 source:
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```
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[source0]
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enable=1
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# 1=Camera (V4L2), 2=URI, 3=MultiURI, 4=RTSP, 5=Camera (CSI; Jetson only)
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type=3
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# Stream URL
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uri=rtsp://192.168.1.2/Streaming/Channels/101/httppreview
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# Number of sources copy (if > 1, edit rows/columns in tiled-display section; use type=3 for more than 1 source)
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num-sources=1
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gpu-id=0
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cudadec-memtype=0
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```
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* Example for 1 duplcated source:
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```
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[source0]
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enable=1
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type=3
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uri=rtsp://192.168.1.2/Streaming/Channels/101/
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num-sources=2
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gpu-id=0
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cudadec-memtype=0
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```
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* Example for 2 sources:
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```
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[source0]
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enable=1
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type=3
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uri=rtsp://192.168.1.2/Streaming/Channels/101/
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num-sources=1
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gpu-id=0
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cudadec-memtype=0
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[source1]
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enable=1
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type=3
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uri=rtsp://192.168.1.3/Streaming/Channels/101/
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num-sources=1
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gpu-id=0
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cudadec-memtype=0
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```
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##
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#### sink
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```
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[sink0]
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enable=1
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# 1=Fakesink, 2=EGL (nveglglessink), 3=Filesink, 4=RTSP, 5=Overlay (Jetson only)
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type=2
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# Indicates how fast the stream is to be rendered (0=As fast as possible, 1=Synchronously)
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sync=0
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gpu-id=0
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nvbuf-memory-type=0
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```
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##
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#### streammux
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```
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[streammux]
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gpu-id=0
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# Boolean property to inform muxer that sources are live
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live-source=1
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batch-size=1
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batched-push-timeout=40000
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# Resolution of streammux
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width=1920
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height=1080
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enable-padding=0
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nvbuf-memory-type=0
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```
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##
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#### primary-gie
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```
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[primary-gie]
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enable=1
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gpu-id=0
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gie-unique-id=1
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nvbuf-memory-type=0
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config-file=config_infer_primary.txt
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```
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##
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### Understanding and editing config_infer_primary
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To understand and edit config_infer_primary.txt file, read the [DeepStream Plugin Guide - Gst-nvinfer File Configuration Specifications](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvinfer.html#gst-nvinfer-file-configuration-specifications)
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##
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#### model-color-format
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```
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# 0=RGB, 1=BGR, 2=GRAYSCALE
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model-color-format=0
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```
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**NOTE**: Set it accoding to number of channels in yolo.cfg file (1=GRAYSCALE, 3=RGB)
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##
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#### model-engine-file
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* Example for batch-size=1 and network-mode=2
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```
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model-engine-file=model_b1_gpu0_fp16.engine
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```
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* Example for batch-size=1 and network-mode=0
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```
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model-engine-file=model_b1_gpu0_fp32.engine
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```
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* Example for batch-size=2 and network-mode=0
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```
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model-engine-file=model_b2_gpu0_fp32.engine
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```
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##
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#### batch-size
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```
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batch-size=1
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```
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##
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#### network-mode
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```
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# 0=FP32, 1=INT8, 2=FP16
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network-mode=0
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```
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##
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#### num-detected-classes
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```
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num-detected-classes=80
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```
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**NOTE**: Set it according to number of classes in yolo.cfg file
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##
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#### network-type
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```
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# 0=Detector, 1=Classifier, 2=Segmentation
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network-type=0
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```
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##
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#### interval
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```
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# Number of consecutive batches to be skipped
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interval=0
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```
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##
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#### pre-cluster-threshold
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```
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[class-attrs-all]
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# CONF_THRESH
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pre-cluster-threshold=0.25
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```
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##
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### Testing model
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```
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deepstream-app -c deepstream_app_config.txt
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```
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@@ -4,7 +4,6 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
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### Future updates (comming soon, stay tuned)
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* New documentation for custom model
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* New documentation for multiple models
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* DeepStream tutorials
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* Native PyTorch support (YOLOv5 and YOLOR)
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@@ -33,6 +32,7 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
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* [dGPU installation](#dgpu-installation)
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* [Basic usage](#basic-usage)
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* [INT8 calibration](#int8-calibration)
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* [Using your custom model](blob/master/docs/customModels.md)
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##
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@@ -213,7 +213,7 @@ cd DeepStream-Yolo
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#### 2. Download cfg and weights files from your model and move to DeepStream-Yolo folder
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#### 3. Compile
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||||
#### 3. Compile lib
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* x86 platform
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