# How to use custom models in DeepStream

* [Requirements](#requirements)
* [Editing files](#editing-files)
* [Compile lib](#compile-lib)
* [Understanding and editing deepstream_app_config](#understanding-and-editing-deepstream_app_config)
* [Understanding and editing config_infer_primary](#understanding-and-editing-config_infer_primary)
* [Testing model](#testing-model)

##

### Requirements

* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
* [Pre-treined YOLO model](https://github.com/AlexeyAB/darknet)

##

### Editing files

#### 1. Download the repo

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

#### 2. Remane the obj.names file to labels.txt and copy it to DeepStream-Yolo directory

#### 3. Copy the yolo.cfg and yolo.weights files to DeepStream-Yolo directory

#### 4. Edit config_infer_primary.txt for your model

```
[property]
...
# CFG
custom-network-config=yolo.cfg
# Weights
model-file=yolo.weights
# Model labels file
labelfile-path=labels.txt
...
```

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

```
[primary-gie]
enable=1
gpu-id=0
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary_yoloV2.txt
```

**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.

##

### Compile lib

* x86 platform

```
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
```

* Jetson platform

```
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
```

##

### Understanding and editing deepstream_app_config

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)

##

#### 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
```

##

### Understanding and editing config_infer_primary

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)

##

#### model-color-format

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

**NOTE**: Set it accoding to number of channels in yolo.cfg file (1=GRAYSCALE, 3=RGB)

##

#### 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=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
```

##

#### 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 yolo.cfg file

##

#### network-type

```
# 0=Detector, 1=Classifier, 2=Segmentation
network-type=0
```

##

#### interval

```
# Number of consecutive batches to be skipped
interval=0
```

##

#### pre-cluster-threshold

```
[class-attrs-all]
# CONF_THRESH
pre-cluster-threshold=0.25
```

##

### Testing model

```
deepstream-app -c deepstream_app_config.txt
```