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Add documentation for multiple models

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Marcos Luciano
2022-07-01 16:39:25 -03:00
parent f484a33648
commit 095696a296
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@@ -4,28 +4,29 @@ NVIDIA DeepStream SDK 6.1 / 6.0.1 / 6.0 configuration for YOLO models
### Future updates
* New documentation for multiple models
* Models benchmarks
* DeepStream tutorials
* Native YOLOX support
* Native PP-YOLO support
* YOLOX support
* PP-YOLO support
* YOLOv6 support
* Dynamic batch-size
### Improvements on this repository
* Darknet CFG params parser (no need to edit nvdsparsebbox_Yolo.cpp or another file)
* Support for new_coords, beta_nms and scale_x_y params
* Darknet cfg params parser (no need to edit `nvdsparsebbox_Yolo.cpp` or other files)
* Support for `new_coords`, `beta_nms` 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
* Support for reorg, implicit and channel layers (YOLOR)
* YOLOv5 4.0, 5.0, 6.0 and 6.1 native support
* YOLOR native support
* Models benchmarks (**outdated**)
* Support for `reorg`, `implicit` and `channel` layers (YOLOR)
* YOLOv5 4.0, 5.0, 6.0 and 6.1 support
* YOLOR support
* **GPU YOLO Decoder (moved from CPU to GPU to get better performance)** [#138](https://github.com/marcoslucianops/DeepStream-Yolo/issues/138)
* **GPU Batched NMS** [#142](https://github.com/marcoslucianops/DeepStream-Yolo/issues/142)
* **New documentation for multiple models**
##
@@ -41,6 +42,7 @@ NVIDIA DeepStream SDK 6.1 / 6.0.1 / 6.0 configuration for YOLO models
* [NMS configuration](#nms-configuration)
* [INT8 calibration](#int8-calibration)
* [Using your custom model](docs/customModels.md)
* [Multiple YOLO GIEs](docs/multipleGIEs.md)
##
@@ -125,7 +127,7 @@ sudo apt-get install libssl1.1 libgstreamer1.0-0 gstreamer1.0-tools gstreamer1.0
sudo apt-get install linux-headers-$(uname -r)
```
**NOTE**: Purge all NVIDIA driver, CUDA, etc (replace $CUDA_PATH to your CUDA path).
**NOTE**: Purge all NVIDIA driver, CUDA, etc (replace $CUDA_PATH to your CUDA path)
```
sudo nvidia-uninstall
@@ -149,35 +151,35 @@ sudo apt-get update
* 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
```
```
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
```
```
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
```
```
sudo sh NVIDIA-Linux-x86_64-510.47.03.run --silent --disable-nouveau --dkms --install-libglvnd
```
**NOTE**: This step will disable the nouveau drivers.
**NOTE**: This step will disable the nouveau drivers.
* Reboot
```
sudo reboot
```
```
sudo reboot
```
* Install
```
sudo sh NVIDIA-Linux-x86_64-510.47.03.run --silent --disable-nouveau --dkms --install-libglvnd
```
```
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
@@ -195,10 +197,9 @@ 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
```
```
echo $'export PATH=/usr/local/cuda-11.6/bin${PATH:+:${PATH}}\nexport LD_LIBRARY_PATH=/usr/local/cuda-11.6/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}' >> ~/.bashrc && source ~/.bashrc
```
#### 6. Download from [NVIDIA website](https://developer.nvidia.com/nvidia-tensorrt-8x-download) and install the TensorRT
@@ -256,13 +257,13 @@ sudo apt install libssl1.0.0 libgstreamer1.0-0 gstreamer1.0-tools gstreamer1.0-p
sudo apt-get install linux-headers-$(uname -r)
```
**NOTE**: Install DKMS only if you are using the default Ubuntu kernel.
**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).
**NOTE**: Purge all NVIDIA driver, CUDA, etc (replace $CUDA_PATH to your CUDA path)
```
sudo nvidia-uninstall
@@ -286,39 +287,39 @@ sudo apt-get update
* 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
```
```
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
```
```
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
```
```
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**: This step will disable the nouveau drivers.
**NOTE**: Remove --dkms flag if you installed the 5.11.0 kernel.
**NOTE**: Remove --dkms flag if you installed the 5.11.0 kernel.
* Reboot
```
sudo reboot
```
```
sudo reboot
```
* Install
```
sudo sh NVIDIA-Linux-x86_64-470.129.06.run --silent --disable-nouveau --dkms --install-libglvnd
```
```
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**: Remove --dkms flag if you installed the 5.11.0 kernel.
**NOTE**: If you are using a laptop with NVIDIA Optimius, run
@@ -336,10 +337,9 @@ 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
```
```
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](https://developer.nvidia.com/nvidia-tensorrt-8x-download) and install the TensorRT
@@ -356,22 +356,22 @@ sudo apt-get install libnvinfer8=8.0.1-1+cuda11.3 libnvinfer-plugin8=8.0.1-1+cud
* DeepStream 6.0.1 for Servers and Workstations (.deb)
```
sudo apt-get install ./deepstream-6.0_6.0.1-1_amd64.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
```
```
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
```
```
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
@@ -392,55 +392,41 @@ git clone https://github.com/marcoslucianops/DeepStream-Yolo.git
cd DeepStream-Yolo
```
#### 2. Download cfg and weights files from your model and move to DeepStream-Yolo folder
#### 2. Download the `cfg` and `weights` files from [Darknet](https://github.com/AlexeyAB/darknet) repo to the DeepStream-Yolo folder
#### 3. Compile lib
#### 3. Compile the lib
* DeepStream 6.1 on x86 platform
```
CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
```
```
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
```
```
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
```
```
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
```
```
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
```
#### 4. Edit config_infer_primary.txt for your model (example for YOLOv4)
#### 4. Edit the `config_infer_primary.txt` file according to your model (example for YOLOv4)
```
[property]
...
# 0=RGB, 1=BGR, 2=GRAYSCALE
model-color-format=0
# YOLO cfg
custom-network-config=yolov4.cfg
# YOLO weights
model-file=yolov4.weights
# Generated TensorRT model (will be created if it doesn't exist)
model-engine-file=model_b1_gpu0_fp32.engine
# Model labels file
labelfile-path=labels.txt
# Batch size
batch-size=1
# 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
# Number of classes in label file
num-detected-classes=80
...
```
@@ -450,15 +436,12 @@ num-detected-classes=80
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
**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
...
```
@@ -467,211 +450,179 @@ config-file=config_infer_primary_yoloV2.txt
### YOLOv5 usage
**NOTE**: Make sure to change the YOLOv5 repo version to your model version before conversion.
**NOTE**: Make sure to change the YOLOv5 repo version according to your model version before the conversion.
#### 1. Copy gen_wts_yoloV5.py from DeepStream-Yolo/utils to [ultralytics/yolov5](https://github.com/ultralytics/yolov5) folder
#### 1. Copy the `gen_wts_yoloV5.py` file from `DeepStream-Yolo/utils` directory to the [YOLOv5](https://github.com/ultralytics/yolov5) folder
#### 2. Open the ultralytics/yolov5 folder
#### 2. Open the YOLOv5 folder
#### 3. Download pt file from [ultralytics/yolov5](https://github.com/ultralytics/yolov5/releases/) website (example for YOLOv5n 6.1)
#### 3. Download the `pt` file from [YOLOv5](https://github.com/ultralytics/yolov5/releases/) repo (example for YOLOv5n 6.1)
```
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt
```
#### 4. Generate cfg and wts files (example for YOLOv5n)
#### 4. Generate the `cfg` and `wts` files (example for YOLOv5n)
```
python3 gen_wts_yoloV5.py -w yolov5n.pt -c models/yolov5n.yaml
```
#### 5. Copy generated cfg and wts files to DeepStream-Yolo folder
#### 5. Copy the generated `cfg` and `wts` files to the DeepStream-Yolo folder
#### 6. Open DeepStream-Yolo folder
#### 6. Open the DeepStream-Yolo folder
#### 7. Compile lib
#### 7. Compile the lib
* DeepStream 6.1 on x86 platform
```
CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
```
```
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
```
```
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
```
```
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
```
```
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
```
#### 8. Edit config_infer_primary_yoloV5.txt for your model (example for YOLOv5n)
#### 8. Edit the `config_infer_primary_yoloV5.txt` file according to your model (example for YOLOv5n)
```
[property]
...
# 0=RGB, 1=BGR, 2=GRAYSCALE
model-color-format=0
# CFG
custom-network-config=yolov5n.cfg
# WTS
model-file=yolov5n.wts
# Generated TensorRT model (will be created if it doesn't exist)
model-engine-file=model_b1_gpu0_fp32.engine
# Model labels file
labelfile-path=labels.txt
# Batch size
batch-size=1
# 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
# Number of classes in label file
num-detected-classes=80
...
```
#### 8. Change the deepstream_app_config.txt file
#### 9. Edit the `deepstream_app_config.txt` file
```
...
[primary-gie]
enable=1
gpu-id=0
gie-unique-id=1
nvbuf-memory-type=0
...
config-file=config_infer_primary_yoloV5.txt
```
#### 9. Run
#### 10. Run
```
deepstream-app -c deepstream_app_config.txt
```
**NOTE**: For YOLOv5 P6 or custom models, check the gen_wts_yoloV5.py args and use them according to your model
**NOTE**: For YOLOv5 P6, check the `gen_wts_yoloV5.py` file args and set them according to your model.
* Input weights (.pt) file path **(required)**
* Input weights (.pt) file path
```
-w or --weights
```
```
-w or --weights
```
* Input cfg (.yaml) file path
```
-c or --yaml
```
```
-c or --yaml
```
* Inference size [size] or [height , weight] **(default [640] / [1280] if --p6)**
* Inference size [size] or [height , weight]
```
-s or --size
```
Default: 640 / 1280 (if --p6)
Example for 1280
```
-s or --size
```
```
-s 1280
```
* Example for 1280
or
```
-s 1280
```
```
-s 1280 1280
```
or
```
-s 1280 1280
```
##
### YOLOR usage
#### 1. Copy gen_wts_yolor.py from DeepStream-Yolo/utils to [yolor](https://github.com/WongKinYiu/yolor) folder
#### 1. Copy the `gen_wts_yolor.py` file from `DeepStream-Yolo/utils` directory to the [YOLOR](https://github.com/WongKinYiu/yolor) folder
#### 2. Open the yolor folder
#### 2. Open the YOLOR folder
#### 3. Download pt file from [yolor](https://github.com/WongKinYiu/yolor) website
#### 3. Download the `pt` file from [YOLOR](https://github.com/WongKinYiu/yolor) repo
#### 4. Generate wts file (example for YOLOR-CSP)
#### 4. Generate the `cfg` and `wts` files (example for YOLOR-CSP)
```
python3 gen_wts_yolor.py -w yolor_csp.pt -c cfg/yolor_csp.cfg
```
#### 5. Copy generated cfg and wts files to DeepStream-Yolo folder
#### 5. Copy the generated `cfg` and `wts` files to the DeepStream-Yolo folder
#### 6. Open DeepStream-Yolo folder
#### 6. Open the DeepStream-Yolo folder
#### 7. Compile lib
#### 7. Compile the lib
* DeepStream 6.1 on x86 platform
```
CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
```
```
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
```
```
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
```
```
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
```
```
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
```
#### 8. Edit config_infer_primary_yolor.txt for your model (example for YOLOR-CSP)
#### 8. Edit the `config_infer_primary_yolor.txt` file according to your model (example for YOLOR-CSP)
```
[property]
...
# 0=RGB, 1=BGR, 2=GRAYSCALE
model-color-format=0
# CFG
custom-network-config=yolor_csp.cfg
# WTS
model-file=yolor_csp.wts
# Generated TensorRT model (will be created if it doesn't exist)
model-engine-file=model_b1_gpu0_fp32.engine
# Model labels file
labelfile-path=labels.txt
# Batch size
batch-size=1
# 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
# Number of classes in label file
num-detected-classes=80
...
```
#### 8. Change the deepstream_app_config.txt file
#### 9. Edit the `deepstream_app_config.txt` file
```
...
[primary-gie]
enable=1
gpu-id=0
gie-unique-id=1
nvbuf-memory-type=0
...
config-file=config_infer_primary_yolor.txt
```
#### 9. Run
#### 10. Run
```
deepstream-app -c deepstream_app_config.txt
@@ -683,12 +634,6 @@ deepstream-app -c deepstream_app_config.txt
To change the `iou-threshold`, `score-threshold` and `topk` values, modify the `config_nms.txt` file and regenerate the model engine file.
**NOTE**: Lower `topk` values will result in more performance.
**NOTE**: Make sure to set cluster-mode=4 in config_infer file.
**NOTE**: You are still able to change the `pre-cluster-threshold` values in the `config_infer.txt` file.
```
[property]
iou-threshold=0.45
@@ -696,6 +641,12 @@ score-threshold=0.25
topk=300
```
**NOTE**: Lower `topk` values will result in more performance.
**NOTE**: Make sure to set `cluster-mode=4` in the config_infer file.
**NOTE**: You are still able to change the `pre-cluster-threshold` values in the config_infer files.
##
### INT8 calibration
@@ -706,100 +657,96 @@ topk=300
sudo apt-get install libopencv-dev
```
#### 2. Compile/recompile the nvdsinfer_custom_impl_Yolo lib with OpenCV support
#### 2. Compile/recompile the `nvdsinfer_custom_impl_Yolo` lib with OpenCV support
* DeepStream 6.1 on x86 platform
```
cd DeepStream-Yolo
CUDA_VER=11.6 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
```
```
CUDA_VER=11.6 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.0.1 / 6.0 on x86 platform
```
cd DeepStream-Yolo
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
```
```
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.1 on Jetson platform
```
cd DeepStream-Yolo
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
```
```
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.0.1 / 6.0 on Jetson platform
```
cd DeepStream-Yolo
CUDA_VER=10.2 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
```
```
CUDA_VER=10.2 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
```
#### 3. For COCO dataset, download the [val2017](https://drive.google.com/file/d/1gbvfn7mcsGDRZ_luJwtITL-ru2kK99aK/view?usp=sharing), extract, and move to DeepStream-Yolo folder
##### Select 1000 random images from COCO dataset to run calibration
* Select 1000 random images from COCO dataset to run calibration
```
mkdir calibration
```
```
mkdir calibration
```
```
for jpg in $(ls -1 val2017/*.jpg | sort -R | head -1000); do \
cp ${jpg} calibration/; \
done
```
```
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
* Create the `calibration.txt` file with all selected images
```
realpath calibration/*jpg > calibration.txt
```
```
realpath calibration/*jpg > calibration.txt
```
##### Set environment variables
* Set environment variables
```
export INT8_CALIB_IMG_PATH=calibration.txt
export INT8_CALIB_BATCH_SIZE=1
```
```
export INT8_CALIB_IMG_PATH=calibration.txt
export INT8_CALIB_BATCH_SIZE=1
```
##### Change config_infer_primary.txt file
* Edit the `config_infer` file
```
...
model-engine-file=model_b1_gpu0_fp32.engine
#int8-calib-file=calib.table
...
network-mode=0
...
```
```
...
model-engine-file=model_b1_gpu0_fp32.engine
#int8-calib-file=calib.table
...
network-mode=0
...
```
* To
To
```
...
model-engine-file=model_b1_gpu0_int8.engine
int8-calib-file=calib.table
...
network-mode=1
...
```
```
...
model-engine-file=model_b1_gpu0_int8.engine
int8-calib-file=calib.table
...
network-mode=1
...
```
##### Run
* Run
```
deepstream-app -c deepstream_app_config.txt
```
```
deepstream-app -c deepstream_app_config.txt
```
**NOTE**: NVIDIA recommends at least 500 images to get a good accuracy. In this example I used 1000 images to get better accuracy (more images = more accuracy). Higher INT8_CALIB_BATCH_SIZE values will increase the accuracy and calibration speed. Set it according to you GPU memory. This process can take a long time.
**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 in Python and C/C++. For C/C++, you need edit deepstream-app or deepstream-test code. For Python your need install and edit [deepstream_python_apps](https://github.com/NVIDIA-AI-IOT/deepstream_python_apps).
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](https://github.com/NVIDIA-AI-IOT/deepstream_python_apps).
Basically, you need manipulate NvDsObjectMeta ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvDsMeta/NvDsObjectMeta.html)/[C/C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/struct__NvDsObjectMeta.html)) and NvDsFrameMeta ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvDsMeta/NvDsFrameMeta.html)/[C/C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/struct__NvDsFrameMeta.html)) to get label, position, etc. of bboxes.
Basically, you need manipulate the `NvDsObjectMeta` ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvDsMeta/NvDsObjectMeta.html) / [C/C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/struct__NvDsObjectMeta.html)) `and NvDsFrameMeta` ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvDsMeta/NvDsFrameMeta.html) / [C/C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/struct__NvDsFrameMeta.html)) to get the label, position, etc. of bboxes.
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