deepstream_yolo/docs/INT8Calibration.md

INT8 calibration (PTQ)

1. Install OpenCV

sudo apt-get install libopencv-dev

2. Compile/recompile the nvdsinfer_custom_impl_Yolo lib with OpenCV support

2.1. Set the CUDA_VER according to your DeepStream version

export CUDA_VER=XY.Z

• x86 platform

  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 7.0 / 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
  

2.2. Set the OPENCV env

export OPENCV=1

2.3. Make the lib

make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo

3. For COCO dataset, download the val2017, extract, and move to DeepStream-Yolo folder

• Select 1000 random images from COCO dataset to run calibration

  mkdir calibration
  

  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

  realpath calibration/*jpg > calibration.txt
  

• Set environment variables

  export INT8_CALIB_IMG_PATH=calibration.txt
  export INT8_CALIB_BATCH_SIZE=1
  

• Edit the config_infer file

  ...
  model-engine-file=model_b1_gpu0_fp32.engine
  #int8-calib-file=calib.table
  ...
  network-mode=0
  ...
  

  To

  ...
  model-engine-file=model_b1_gpu0_int8.engine
  int8-calib-file=calib.table
  ...
  network-mode=1
  ...
  

• Run

  deepstream-app -c deepstream_app_config.txt
  

NOTE: NVIDIA recommends at least 500 images to get a good accuracy. On this example, I recommend to use 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 may take a long time.