105 lines
2.1 KiB
Markdown
105 lines
2.1 KiB
Markdown
# INT8 calibration (PTQ)
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### 1. Install OpenCV
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```
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sudo apt-get install libopencv-dev
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```
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### 2. Compile/recompile the `nvdsinfer_custom_impl_Yolo` lib with OpenCV support
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2.1. Set the `CUDA_VER` according to your DeepStream version
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```
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export CUDA_VER=XY.Z
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```
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* x86 platform
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```
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DeepStream 7.0 / 6.4 = 12.2
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DeepStream 6.3 = 12.1
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DeepStream 6.2 = 11.8
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DeepStream 6.1.1 = 11.7
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DeepStream 6.1 = 11.6
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DeepStream 6.0.1 / 6.0 = 11.4
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DeepStream 5.1 = 11.1
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```
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* Jetson platform
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```
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DeepStream 7.0 / 6.4 = 12.2
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DeepStream 6.3 / 6.2 / 6.1.1 / 6.1 = 11.4
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DeepStream 6.0.1 / 6.0 / 5.1 = 10.2
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```
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2.2. Set the `OPENCV` env
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```
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export OPENCV=1
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```
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2.3. Make the lib
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```
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make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo
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```
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### 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
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* Select 1000 random images from COCO dataset to run calibration
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```
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mkdir calibration
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```
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```
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for jpg in $(ls -1 val2017/*.jpg | sort -R | head -1000); do \
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cp ${jpg} calibration/; \
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done
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```
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* Create the `calibration.txt` file with all selected images
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```
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realpath calibration/*jpg > calibration.txt
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```
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* Set environment variables
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```
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export INT8_CALIB_IMG_PATH=calibration.txt
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export INT8_CALIB_BATCH_SIZE=1
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```
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* Edit the `config_infer` file
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```
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...
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model-engine-file=model_b1_gpu0_fp32.engine
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#int8-calib-file=calib.table
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...
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network-mode=0
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...
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```
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To
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```
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...
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model-engine-file=model_b1_gpu0_int8.engine
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int8-calib-file=calib.table
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...
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network-mode=1
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...
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```
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* Run
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```
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deepstream-app -c deepstream_app_config.txt
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```
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**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.
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