sudo apt-get install libopencv-dev
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DeepStream 6.3 on x86 platform
CUDA_VER=12.1 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
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DeepStream 6.2 on x86 platform
CUDA_VER=11.8 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
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DeepStream 6.1.1 on x86 platform
CUDA_VER=11.7 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
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DeepStream 6.1 on x86 platform
CUDA_VER=11.6 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
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DeepStream 6.0.1 / 6.0 on x86 platform
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
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DeepStream 5.1 on x86 platform
CUDA_VER=11.1 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
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DeepStream 6.3 / 6.2 / 6.1.1 / 6.1 on Jetson platform
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
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DeepStream 6.0.1 / 6.0 / 5.1 on Jetson platform
CUDA_VER=10.2 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
3. For COCO dataset, download the val2017, extract, and move to DeepStream-Yolo folder
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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
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Create the
calibration.txt
file with all selected imagesrealpath calibration/*jpg > calibration.txt
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Set environment variables
export INT8_CALIB_IMG_PATH=calibration.txt export INT8_CALIB_BATCH_SIZE=1
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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 ...
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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.