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Comparison of Benchmark Test Performance on NVIDIA® Jetson™ AGX Xavier™, Jetson™ Xavier™ NX and Jetson™ Nano™

WHAT YOU WILL LEARN?

1- Cloning the test repository

2- Setting up the benchmark test

3- Testing the performance in each device




ENVIRONMENT

Hardware 1: Jetson AGX Xavier Developer Kit

Hardware 2: DSBOX-NX2

Hardware 3: DSBOX-N2

OS 1: JetPack 4.4 (L4T-32.4.3)

OS 2: JetPack 4.5 (L4T-32.5)

OS 3: JetPack 4.5 (L4T-32.5)

Cloning The Test Repository


First, let's set our test environment from github:


git clone https://github.com/NVIDIA-AI-IOT/jetson_benchmarks.git
cd jetson_benchmarks
mkdir models
sudo sh install_requirements.sh


Benchmark Test Setup



Now let's download all model files:


For AGX Xavier:


python3 utils/download_models.py --all --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --save_dir ${PWD}/models


For Xavier NX:


python3 utils/download_models.py --all --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --save_dir ${PWD}/models


For Nano:


python3 utils/download_models.py --all --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --save_dir ${PWD}/models


Performance Test in Each Device


Jetson AGX Xavier Test Results


Let's test all models one by one:


sudo python3 benchmark.py --model_name inception_v4 --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0



sudo python3 benchmark.py --model_name vgg19 --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0



sudo python3 benchmark.py --model_name super_resolution --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0



sudo python3 benchmark.py --model_name unet --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0



sudo python3 benchmark.py --model_name pose_estimation --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0



sudo python3 benchmark.py --model_name tiny-yolov3 --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0



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sudo python3 benchmark.py --model_name ssd-mobilenet-v1 --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0


Jetson Xavier NX Test Results


Let's test all models one by one:


sudo python3 benchmark.py --model_name inception_v4 --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode



sudo python3 benchmark.py --model_name vgg19 --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2



sudo python3 benchmark.py --model_name super_resolution --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2



sudo python3 benchmark.py --model_name unet --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2



sudo python3 benchmark.py --model_name pose_estimation --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2



sudo python3 benchmark.py --model_name tiny-yolov3 --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2



sudo python3 benchmark.py --model_name resnet --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2



sudo python3 benchmark.py --model_name ssd-mobilenet-v1 --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2

Jetson Nano Test Results


Let's test all models one by one (with fp16 model):


sudo python3 benchmark.py --model_name inception_v4 --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16



sudo python3 benchmark.py --model_name vgg19 --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16



sudo python3 benchmark.py --model_name super_resolution --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16



sudo python3 benchmark.py --model_name unet --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16



sudo python3 benchmark.py --model_name pose_estimation --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16



sudo python3 benchmark.py --model_name tiny-yolov3 --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16



sudo python3 benchmark.py --model_name tiny-yolov3 --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16



sudo python3 benchmark.py --model_name ssd-mobilenet-v1 --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16


Comparison of Results


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