Comparison of Benchmark Test Performance on Nvidia Jetson AGX Xavier,

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

Thank you for reading our blog post.

Comparison of Benchmark Test Performance on Nvidia Jetson AGX Xavier, Jetson Xavier NX and Jetson Nano

Jetson AGX Xavier | Jetson Nano | Jetson Xavier NX