Tensorflow realtime_object_detection on Jetson TX2/TX1

About this repository

forked from GustavZ/realtime_object_detection: https://github.com/GustavZ/realtime_object_detection

Getting Started:

• login Jetson TX2 ssh -C -Y [email protected]
• run python object_detection.py realtime object detection (Multi-Thread)
• run python realtime_object_detection.py realtime object detection (Multi-Process)
• wait few minuts.
• Multi-Thread is better performance. Multi-Process bottleneck is interprocess communication.

My Setup:

• Jetson TX2
  • JetPack 3.2
    • Python 3.6
    • OpenCV 3.4.1/Tensorflow 1.6.0
    • OpenCV 3.4.1/Tensorflow 1.7.0
  • JetPack 3.1
    • Python 3.6
    • OpenCV 3.3.1/Tensorflow 1.4.1 (Main)
    • OpenCV 3.4.0/Tensorflow 1.5.0
    • OpenCV 3.4.0/Tensorflow 1.6.0
• Jetson TX1
  • JetPack 3.2
    • Python 3.6
    • OpenCV 3.4.1/Tensorflow 1.6.0

NVPMODEL

Mode	Mode Name	Denver 2	Frequency	ARM A57	Frequency	GPU Frequency
0	Max-N	2	2.0 GHz	4	2.0 GHz	1.30 GHz
1	Max-Q	0		4	1.2 GHz	0.85 GHz
2	Max-P Core-All	2	1.4 GHz	4	1.4 GHz	1.12 GHz
3	Max-P ARM	0		4	2.0 GHz	1.12 GHz
4	Max-P Denver	2	2.0 GHz	0		1.12 GHz

Max-N

sudo nvpmodel -m 0
sudo ./jetson_clocks.sh

Max-P ARM(Default)

sudo nvpmodel -m 3
sudo ./jetson_clocks.sh

Show current mode

sudo nvpmodel -q --verbose

Current max Performance on ssd_mobilenet (with visualization 160x120):

FPS	Multi	Mode	CPU	Watt	Ampere	Volt-Ampere	Model	classes
40	Multi-Thread	Max-N	27-55%	15.6W	0.27A	27.8VA	roadsign_frozen_inference_graph_v1_2nd_4k.pb	4
36	Multi-Thread	Max-P ARM	50-59%	12.1W	0.21A	21.9VA	roadsign_frozen_inference_graph_v1_2nd_4k.pb	4
35	Multi-Process	Max-N	0-64%	14.7W	0.25A	25.4VA	roadsign_frozen_inference_graph_v1_2nd_4k.pb	4
33	Multi-Process	Max-P ARM	49-55%	11.6W	0.20A	20.1VA	roadsign_frozen_inference_graph_v1_2nd_4k.pb	4

TX1 Multi-Thread is 25-26 FPS.

Training ssd_mobilenet with own data

https://github.com/naisy/train_ssd_mobilenet