Please provide complete information as applicable to your setup.
hardware tested on jetson nano and agx xvaier;
deepstream5.1
secondary classifier:car color classifier
I tried to enable car color classifier linked behind the primary object detection and tracker , and tested its classifying performance in Jetnano and xvaier respectively, unfortunately，it didn’ t performed so well as i expected, it only can classify correct color approximately mere 10% of overall detected vehicles, as i counted in the app.
all correctly classifying nearly distribute the center of images,
![800px-Xavier_Deepstream_primary_secondarycolor_rtsp2|645x499]
(upload://c2x2UPu0kiBeKWTwuipKVVfFHqc.jpeg)
By the way, i retrained the primary model, it can correctly detect up tp 93% of vehicles running on the road, and another thing is the camera was mounted 6.5m height, namely, all vehicles are top views. i don’t know could this top view contribute unsafisfied classifying results. (ps, i only can refer to the vehicleMakenet and vehicletypenet’s instructions, they both mentioned Limitations as follows:
Top view
This model is design to classify detected cars from DashCamNet model with cars visible at a 5ft height, with visible front, back or side facia. The model doesn’t classify well for cars seen from top view.)
so my question:
1\ are there any limitations of car color models if deployed it in the deepstream5.1 ?
2\ how can i improve the performance of the car color model via slightly tuning the config.txt?
thanks in advance
