Please provide the following information when requesting support.
• Hardware (T4/V100/Xavier/Nano/etc)
x86, Ubuntu, RTX3090.
• Network Type (Detectnet_v2/Faster_rcnn/Yolo_v4/LPRnet/Mask_rcnn/Classification/etc)
Detectnet_v2
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here)
• Training spec file(If have, please share here)
• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)
I’m using a very small custom dataset for retraining based on un-pruned detectnet-v2
.
my target detection classes are:
- eletric bicycle
- people
each class has less than 400 samples in training dataset.
as I’m testing with a video source with the export model by my deepstream app
(detect and then upload above 2 classes to a remote server), I noticed the detection accuracy for eletric bicycle
and traditional bicycle
is pretty low, the model almost recognize all traditional bicycle
to eletric bicycle
.
I want to ask, what is the suggestion to help the model to better distinguish these 2 similar classes of traditional bicycle
and eletric bicycle
, as I can guess:
- adding more training samples for
eletric bicycle
.
then the model can better recognize the expecting object. - adding one extra classe of
traditional bicycle
, as well as training samples for it.
when run the the inference, I can ignore this class by code.