Please provide the following information when requesting support.
• Hardware (T4/V100/Xavier/Nano/etc) RTX4090
• 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) nvcr.io/nvidia/tao/tao-toolkit:4.0.0-tf1.15.5
• Training spec file(If have, please share here) exactly same with detectnet_v2 notebook, using bdd100k car dataset
• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)
image below is the inference result of detectnet_v2, if the car object is very close to the camera, it became large, the the bbox only cover part of the object. So In deepstream app, there may be 2 or 3 bbox with one car in one frame , and the nv_tracker can’t work.
This is another inference result of picodet network, trained by paddlepaddle with the same dataset. It works well with all large object.
Is there any parameter to adjust for the large object with training config?