Please provide the following information when requesting support.
• Hardware (T4/V100/Xavier/Nano/etc) Nano
• Network Type (Detectnet_v2/Faster_rcnn/Yolo_v4/LPRnet/Mask_rcnn/Classification/etc) Unet
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here) v3.0-py3
• Training spec file(If have, please share here) experiment_spec.txt (16.9 KB)
• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)
Hello, I am experiencing trouble reproducing results that I see when running tlt-infer on a trained unet model and the same model but exported and converted to a trt engine. The segmentation result with tlt-infer is much more accurate, whereas trt engine produces more errors and masks seem to be off the objects. I have checked many times that I do the preprocessing as required, run on same image etc. The issue persists with both a fp16 and fp32 engine. I attach the result from trt-infer and a mask for lane marking class produced by trt engine, to illustrate the kind of difference. Please let me know if you need extra input from me like models or specs