Not sure if I can explain my question clearly but I will give a try -
I tried to train trafficcamnet based on my own dataset which is the traffic in a specific env, e.g tunnel with higher cam position. The expectation is that this will give the model more ability to detect object under such env, in the mean time, it still detects traffic under norml road enviroment as shown in the sample video files.
The training indeeded improved the detection under such env but it completedly corrupted the detection under normal road env.
I thought tlt works in an incremental way, seems it does not. If it does not, does it mean every time when I do the training I should give a complete dataset with various env?
Please provide the following information when requesting support.
• Hardware (T4/V100/Xavier/Nano/etc)
• Network Type (Detectnet_v2/Faster_rcnn/Yolo_v4/LPRnet/Mask_rcnn/Classification/etc)
• 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.)