Please provide the following information when requesting support.
• Hardware (T4/V100/Xavier/Nano/etc) : A40
• 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) nvidia/tao/tao-toolkit: 5.5.0-pyt
• 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 am doing model training using Detectnetv2 using Nvidia Tao on my custom dataset having one class with around 220 trainset 20 validationa nd 50 test set data points,
I trained the model on detectnetv2 using RN34(resnet34) backbone on Nvidia peoplenet input dimensions (960*544)and got the following result metrics
marker_1 AP 0.81818
mAP 0.818
Its same as when trained on the original detectnetV2 dimension(1248*384).
I cant understand no change in mAP because of input dimention change. I expect some change, it looks as if nothing changed !!
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