Always get 0 or nan precision during training detectnet

Please provide the following information when requesting support.

• Hardware (T4/V100/Xavier/Nano/etc)
• Network Type (Detectnet_v2)
• TLT Version (docker_tag: v3.0-py3)
• Training spec file(SPECS_train.txt)
• How to reproduce the issue ? (tlt detectnet_v2 train -e /workspace/openalpr/SPECS_train.txt -r /workspace/openalpr/exp_unpruned -k nvidia_tlt)

I follow the steps on to train my own data set, But Mean average_precision (in %): 0.0000 or nan, except that the data set is my own, I modify nothing ,I use my Iphone take the img.
lpd.tar.gz is my datasetslpd.tar.gz (4.1 MB)

tfrecord.log is tlt detectnet_v2 dataset_convert -d /workspace/openalpr/SPECS_tfrecord.txt -o /workspace/openalpr/lpd_tfrecord/lpd output log tfrecord.log (3.9 KB)

train.log is tlt detectnet_v2 train -e /workspace/openalpr/SPECS_train.txt -r /workspace/openalpr/exp_unpruned -k nvidia_tlt output logtrain.log (260.3 KB)

Hope someone help me,very thanks

It is related to the small lpd object. Most of the lpd objects are 10x10 pixels or less than it.
For detectnet_v2, it cannot detect this kind of pixels well. More info, see DetectNet_v2 — Transfer Learning Toolkit 3.0 documentation


@Morganh Thank you very much for your reply,I’m trying to detect and recognize license plates from a far distance,could you give me some advice.

Suggest enlarging the pixels of lpd objects.
Or you can try Yolo_v4 network.

Thank you very much,I will have a try.