I want to retrain PeopleNet using Transfer Learning Toolkit and prune it to use on Nvidia Jetson Nano.
Do I have to keep the dimensions of all training images same? I have dataset containing images of different resolutions. What would you recommend in that case?
How many images do you think are required for retraining? I am using 100 images will that be fine?
Can you recommend some annotation tool? I used LabelIMG previously but it does not give the ouptut in kitti format.
How to train false positives? In my test, I saw some dogs, cows etc were detected as “person”. There are 3 classes person, face and bag. Should I add new “other” class? What should I do?
Thanks for the help.