Hello! I have 2 question about training data for TLT classifier.
- How should i preprocess data for people classifier, being used after people detector(peoplenet_v2)? I tried to train two different models, one on pictures with random resolutions and another on images rescaled to 224x224, but none of them works correctly. I got different results on deepstream and tlt validation/inference. I had similar problem, when using classifier as primary model and rescaling training data to 224x224 solved problem. Maybe i should resize pictures to 244, keeping its aspect ratio?
- Can i use data augmentation in classifier models simultaneously with training, like with detectors? As i understand documentation, i have to augment data before training with tlt-augment. And i haven’t seen any augmentation fields in example configs nor in docs for spec files.
OS: Ubuntu 18.04
TLT docker: tlt-streamanalytics:v2.0_py3
Model: resnet18 classifier from ngc.nvidia.com