Hi, I followed the example on GitHub - NVIDIA/object-detection-tensorrt-example: Running object detection on a webcam feed using TensorRT on NVIDIA GPUs in Python., using a .engine finetuned from the
[Nvidia's Catalog lpdNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet). When inference is done there are two arrays as output one of length 4800 and the other 1200 which make no sense on the desired output, namely the bounding box coordinates. Using the mapping dictionary (Model Layout) the variables are still nonsense.
I’ve tried to use every SDK from Nvidia → TAO, DeepStream, TensorRT and the documentation is impossible! One spends more time solving issues on relatively easy tasks than developing applications, all for trying to use your hardware!
If I train a model on TAO, why is it so difficult to infer from it on python!
TensorRT Version: 7.1.3
CUDA Version: 10.2
CUDNN Version: 8.0
Operating System + Version: Jtpack 4.5.1
Python Version (if applicable): 3.8