For 1) , If you want to use detectnet_v2 evaluate to do evaluation against the unpruned trafficcamnet.tlt, please resized the car images/labels to 960x544, then modify one place in your config
soft_start_annealing_schedule { min_learning_rate: 10e-10 max_learning_rate: 10e-10
And also need to set load_graph to True.
model_config {
pretrained_model_file: “/workspace/detector/models/tlt_trafficcamnet_vunpruned_v1.0/resnet18_trafficcamnet.tlt”
num_layers: 18
load_graph: True
This will directly load the tlt model and run evaluation.
More, suggest you to run detectnet_v2 inference against the car part of COCO images. In this case, you need not resize images/labels.
And also you can run inference with deepstream (mentioned in https://ngc.nvidia.com/catalog/models/nvidia:tlt_trafficcamnet)
Last, please note that trafficcamnet is not trained via COCO dataset. So, please use the unpruned trafficcamnet model to trigger training. This is the value of TLT (transfer learning toolkit).
For 2), Do you run with your own evaluation method to get “easy, moderate, hard” ? In TLT evaluate , it does not print this kind of result.