• Hardware (RTX 3080)
• Network Type (Detectnet_v2)
• TLT Version v3.22.05-tf1.15.5-py3
I have trained a Resnet50_detectnet_v2 network that performs pretty well, based on the final epoch of my most successful experiment. However there is a more useful result earlier in the same training program. I am pretty sure that there is a way to chose the epoch upon which the final model is based, but I cannot find any reference to this in the current documentation.
Is this possible?
Do I need anything other than the unpruned model?
Please tell me where the documentation for this can be found.
Am I right to think that each of the .tlt files here should correspond to a saved epoch? I ask because there were 50 epochs for this experiment and there are 51 .tlt files, is the first one : “model.step.-0” the pre-trained weights?
Secondly, should I be able to substitute one of the model.step-*.tlt files for the “resnet18_detector.tlt” in the jupyter notebook evaluate cell?
I ask because I have not been able to make this work yet.
I have also been unable to make your code in the link work.
I know which epoch I am looking for.
here’s a screenshot of my tao_mounts.json. It shows the path to the detectnet_v2 folder where the “experiment_pruned_dir” is located.
Inside the “experiment_pruned_dir” are the 51 .tlts, amongst other things.
I intend to run the “model.step-6552.tlt”, which I suspect corresponds to epoch 14, once I have the cell finding the model.
I have accomplished this part of the notebook in an earlier experiment, where I was using the “experiment_dir_pruned/weights/resnet50_detector.tlt”.