Detectnet KITTI training doesn't seem to converge

[rtrahms]

Hi all -
I have NVcaffe 0.15.13, CUDA 8.0 and CUDNN 5.0. Running Digits 4.1, and have been following the Detectnet tutorial on github. I have a GTX 1080, and the training runs, but doesn't seem to converge. mAP is always zero. Here is a screenshot of training (note this only goes through epoch 5, but I have run through epoch 30 in previous jobs - same results. The settings are identical to the tutorial as far as I can tell. What am I missing?
Thanks,
Rob

[rtrahms]

I am using advice from #1108 and adjusting probabilities in the data_augmentation_layer to zero and retraining.

[rtrahms]

Setting all probabilities to zero in the data augmentation layer kicked in some learning, but mAP didn't above 20%, even after 30 epochs. Still doesn't resemble the training from the tutorial. Interestingly, testing with one of the KITTI images does pull out cars, but nothing else (no pedestrians, trucks, etc). Are there other settings I should be looking at?

[rtrahms]

I discovered in the docs that DetectNet is actually a single class object detection net:
https://github.com/NVIDIA/DIGITS/blob/digits-4.0/digits/extensions/data/objectDetection/README.md#custom-class-mappings
So I think it is working as designed, albeit not the way I was expecting. Oh well. I look forward to a multiclass version of DetectNet sometime soon!

Thanks,
Rob