CUDA out of memory, any tips?

[ghost]

I've gotten image recognition to work at multiple frames/second, using a GTX 1060 with 6GB of memory. Now I'm trying to train a custom classifier but I keep running out of memory.
Running on the darknet implementation, I can train using the yolov3-tiny.cfg file but not the yolov3.cfg file, which I guess is probably expected behavior given my hardware limitations. Now I'm trying to train with this implementation.

What parameters could I tweak in training/params.py to reduce my memory consumption?
Is there an equivalent param in this implementation for subdivisions in the darknet implementation?

[guagen]

I think you should turn down batch_size.
You can set batch_size equal 1 at first and turn up it slowly.From experience，batch_size equals 6 maybe the best for GTX 1060 when img_h and img_h equals 416.

[AndrewZhuZJU]

In my experience, batch size should be set to be 16 if your GPU memory is 12G (GTX1080Ti).

[leonardozcm]

remove the parallels config in params.py and related code in main.py