Can't train with GPU on Windows10

[FeederDiver]

I've used YOLO detection with trained model using my GPU - Nvidia 1060 3Gb, and everything worked fine.

Now I am trying to generate my own model, with param --gpu 1.0. Tensorflow can see my gpu, as I can read at start those communicates:
"name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705"
"totalMemory: 3.00GiB freeMemory: 2.43GiB"

Anyway, later on, when program loads data, and is trying to start learning i got following error:
"failed to allocate 832.51M (872952320 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY"

I've checked if it tries to use my other gpu (Intel 630) , but it doesn't.

As i run the train process without "--gpu" mode option, it works fine, but slowly.
( I've tried also --gpu 0.8, 0.4 etc.)

Any idea how to fix it?

[FeederDiver]

Problem solved. Changing values of batch size and image size / subdivisions and other in cfg file didn't work, as they somehow loaded incorrectly. I went to defaults.py file and changed them up there, so my GPU is now capable of processing it.