The most usual case of bias=False is in layers before/after Batch Normalization with no activators in between. The BatchNorm layer will re-center the data anyway, removing the bias and making it a useless trainable parameter. Quoting the original BatchNorm paper:
Note that, since we normalize $Wu+b$, the bias $b$ can be ignored since its effect will be ...
I fixed the problem by implementing a custom dataloader.
For more information about custom dataloader: https://debuggercafe.com/custom-dataset-and-dataloader-in-pytorch/ (Links to an external site.)
Have you tried using a different learning rate?
Yours seems to be quite high, you could try 1e-4 or something in that region.
Coming from the computer vision side, you could also try to improve image readability with prior grayscaling or blurring.
Here are some repos on GTSRB: