Comments (1)
@JohnnieXDU For example, given an 300500 image, first resize it to 448 746 (keep ratio), then random crop (in training) 448*448 patches to be the input of the classification CNN, with the "mirror" parameter being "true". By the way, I think a large "dropout ratio" is important for a promising accuracy.
from recurrent-attention-cnn.
Related Issues (20)
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from recurrent-attention-cnn.