Add support for STL10 at resolutions 32, 48, and 96 #25
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chencjGene
reviewed
Jan 4, 2020
| class WideResNet96(nn.Module): | ||
| def __init__(self, num_classes, depth=28, widen_factor=2, dropRate=0.0): | ||
| super(WideResNet96, self).__init__() | ||
| nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor, 64*widen_factor] |
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should this line be "nChannels = [16, 16widen_factor, 32widen_factor, 64widen_factor, 128widen_factor]"?
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This change doubles the parameter count from 2.67M to 5.93M. I started training the larger model now since I don't remember the performance difference from a few months ago. I'll post the results soon.
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@chencjGene It doesn't provide any gains. Last layer 64 = max top1 91.8 vs last layer 128 = max top1 91.76. The larger network trains slightly slower and starts to overfit sooner. wider results, narrower results
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Adds WideResnet at sizes 48, 96.
Adds dataloading for entire STL10 dataset.
Moves transforms to
dataset/code.Adds results and instructions to
README.md.