Comments (9)
Practically it does perform better than official. -_- .
I've only observed better performance in one case so I'm not sure it generalizes. In that case, the improved performance does indicate that extreme low survival rates (<0.3) might be a good regularization approach.
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I check the drop rate per block and it looks fine:
block1a_ 1.0
block1b_ 0.9875
block1c_ 0.975
block2a_ 0.9625
block2b_ 0.95
block2c_ 0.9375
block2d_ 0.925
block2e_ 0.9125
block3a_ 0.9
block3b_ 0.8875
block3c_ 0.875
block3d_ 0.8625
block3e_ 0.85
block4a_ 0.8375
block4b_ 0.825
block4c_ 0.8125
block4d_ 0.8
block4e_ 0.7875
block4f_ 0.775
block4g_ 0.7625
block5a_ 0.75
block5b_ 0.7375
block5c_ 0.725
block5d_ 0.7124999999999999
block5e_ 0.7
block5f_ 0.6875
block5g_ 0.675
block6a_ 0.6625
block6b_ 0.6499999999999999
block6c_ 0.6375
block6d_ 0.625
block6e_ 0.6125
block6f_ 0.6
block6g_ 0.5874999999999999
block6h_ 0.575
block6i_ 0.5625
block7a_ 0.55
block7b_ 0.5375
block7c_ 0.5249999999999999
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@darcula1993 I'm confused. Shouldn't the block rate be at ~0.8 for the final block since the drop_connect_rate is 0.2 by default?
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So it turns out I pasted different values. However the problem remains as indicated.
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for j in range(round_repeats(args.pop('repeats'))):
# The first block needs to take care of stride and filter size increase.
if j > 0:
args['strides'] = 1
args['filters_in'] = args['filters_out']
x = block(x, activation_fn, drop_connect_rate * b / blocks,
name='block{}{}_'.format(i + 1, chr(j + 97)), **args)
b += 1
I check the code and seems that b can be greater than num of blocks,not sure why.
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I've observed the same thing as well.
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Qubvel's implementation does not calculate the total number of blocks correctly for configurations larger than B0.
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Practically it does perform better than official. -_- .
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Well, I'm not sure, maybe I need to look again properly. In fact, I spent almost a week assuming that there probably some problem with my data loader using the official efficient-net. But when I use non-official implementation, it was just fine.
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Related Issues (20)
- [Requested Queries]: Adding features in keras-application for Object Detection API!
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