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Hi, thank you for your work. One thing I want to point out here is that you have some tiny difference compared with original implementaion, which may lead to more latency.
Your implementation here is conv + activation + bn as shown below:
y = Conv2D(32, 3, strides=2, padding='same', activation='relu', name='conv1_1_3x3_s2')(y)
y = BatchNormalization(name='conv1_1_3x3_s2_bn')(y)
But the original caffe implementaion is conv + bn + activation, which normally won't have much differnce, but this kind of order make it possible to merge bn into conv, thus leads to smaller latency.
The text was updated successfully, but these errors were encountered:
Hi, thank you for your work. One thing I want to point out here is that you have some tiny difference compared with original implementaion, which may lead to more latency.
Your implementation here is conv + activation + bn as shown below:
But the original caffe implementaion is conv + bn + activation, which normally won't have much differnce, but this kind of order make it possible to merge bn into conv, thus leads to smaller latency.
The text was updated successfully, but these errors were encountered: