Comments (2)
Hi,
As the .train()
or .eval()
mode only affect the BN layers in our network, we freeze these layers during both training and testing.
All the parameters of BN layers are initialized with the imagenet pre-trained model.
For now our model can only run with batch_size=1
, there is no reason to learn the params of these layers with so small a batch size.
from poolnet.
Hi,
As the.train()
or.eval()
mode only affect the BN layers in our network, we freeze these layers during both training and testing.
All the parameters of BN layers are initialized with the imagenet pre-trained model.
For now our model can only run withbatch_size=1
, there is no reason to learn the params of these layers with so small a batch size.
Hi,
Thanks for your timely reply, I get the point now
from poolnet.
Related Issues (20)
- How did you measure the accuracy, precision, recall for each dataset?
- How to create the train.lst HOT 1
- Can the model or the dataset be used commerically ? HOT 1
- Resize or not? which will be better?
- Releasing the pretrained models HOT 3
- 关于BN和model.eval()的问题 HOT 2
- detach during salient mode HOT 3
- about composite images HOT 1
- Issue in reusing the pretrained models HOT 2
- Compression network HOT 2
- About self.net.train() HOT 1
- how do you get the edge-map in DUTS-TR?
- Keras version HOT 1
- Questions about joint training HOT 2
- Question about Pre-trained model
- 关于VGG16作为backbone训练的问题
- About pretrained model using MobileNetV2 HOT 2
- Got an error when I test the pretrained model. HOT 1
- IEEE TPAMI, 2021.PoolNet+ 代码能够提供下吗?谢谢 HOT 5
- cv2.error: OpenCV(4.7.0) :-1: error: (-5:Bad argument) in function 'resize'
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from poolnet.