Comments (3)
Tongcheng
commented on June 19, 2024
@jh-jeong In my "Much more efficient caffe implementation", I also reach about 4.8% for DenseNet-BC-100-12. I am curious of the cause which seems to be common between Caffe and Tensorflow.
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jh-jeong
commented on June 19, 2024
@Tongcheng Finally I could get 4.5% in Tensorflow. What I changed are as follows:
- Changing the momentum in each BN. In Tensorflow, batch normalization uses 0.999 as the default value, but torch uses 0.9.
- Applying weight decay for 'all' trainable variables, as fb.resnet.torch did, including beta/gamma variables in BN and all biases.
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anthony123
commented on June 19, 2024
@jh-jeong can you share your tf-version code?
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Related Issues (20)
- What is architecture of (L=40,k=12) DenseNet on Cifar10 HOT 6
- Covolution before entering the first dense block for imagenet dataset HOT 1
- DenseNet on Pascal VOC HOT 2
- results on cifar100 HOT 1
- I tried to reproduce Wide-DenseNet-BC results on cifar10, but got 0.5% more than your error HOT 4
- Why is composite function BN-ReLU-Conv3x3 ? HOT 1
- Pretrained weights for the 0.8M parameters config HOT 1
- Why not share the first BN and ReLU? HOT 2
- The layers within the second and third dense block don't assign the least weight to the outputs of the transition layer in my trained model
- Why we can detach any layer without affecting others in densenet?
- question about standardization HOT 6
- cifar validation loss decrease than increase after learning rate change HOT 4
- Question on channel before entering the first block HOT 2
- Question on impede information flow HOT 1
- Is there a pretrained CIFAR 100 or CIFAR 10 model? HOT 2
- Densenet on CIFAR training from scratch
- Question on the last transition layer
- Receptive field of DenseNet
- image classification
- cannot open </cifar-10-python/data_batch_1>
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