Comments (3)
Hi @John1231983 We follow ResNet's design on both datasets. https://github.com/facebook/fb.resnet.torch
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Thanks. I knew it. In your opinion, do you have any explanation?
from densenet.
In ImageNet we need to downsample the image twice (so that the image becomes of 56x56 size) before entering the first dense block, this is through one stride convolution and one max pooling.
In CIFAR dataset, the input image and the first dense block feature maps are of the same size (32x32), the first convolution is just a conventional design so that we don't include raw input image in the first dense block.
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Related Issues (20)
- 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>
- Different DensceNet
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