Question on channel before entering the first block

[Sirius083]

Hello, I want to reproduce the results on densenet-cifar10/cifar100, but got lower accuracy on tensorflow implementation. There is one question on model architecture, In the paper Implementation Details part: "Before entering the first dense block, a convolutional with 16(or twice the growth eate for DenseNet-BC) output channels is performed on the input images." However the code seemingly all use twice the growth rate on first block. https://github.com/liuzhuang13/DenseNet/blob/master/models/densenet.lua#L15 https://github.com/liuzhuang13/DenseNet/blob/master/models/densenet.lua#L70 Since I did not get the same accuracy level with pytorch implementaion (25.53% on cifar100 d_40_k_12_no_bottleneck, 24.42% in paper. following the same data augmentation as your official code), I am wondering whether this caused the difference? (since I am a beginner on pytorch, maybe it is in other part of the code, can you point it out) Thanks in advance

[atztao]

same issues

[YanFuHai06]

same issues