Zhuang Liu

Thanks for your interest! Sorry we have no pre-trained models on ImageNet in caffe currently. If you really want to use the models, maybe you can try to convert the...

@sunformoon @liu666666 @kaishijeng Sorry for the late response. Our pretrained DenseNet-121 and prototxt in Caffe has just been released here https://github.com/liuzhuang13/DenseNet#imagenet-and-pretrained-models Thanks for your interest!

@saiguruju Thanks for your interest. Sorry we didn't test this caffe code for cifar100, but in our original experiment on paper, we use exactly the same setting as cifar10 when...

@saiguruju The solver should be the same, but at least you should modify the train.prototxt to 100 classes instead of 10. If you only get an accuracy of 0.05 after...

@saiguruju We used softmax loss throughout the work, maybe the problem is your L2 loss. This is just my guess

Please see this note #0. https://github.com/liuzhuang13/DenseNetCaffe#note If your input is 160x160, I suggest downsampling the images twice (by pooling or strided convolution) so that it becomes 40x40 before entering the...

Thanks for your interests! We'll work on it as soon as possible. Hopefully the model will come out this year.

Sorry for the late response. Our pretrained DenseNet-121 and prototxt in Caffe has just been released here https://github.com/liuzhuang13/DenseNet#imagenet-and-pretrained-models Thanks for your interest!

@shicai Thanks for using our models! Yes, we are aware of this issue. Your 72.5%/91.3% accuracy (256x256) is the same as what we get. The difference may come from a...

@shicai Thanks! Could you share your converted caffe models if convenient?