Comments (3)
The learning rate is ok for me.
It is quite easy to reproduce the results from the paper.
from densenet-caffe.
Thanks. I think the learning rate depends on dataset and solver optimization method.
In all prototxts, the dropout did not use. Did you try to add the dropout as 0.2 in BC model and get the result? How much it improve?
from densenet-caffe.
I have trained DenseNet on CIFAR10 and ImageNet, and lr=0.1 works for me.
I did not try to use dropout for DenseNet.
You'd better ask the author. @liuzhuang13
from densenet-caffe.
Related Issues (20)
- Is there anyone successed? My caffe version is not support! HOT 1
- how to use your deploy.prototoxt to 2 classification HOT 3
- Do you have Network definition in Python? HOT 1
- Where can I get the train_val.prototxt ? HOT 2
- about some details HOT 2
- The shapes in netscope are wrong? HOT 1
- About some computing problem HOT 1
- Scope name problem when conventing from LiuZhuang13's code HOT 3
- DenseNet和Faster R-CNN的结合 HOT 3
- { lr_mult: 1 decay_mult: 1}的含义 HOT 16
- Not achieving your reported performance! HOT 3
- when i train my own dataset,the loss is not decrease and it is always about 6.97 HOT 1
- cuDNN does not allow an eps value less than 1e-5
- Hello, I was using caffe once, using a custom version of Microsoft, I do not know how to solve this problem, I hope you can help me.
- Question about scale=0.017 HOT 1
- problems about the data preprocessing HOT 2
- Fine-tune Dense 201 problem
- 关于densenet训练阶段BN层的设置 HOT 1
- 能不能提供提供训练代码以及测试代码
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