Awesome
Densely Connected Convolutional Network (DenseNet)
paper: Densely Connected Convolutional Networks
This is the MXNet implement of DenseNet with pretrained model, therefor you can fine-tune in the pretrained model for your own dataset.
Do as follows:
- Download pretrained models(pretrained in ImageNet dataset) from
Network | Top-1 error | MXNet model |
---|---|---|
DenseNet-121 (k=32) | 25.16 | Google Drive (32.3MB) |
DenseNet-169 (k=32) | 23.74 | Google Drive (57.3MB) |
DenseNet-201 (k=32) | 22.54 | Google Drive (81.0MB) |
DenseNet-161 (k=48) | 22.28 | Google Drive (115.7MB) |
These pretrained models are manually converted from https://github.com/shicai/DenseNet-Caffe ,put the pretrained model under /DenseNet/model/
file.
- I produce two ways of image data reading:
If you want to use .rec
file to train your model:
- Change some configuration in
run_train_rec.sh
, for example:--epoch
and--model
are corresponding to the pretrained model,--data-train
is your train.rec
file,--save-result
is the train result you want to save,--num-examples
is the number of your training data,--save-name
is the name of final model. - Run
sh run_train_rec.sh
If you want to use .lst
file and image to train your model:
- Change some configuration in
run_train_lst.sh
, for example:--epoch
and--model
are corresponding to the pretrained model,--data-train
is your train.lst
file,--image-train
is your train image file,--save-result
is the train result you want to save,--num-examples
is the number of your training data,--save-name
is the name of final model. - Run
sh run_train_lst.sh