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./caffe-dualnet: modified from Caffe (https://github.com/BVLC/caffe)
./dualnet-dataset: the prototxt defining the models for each dataset
Usage
We illustrate the training process taking DNI on CIFAR100 as an example:
Training standard deep model
./build/tools/caffe.bin train -solver data/pklcifar100/model/v4_ninnet/solver.prototxt 2>&1 | tee -a data/pklcifar100/model/v4_ninnet/pklcifar100_nin_log.txt
Iterative Training (max_iter is set to 1 in this case)
ln -s data/pklcifar100/model/v4_ninnet/snapshot/v4_pklcifar100_nin_iter_120000.caffemodel data/pklcifar100/model/v4_ninnet/pklcifar100_nin_train_iter_120000.caffemodel_coarse
./build/tools/caffe train -solver data/pklcifar100/model/v4_ninnet/res_e1/res_e1_solver.prototxt -weights data/pklcifar100/model/v4_ninnet/pklcifar100_nin_train_iter_120000.caffemodel_coarse 2>&1 | tee -a data/pklcifar100/model/v4_ninnet/res_e1/pklcifar100_nin_res_e1_log.txt
Please cite the following paper if you find this useful in your research:
@InProceedings{Hou2017DualNet,
Title = {DualNet: Learn Complementary Features for Image Recognition},
Author = {Saihui Hou, Xu Liu and Zilei Wang},
Booktitle = {IEEE International Conference on Computer Vision (ICCV)},
Year = {2017}
}
About
DualNet: Learn Complementary Features for Image Recognition
