ResidualDenseNetwork Pytorch Save

Pytorch implement: Residual Dense Network for Image Super-Resolution

Project README

ResidualDenseNetwork-Pytorch

Pytorch implement: Residual Dense Network for Image Super-Resolution

Two advantage ideas of the paper:

  • join denese connect layer to ResNet
    RDB
  • concatenation of hierarchical features
    RDN

Different with the paper, I just use there RDBs(Residual dense block), every RDB has three dense layers. So ,this is a sample implement the RDN(Residual Dense Network) proposed by the author.

Requirements

  • python3.5 / 3.6
  • pytorch >= 0.2
  • opencv

Usage

you need prepare DIV2K dataset (./data/)
train model :

python3 main.py --model_name 'RDN' --load demo_x3_RDN --dataDir ./DIV2K/ --need_patch True  --patchSize 144 --nDenselayer 3 --nFeat 64 --growthRate 32  --scale 3 --epoch 10000 --lrDecay 2000 --lr 1e-4 --batchSize 16 --nThreads 4 --lossType 'L1' 
    
    

References

densenet-pytorch

EDSR-PyTorch

Open Source Agenda is not affiliated with "ResidualDenseNetwork Pytorch" Project. README Source: lizhengwei1992/ResidualDenseNetwork-Pytorch
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