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Recursively Branched Deconvolutional Network: DCNN architecture for "Generalized Deep Image to Image Regression." CVPR2017 (Spotlight).
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README.md

RBDN (Recursively Branched Deconvolutional Network)

placeholder RBDN is an architecture for Generalized Deep Image to Image Regression which features

  • a memory-efficient recursive branched scheme with extensive parameter sharing that computes an early learnable multi-context representation of the input,
  • end-to-end preservation of local correspondences from input to output and
  • ability to choose context-vs-locality based on task as well as apply a per-pixel multi-context non-linearity.

Architecture

pipeline RBDN gives state-of-the-art performance on 3 diverse image-to-image regression tasks: Denoising, Relighting, Colorization.

Installation & Usage

  • Clone: Run git clone -b master --single-branch https://github.com/venkai/RBDN.git

  • Setup: Go to repository cd RBDN and run ./setup.sh. This will fetch caffe, download pretrained caffe models for all 3 experiments (denoising/relighting/colorization) and inference data, as well as set up the directory structure and symbolic links for all the training/inference scripts.

  • Install Caffe: Note that setup.sh pulls 2 different branches of caffe into 2 separate directories: namely caffe_colorization used for colorization and caffe_rbdn which is used for both denoising/relighting experiments. Both these branches will eventually be merged with the master branch in venkai/caffe. However for now, you would have to separately install both these caffe versions if you want to perform all 3 experiments.

  • Data:

    • Inference data is automatically downloaded by setup.sh.

    • Training data/imglist for relighting experiment can be downloaded from either of these mirrors: [1]/[2]
      This downloads the file multipie.tar.gz. Move it to ./data/training and run tar xvzf multipie.tar.gz && rm multipie.tar.gz

    • Denoising/colorization experiments use the same training data/imglist: which is every single unresized train & validation image from both ImageNet ILSVRC2012 and MS-COCO2014 whose smallest spatial dimension is greater than 128 (~1.7 million images in total). You can simply download these datasets from their respective sources and place/symlink them within ./data/training/ without any preprocessing whatsoever. Place the appropriate imglist in ./data/training/imgset/train.txt with the image-paths in train.txt being relative to ./data/training

    • Note that data folders are not tracked by git.

  • Inference: Each experiment (denoising/relighting/colorization) has its own folder in ./inference that contains an experiment specific MATLAB inference script get_pred.m which uses the Matcaffe interface to evaluate pretrained models in ./models. The script ./inference/run_matcaffe.sh can be used to load caffe dependencies to LD_LIBRARY_PATH and then start MATLAB interactively.

  • Training: Each experiment (denoising/relighting/colorization) has its own folder in ./training that contain 2 key experiment specific scripts:

    • start_train.sh: This starts training an RBDN model, either from scratch or from the most recent snapshot in the snapshot directory. You can pause training at any moment with Ctrl+C and most recent snapshot will be saved in ./snapshot/trn_iter_[*].solverstate. Running ./start_train.sh again will automatically resume from that snapshot.
    • run_bn.sh: This takes the most recent snapshot in ./snapshot and prepares it for inference by passing training data through the network and computing global mean/variance for all the batch-normalization layers in the network. The resulting inference-ready model is saved as ./tst_[ITER].caffemodel, where ITER is the iteration corresponding to the most recent snapshot.

License & Citation

RBDN is released under a variant of the BSD 2-Clause license.

If you find RBDN useful in your research, please consider citing our paper:

@article{santhanam2016generalized,
  title={Generalized Deep Image to Image Regression},
  author={Santhanam, Venkataraman and Morariu, Vlad I and Davis, Larry S},
  journal={arXiv preprint arXiv:1612.03268},
  year={2016}
}

Acknowledgments

  • We would like to thank Yangqing Jia, Evan Shelhamer and the BVLC/BAIR team for creating & maintaining caffe, Richard Zhang for colorization layers in caffe and Hyeonwoo Noh, Seunghoon Hong, Dmytro Mishkin for several useful caffe layers, all of which were instrumental in creating RBDN.

  • This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2014-14071600012. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

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