Unsupervised Feature Learning by Image Inpainting
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Latest commit 53f7ee9 Nov 23, 2016 @pathak22 committed on GitHub Update README.md

README.md

Context Encoders: Feature Learning by Inpainting

Project Website
RECENT: Checkout new Imagenet Results !! Sample results on held-out images:

teaser

This is the training code for our CVPR 2016 paper on Context Encoders for learning deep feature representation in an unsupervised manner by image inpainting. Context Encoders are trained jointly with reconstruction and adversarial loss. This repo contains quick demo, training/testing code for center region inpainting and training/testing code for arbitray random region inpainting. This code is adapted from an initial fork of Soumith's DCGAN implementation. Scroll down to try out a quick demo or train your own inpainting models!

If you find Context Encoders useful in your research, please cite:

@inproceedings{pathakCVPR16context,
    Author = {Pathak, Deepak and Kr\"ahenb\"uhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei},
    Title = {Context Encoders: Feature Learning by Inpainting},
    Booktitle = {Computer Vision and Pattern Recognition ({CVPR})},
    Year = {2016}
}

Contents

  1. Semantic Inpainting Demo
  2. Train Context Encoders
  3. Download Features Caffemodel
  4. TensorFlow Implementation
  5. Project Website

1) Semantic Inpainting Demo

  1. Install Torch: http://torch.ch/docs/getting-started.html#_

  2. Clone the repository

    git clone https://github.com/pathak22/context-encoder.git
  3. Demo

    cd context-encoder
    bash ./models/scripts/download_inpaintCenter_models.sh
    # This will populate the `./models/` folder with trained models.
    
    net=models/inpaintCenter/paris_inpaintCenter.t7 name=paris_result imDir=images/paris overlapPred=4 manualSeed=222 batchSize=21 gpu=1 th demo.lua
    net=models/inpaintCenter/imagenet_inpaintCenter.t7 name=imagenet_result imDir=images/imagenet overlapPred=4 manualSeed=222 batchSize=21 gpu=1 th demo.lua
    net=models/inpaintCenter/paris_inpaintCenter.t7 name=ucberkeley_result imDir=images/ucberkeley overlapPred=4 manualSeed=222 batchSize=4 gpu=1 th demo.lua
    # Note: If you are running on cpu, use gpu=0
    # Note: samples given in ./images/* are held-out images

2) Train Context Encoders

If you could successfully run the above demo, run following steps to train your own context encoder model for image inpainting.

  1. [Optional] Install Display Package as follows. If you don't want to install it, then set display=0 in train.lua.

    luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec
    cd ~
    th -ldisplay.start 8000
    # if working on server machine create tunnel: ssh -f -L 8000:localhost:8000 -N server_address.com
    # on client side, open in browser: http://localhost:8000/
  2. Make the dataset folder.

    mkdir -p /path_to_wherever_you_want/mydataset/train/images/
    # put all training images inside mydataset/train/images/
    mkdir -p /path_to_wherever_you_want/mydataset/val/images/
    # put all val images inside mydataset/val/images/
    cd context-encoder/
    ln -sf /path_to_wherever_you_want/mydataset dataset
  3. Train the model

    # For training center region inpainting model, run:
    DATA_ROOT=dataset/train display_id=11 name=inpaintCenter overlapPred=4 wtl2=0.999 nBottleneck=4000 niter=500 loadSize=350 fineSize=128 gpu=1 th train.lua
    
    # For training random region inpainting model, run:
    DATA_ROOT=dataset/train display_id=11 name=inpaintRandomNoOverlap useOverlapPred=0 wtl2=0.999 nBottleneck=4000 niter=500 loadSize=350 fineSize=128 gpu=1 th train_random.lua
    # or use fineSize=64 to train to generate 64x64 sized image (results are better):
    DATA_ROOT=dataset/train display_id=11 name=inpaintRandomNoOverlap useOverlapPred=0 wtl2=0.999 nBottleneck=4000 niter=500 loadSize=350 fineSize=64 gpu=1 th train_random.lua
  4. Test the model

    # For training center region inpainting model, run:
    DATA_ROOT=dataset/val net=checkpoints/inpaintCenter_500_net_G.t7 name=test_patch overlapPred=4 manualSeed=222 batchSize=30 loadSize=350 gpu=1 th test.lua
    DATA_ROOT=dataset/val net=checkpoints/inpaintCenter_500_net_G.t7 name=test_full overlapPred=4 manualSeed=222 batchSize=30 loadSize=129 gpu=1 th test.lua
    
    # For testing random region inpainting model, run (with fineSize=64 or 124, same as training):
    DATA_ROOT=dataset/val net=checkpoints/inpaintRandomNoOverlap_500_net_G.t7 name=test_patch_random useOverlapPred=0 manualSeed=222 batchSize=30 loadSize=350 gpu=1 th test_random.lua
    DATA_ROOT=dataset/val net=checkpoints/inpaintRandomNoOverlap_500_net_G.t7 name=test_full_random useOverlapPred=0 manualSeed=222 batchSize=30 loadSize=129 gpu=1 th test_random.lua

3) Download Features Caffemodel

Features for context encoder trained with reconstruction loss.

4) TensorFlow Implementation

Checkout the TensorFlow implementation of our paper by Taeksoo here. However, it does not implement full functionalities of our paper.

5) Project Website

Click here.