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PyTorch Implement of Context Encoders: Feature Learning by Inpainting

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Context Encoders: Feature Learning by Inpainting

This is the Pytorch implement of CVPR 2016 paper on Context Encoders

corrupted result

1) Semantic Inpainting Demo

  1. Install PyTorch

  2. Clone the repository

    git clone
  3. Demo

    Download pre-trained model on Paris Streetview from Google Drive OR BaiduNetdisk ```Shell cp netGstreetview.pth contextencoderpytorch/model/ cd contextencoder_pytorch/model/

    Inpainting a batch iamges

    python --netG model/netG_streetview.pth --dataroot dataset/val --batchSize 100

    Inpainting one image

    python --netG model/netGstreetview.pth --testimage result/test/cropped/065im.png ```

2) Train on your own dataset

  1. Build dataset

    Put your images under dataset/train,all images should under subdirectory




    Note:For Google Policy,Paris StreetView Dataset is not public data,for research using please contact with pathak22. You can also use The Paris Dataset to train your model

  2. Train

    python --cuda --wtl2 0.999 --niter 200
  3. Test

    This step is similar to Semantic Inpainting Demo

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